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Present Your Data Like a Pro

  • Joel Schwartzberg

data presentation or analysis

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

data presentation or analysis

  • JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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Your Modern Business Guide To Data Analysis Methods And Techniques

Data analysis methods and techniques blog post by datapine

Table of Contents

1) What Is Data Analysis?

2) Why Is Data Analysis Important?

3) What Is The Data Analysis Process?

4) Types Of Data Analysis Methods

5) Top Data Analysis Techniques To Apply

6) Quality Criteria For Data Analysis

7) Data Analysis Limitations & Barriers

8) Data Analysis Skills

9) Data Analysis In The Big Data Environment

In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.

Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery , improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.

With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.

In science, data analysis uses a more complex approach with advanced techniques to explore and experiment with data. On the other hand, in a business context, data is used to make data-driven decisions that will enable the company to improve its overall performance. In this post, we will cover the analysis of data from an organizational point of view while still going through the scientific and statistical foundations that are fundamental to understanding the basics of data analysis. 

To put all of that into perspective, we will answer a host of important analytical questions, explore analytical methods and techniques, while demonstrating how to perform analysis in the real world with a 17-step blueprint for success.

What Is Data Analysis?

Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.

All these various methods are largely based on two core areas: quantitative and qualitative research.

To explain the key differences between qualitative and quantitative research, here’s a video for your viewing pleasure:

Gaining a better understanding of different techniques and methods in quantitative research as well as qualitative insights will give your analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis.

Apart from qualitative and quantitative categories, there are also other types of data that you should be aware of before dividing into complex data analysis processes. These categories include: 

  • Big data: Refers to massive data sets that need to be analyzed using advanced software to reveal patterns and trends. It is considered to be one of the best analytical assets as it provides larger volumes of data at a faster rate. 
  • Metadata: Putting it simply, metadata is data that provides insights about other data. It summarizes key information about specific data that makes it easier to find and reuse for later purposes. 
  • Real time data: As its name suggests, real time data is presented as soon as it is acquired. From an organizational perspective, this is the most valuable data as it can help you make important decisions based on the latest developments. Our guide on real time analytics will tell you more about the topic. 
  • Machine data: This is more complex data that is generated solely by a machine such as phones, computers, or even websites and embedded systems, without previous human interaction.

Why Is Data Analysis Important?

Before we go into detail about the categories of analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.

  • Informed decision-making : From a management perspective, you can benefit from analyzing your data as it helps you make decisions based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your income, or tackle uncommon situations before they become problems. Through this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software , present the data in a professional and interactive way to different stakeholders.
  • Reduce costs : Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply. 
  • Target customers better : Customers are arguably the most crucial element in any business. By using analytics to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more. In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.

What Is The Data Analysis Process?

Data analysis process graphic

When we talk about analyzing data there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. We will cover each of them more in detail later in the post, but to start providing the needed context to understand what is coming next, here is a rundown of the 5 essential steps of data analysis. 

  • Identify: Before you get your hands dirty with data, you first need to identify why you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step. 
  • Collect: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of data you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others.  An important note here is that the way you collect the data will be different in a quantitative and qualitative scenario. 
  • Clean: Once you have the necessary data it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of data in different formats it is very likely that you will find yourself with duplicate or badly formatted data. To avoid this, before you start working with your data you need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data. 
  • Analyze : With the help of various techniques such as statistical analysis, regressions, neural networks, text analysis, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Various technologies in the market assist researchers and average users with the management of their data. Some of them include business intelligence and visualization software, predictive analytics, and data mining, among others. 
  • Interpret: Last but not least you have one of the most important steps: it is time to interpret your results. This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them. 

Now that you have a basic understanding of the key data analysis steps, let’s look at the top 17 essential methods.

17 Essential Types Of Data Analysis Methods

Before diving into the 17 essential types of methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.

a) Descriptive analysis - What happened.

The descriptive analysis method is the starting point for any analytic reflection, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights for your organization.

Performing descriptive analysis is essential, as it enables us to present our insights in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, it will leave your data organized and ready to conduct further investigations.

b) Exploratory analysis - How to explore data relationships.

As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there is still no notion of the relationship between the data and the variables. Once the data is investigated, exploratory analysis helps you to find connections and generate hypotheses and solutions for specific problems. A typical area of ​​application for it is data mining.

c) Diagnostic analysis - Why it happened.

Diagnostic data analytics empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.

Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics , e.g.

c) Predictive analysis - What will happen.

The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Through this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.

With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge over the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.

e) Prescriptive analysis - How will it happen.

Another of the most effective types of analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.

By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics , and others.

Top 17 data analysis methods

As mentioned at the beginning of the post, data analysis methods can be divided into two big categories: quantitative and qualitative. Each of these categories holds a powerful analytical value that changes depending on the scenario and type of data you are working with. Below, we will discuss 17 methods that are divided into qualitative and quantitative approaches. 

Without further ado, here are the 17 essential types of data analysis methods with some use cases in the business world: 

A. Quantitative Methods 

To put it simply, quantitative analysis refers to all methods that use numerical data or data that can be turned into numbers (e.g. category variables like gender, age, etc.) to extract valuable insights. It is used to extract valuable conclusions about relationships, differences, and test hypotheses. Below we discuss some of the key quantitative methods. 

1. Cluster analysis

The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.

Let's look at it from an organizational perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.

2. Cohort analysis

This type of data analysis approach uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

Cohort analysis can be really useful for performing analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up for your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.  

A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide . In the bottom image, you see an example of how you visualize a cohort in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.

Cohort analysis chart example from google analytics

3. Regression analysis

Regression uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future.

Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Through this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.

If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.

4. Neural networks

The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of analytics that attempts, with minimal intervention, to understand how the human brain would generate insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.

A typical area of application for neural networks is predictive analytics. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced scientist. 

Here is an example of how you can use the predictive analysis tool from datapine:

Example on how to use predictive analytics tool from datapine

**click to enlarge**

5. Factor analysis

The factor analysis also called “dimension reduction” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal method for streamlining specific segments.

A good way to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, the place where they bought the product, and frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes into the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.

If you want to start analyzing data using factor analysis we recommend you take a look at this practical guide from UCLA.

6. Data mining

A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge.  When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.

An excellent use case of data mining is datapine intelligent data alerts . With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs , you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.

In the following picture, you can see how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions, and revenues, the alarms will notify you if the goal was not completed or if it exceeded expectations.

Example on how to use intelligent alerts from datapine

7. Time series analysis

As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result. 

In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a period of time and forecast different future events. 

A great use case to put time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise over a specific period of time (e.g. swimwear during summertime, or candy during Halloween). These insights allow you to predict demand and prepare production accordingly.  

8. Decision Trees 

The decision tree analysis aims to act as a support tool to make smart and strategic decisions. By visually displaying potential outcomes, consequences, and costs in a tree-like model, researchers and company users can easily evaluate all factors involved and choose the best course of action. Decision trees are helpful to analyze quantitative data and they allow for an improved decision-making process by helping you spot improvement opportunities, reduce costs, and enhance operational efficiency and production.

But how does a decision tree actually works? This method works like a flowchart that starts with the main decision that you need to make and branches out based on the different outcomes and consequences of each decision. Each outcome will outline its own consequences, costs, and gains and, at the end of the analysis, you can compare each of them and make the smartest decision. 

Businesses can use them to understand which project is more cost-effective and will bring more earnings in the long run. For example, imagine you need to decide if you want to update your software app or build a new app entirely.  Here you would compare the total costs, the time needed to be invested, potential revenue, and any other factor that might affect your decision.  In the end, you would be able to see which of these two options is more realistic and attainable for your company or research.

9. Conjoint analysis 

Last but not least, we have the conjoint analysis. This approach is usually used in surveys to understand how individuals value different attributes of a product or service and it is one of the most effective methods to extract consumer preferences. When it comes to purchasing, some clients might be more price-focused, others more features-focused, and others might have a sustainable focus. Whatever your customer's preferences are, you can find them with conjoint analysis. Through this, companies can define pricing strategies, packaging options, subscription packages, and more. 

A great example of conjoint analysis is in marketing and sales. For instance, a cupcake brand might use conjoint analysis and find that its clients prefer gluten-free options and cupcakes with healthier toppings over super sugary ones. Thus, the cupcake brand can turn these insights into advertisements and promotions to increase sales of this particular type of product. And not just that, conjoint analysis can also help businesses segment their customers based on their interests. This allows them to send different messaging that will bring value to each of the segments. 

10. Correspondence Analysis

Also known as reciprocal averaging, correspondence analysis is a method used to analyze the relationship between categorical variables presented within a contingency table. A contingency table is a table that displays two (simple correspondence analysis) or more (multiple correspondence analysis) categorical variables across rows and columns that show the distribution of the data, which is usually answers to a survey or questionnaire on a specific topic. 

This method starts by calculating an “expected value” which is done by multiplying row and column averages and dividing it by the overall original value of the specific table cell. The “expected value” is then subtracted from the original value resulting in a “residual number” which is what allows you to extract conclusions about relationships and distribution. The results of this analysis are later displayed using a map that represents the relationship between the different values. The closest two values are in the map, the bigger the relationship. Let’s put it into perspective with an example. 

Imagine you are carrying out a market research analysis about outdoor clothing brands and how they are perceived by the public. For this analysis, you ask a group of people to match each brand with a certain attribute which can be durability, innovation, quality materials, etc. When calculating the residual numbers, you can see that brand A has a positive residual for innovation but a negative one for durability. This means that brand A is not positioned as a durable brand in the market, something that competitors could take advantage of. 

11. Multidimensional Scaling (MDS)

MDS is a method used to observe the similarities or disparities between objects which can be colors, brands, people, geographical coordinates, and more. The objects are plotted using an “MDS map” that positions similar objects together and disparate ones far apart. The (dis) similarities between objects are represented using one or more dimensions that can be observed using a numerical scale. For example, if you want to know how people feel about the COVID-19 vaccine, you can use 1 for “don’t believe in the vaccine at all”  and 10 for “firmly believe in the vaccine” and a scale of 2 to 9 for in between responses.  When analyzing an MDS map the only thing that matters is the distance between the objects, the orientation of the dimensions is arbitrary and has no meaning at all. 

Multidimensional scaling is a valuable technique for market research, especially when it comes to evaluating product or brand positioning. For instance, if a cupcake brand wants to know how they are positioned compared to competitors, it can define 2-3 dimensions such as taste, ingredients, shopping experience, or more, and do a multidimensional scaling analysis to find improvement opportunities as well as areas in which competitors are currently leading. 

Another business example is in procurement when deciding on different suppliers. Decision makers can generate an MDS map to see how the different prices, delivery times, technical services, and more of the different suppliers differ and pick the one that suits their needs the best. 

A final example proposed by a research paper on "An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data". Researchers picked a two-dimensional MDS map to display the distances and relationships between different sentiments in movie reviews. They used 36 sentiment words and distributed them based on their emotional distance as we can see in the image below where the words "outraged" and "sweet" are on opposite sides of the map, marking the distance between the two emotions very clearly.

Example of multidimensional scaling analysis

Aside from being a valuable technique to analyze dissimilarities, MDS also serves as a dimension-reduction technique for large dimensional data. 

B. Qualitative Methods

Qualitative data analysis methods are defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. As opposed to quantitative methods, qualitative data is more subjective and highly valuable in analyzing customer retention and product development.

12. Text analysis

Text analysis, also known in the industry as text mining, works by taking large sets of textual data and arranging them in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your organization and use it to develop actionable insights that will propel you forward.

Modern software accelerate the application of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant to your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article .

By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. There are various other “sub-methods” that are an extension of text analysis. Each of them serves a more specific purpose and we will look at them in detail next. 

13. Content Analysis

This is a straightforward and very popular method that examines the presence and frequency of certain words, concepts, and subjects in different content formats such as text, image, audio, or video. For example, the number of times the name of a celebrity is mentioned on social media or online tabloids. It does this by coding text data that is later categorized and tabulated in a way that can provide valuable insights, making it the perfect mix of quantitative and qualitative analysis.

There are two types of content analysis. The first one is the conceptual analysis which focuses on explicit data, for instance, the number of times a concept or word is mentioned in a piece of content. The second one is relational analysis, which focuses on the relationship between different concepts or words and how they are connected within a specific context. 

Content analysis is often used by marketers to measure brand reputation and customer behavior. For example, by analyzing customer reviews. It can also be used to analyze customer interviews and find directions for new product development. It is also important to note, that in order to extract the maximum potential out of this analysis method, it is necessary to have a clearly defined research question. 

14. Thematic Analysis

Very similar to content analysis, thematic analysis also helps in identifying and interpreting patterns in qualitative data with the main difference being that the first one can also be applied to quantitative analysis. The thematic method analyzes large pieces of text data such as focus group transcripts or interviews and groups them into themes or categories that come up frequently within the text. It is a great method when trying to figure out peoples view’s and opinions about a certain topic. For example, if you are a brand that cares about sustainability, you can do a survey of your customers to analyze their views and opinions about sustainability and how they apply it to their lives. You can also analyze customer service calls transcripts to find common issues and improve your service. 

Thematic analysis is a very subjective technique that relies on the researcher’s judgment. Therefore,  to avoid biases, it has 6 steps that include familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. It is also important to note that, because it is a flexible approach, the data can be interpreted in multiple ways and it can be hard to select what data is more important to emphasize. 

15. Narrative Analysis 

A bit more complex in nature than the two previous ones, narrative analysis is used to explore the meaning behind the stories that people tell and most importantly, how they tell them. By looking into the words that people use to describe a situation you can extract valuable conclusions about their perspective on a specific topic. Common sources for narrative data include autobiographies, family stories, opinion pieces, and testimonials, among others. 

From a business perspective, narrative analysis can be useful to analyze customer behaviors and feelings towards a specific product, service, feature, or others. It provides unique and deep insights that can be extremely valuable. However, it has some drawbacks.  

The biggest weakness of this method is that the sample sizes are usually very small due to the complexity and time-consuming nature of the collection of narrative data. Plus, the way a subject tells a story will be significantly influenced by his or her specific experiences, making it very hard to replicate in a subsequent study. 

16. Discourse Analysis

Discourse analysis is used to understand the meaning behind any type of written, verbal, or symbolic discourse based on its political, social, or cultural context. It mixes the analysis of languages and situations together. This means that the way the content is constructed and the meaning behind it is significantly influenced by the culture and society it takes place in. For example, if you are analyzing political speeches you need to consider different context elements such as the politician's background, the current political context of the country, the audience to which the speech is directed, and so on. 

From a business point of view, discourse analysis is a great market research tool. It allows marketers to understand how the norms and ideas of the specific market work and how their customers relate to those ideas. It can be very useful to build a brand mission or develop a unique tone of voice. 

17. Grounded Theory Analysis

Traditionally, researchers decide on a method and hypothesis and start to collect the data to prove that hypothesis. The grounded theory is the only method that doesn’t require an initial research question or hypothesis as its value lies in the generation of new theories. With the grounded theory method, you can go into the analysis process with an open mind and explore the data to generate new theories through tests and revisions. In fact, it is not necessary to collect the data and then start to analyze it. Researchers usually start to find valuable insights as they are gathering the data. 

All of these elements make grounded theory a very valuable method as theories are fully backed by data instead of initial assumptions. It is a great technique to analyze poorly researched topics or find the causes behind specific company outcomes. For example, product managers and marketers might use the grounded theory to find the causes of high levels of customer churn and look into customer surveys and reviews to develop new theories about the causes. 

How To Analyze Data? Top 17 Data Analysis Techniques To Apply

17 top data analysis techniques by datapine

Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different data analysis types, it’s time to dig deeper into how to perform your analysis by working through these 17 essential techniques.

1. Collaborate your needs

Before you begin analyzing or drilling down into any techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.

2. Establish your questions

Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important techniques as it will shape the very foundations of your success.

To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions .

3. Data democratization

After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with democratization.

Data democratization is an action that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.  

Once you have decided on your most valuable sources, you need to take all of this into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your company.

data connectors from datapine

4. Think of governance 

When collecting data in a business or research context you always need to think about security and privacy. With data breaches becoming a topic of concern for businesses, the need to protect your client's or subject’s sensitive information becomes critical. 

To ensure that all this is taken care of, you need to think of a data governance strategy. According to Gartner , this concept refers to “ the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics .” In simpler words, data governance is a collection of processes, roles, and policies, that ensure the efficient use of data while still achieving the main company goals. It ensures that clear roles are in place for who can access the information and how they can access it. In time, this not only ensures that sensitive information is protected but also allows for an efficient analysis as a whole. 

5. Clean your data

After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.

There are many things that you need to look for in the cleaning process. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of information. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.

Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors. 

Most importantly, the aim of cleaning is to prevent you from arriving at false conclusions that can damage your company in the long run. By using clean data, you will also help BI solutions to interact better with your information and create better reports for your organization.

6. Set your KPIs

Once you’ve set your sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.

KPIs are critical to both qualitative and quantitative analysis research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.

To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI : transportation-related costs. If you want to see more go explore our collection of key performance indicator examples .

Transportation costs logistics KPIs

7. Omit useless data

Having bestowed your data analysis tools and techniques with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.

Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.

Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.

8. Build a data management roadmap

While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.

Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.

9. Integrate technology

There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.

Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present them in a digestible, visual, interactive format from one central, live dashboard . A data methodology you can count on.

By integrating the right technology within your data analysis methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.

For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing, glance over our selection of dashboard examples .

10. Answer your questions

By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.

11. Visualize your data

Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the organization to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.

The purpose of analyzing is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard .

An executive dashboard example showcasing high-level marketing KPIs such as cost per lead, MQL, SQL, and cost per customer.

This visual, dynamic, and interactive online dashboard is a data analysis example designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.

In detail, this example generated with a modern dashboard creator displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them are compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports .

The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the company exponentially.

12. Be careful with the interpretation

We already dedicated an entire post to data interpretation as it is a fundamental part of the process of data analysis. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations. 

To help you through the process, here we list three common practices that you need to avoid at all costs when looking at your data:

  • Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation. 
  • Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire analytical project is finalized.
  • Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.

13. Build a narrative

Now, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.

The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools , you should strive to tell a story - one with a clear-cut beginning, middle, and end.

By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.

14. Consider autonomous technology

Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.

Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.

At the moment, these technologies are revolutionizing the analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.

15. Share the load

If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.

Modern dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.

Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.

16. Data analysis tools

In order to perform high-quality analysis of data, it is fundamental to use tools and software that will ensure the best results. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.

  • Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Through this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible to beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge analysis of data, KPIs visualization, live dashboards, reporting, and artificial intelligence technologies to predict trends and minimize risk.
  • Statistical analysis: These tools are usually designed for scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better decisions. SPSS also works as a cloud service that enables you to run it anywhere.
  • SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these are popular among data scientists as they are extremely effective in unlocking these databases' value. Undoubtedly, one of the most used SQL software in the market is MySQL Workbench . This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, administration tools, and visual performance dashboards to keep track of KPIs.
  • Data Visualization: These tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.

17. Refine your process constantly 

Last is a step that might seem obvious to some people, but it can be easily ignored if you think you are done. Once you have extracted the needed results, you should always take a retrospective look at your project and think about what you can improve. As you saw throughout this long list of techniques, data analysis is a complex process that requires constant refinement. For this reason, you should always go one step further and keep improving. 

Quality Criteria For Data Analysis

So far we’ve covered a list of methods and techniques that should help you perform efficient data analysis. But how do you measure the quality and validity of your results? This is done with the help of some science quality criteria. Here we will go into a more theoretical area that is critical to understanding the fundamentals of statistical analysis in science. However, you should also be aware of these steps in a business context, as they will allow you to assess the quality of your results in the correct way. Let’s dig in. 

  • Internal validity: The results of a survey are internally valid if they measure what they are supposed to measure and thus provide credible results. In other words , internal validity measures the trustworthiness of the results and how they can be affected by factors such as the research design, operational definitions, how the variables are measured, and more. For instance, imagine you are doing an interview to ask people if they brush their teeth two times a day. While most of them will answer yes, you can still notice that their answers correspond to what is socially acceptable, which is to brush your teeth at least twice a day. In this case, you can’t be 100% sure if respondents actually brush their teeth twice a day or if they just say that they do, therefore, the internal validity of this interview is very low. 
  • External validity: Essentially, external validity refers to the extent to which the results of your research can be applied to a broader context. It basically aims to prove that the findings of a study can be applied in the real world. If the research can be applied to other settings, individuals, and times, then the external validity is high. 
  • Reliability : If your research is reliable, it means that it can be reproduced. If your measurement were repeated under the same conditions, it would produce similar results. This means that your measuring instrument consistently produces reliable results. For example, imagine a doctor building a symptoms questionnaire to detect a specific disease in a patient. Then, various other doctors use this questionnaire but end up diagnosing the same patient with a different condition. This means the questionnaire is not reliable in detecting the initial disease. Another important note here is that in order for your research to be reliable, it also needs to be objective. If the results of a study are the same, independent of who assesses them or interprets them, the study can be considered reliable. Let’s see the objectivity criteria in more detail now. 
  • Objectivity: In data science, objectivity means that the researcher needs to stay fully objective when it comes to its analysis. The results of a study need to be affected by objective criteria and not by the beliefs, personality, or values of the researcher. Objectivity needs to be ensured when you are gathering the data, for example, when interviewing individuals, the questions need to be asked in a way that doesn't influence the results. Paired with this, objectivity also needs to be thought of when interpreting the data. If different researchers reach the same conclusions, then the study is objective. For this last point, you can set predefined criteria to interpret the results to ensure all researchers follow the same steps. 

The discussed quality criteria cover mostly potential influences in a quantitative context. Analysis in qualitative research has by default additional subjective influences that must be controlled in a different way. Therefore, there are other quality criteria for this kind of research such as credibility, transferability, dependability, and confirmability. You can see each of them more in detail on this resource . 

Data Analysis Limitations & Barriers

Analyzing data is not an easy task. As you’ve seen throughout this post, there are many steps and techniques that you need to apply in order to extract useful information from your research. While a well-performed analysis can bring various benefits to your organization it doesn't come without limitations. In this section, we will discuss some of the main barriers you might encounter when conducting an analysis. Let’s see them more in detail. 

  • Lack of clear goals: No matter how good your data or analysis might be if you don’t have clear goals or a hypothesis the process might be worthless. While we mentioned some methods that don’t require a predefined hypothesis, it is always better to enter the analytical process with some clear guidelines of what you are expecting to get out of it, especially in a business context in which data is utilized to support important strategic decisions. 
  • Objectivity: Arguably one of the biggest barriers when it comes to data analysis in research is to stay objective. When trying to prove a hypothesis, researchers might find themselves, intentionally or unintentionally, directing the results toward an outcome that they want. To avoid this, always question your assumptions and avoid confusing facts with opinions. You can also show your findings to a research partner or external person to confirm that your results are objective. 
  • Data representation: A fundamental part of the analytical procedure is the way you represent your data. You can use various graphs and charts to represent your findings, but not all of them will work for all purposes. Choosing the wrong visual can not only damage your analysis but can mislead your audience, therefore, it is important to understand when to use each type of data depending on your analytical goals. Our complete guide on the types of graphs and charts lists 20 different visuals with examples of when to use them. 
  • Flawed correlation : Misleading statistics can significantly damage your research. We’ve already pointed out a few interpretation issues previously in the post, but it is an important barrier that we can't avoid addressing here as well. Flawed correlations occur when two variables appear related to each other but they are not. Confusing correlations with causation can lead to a wrong interpretation of results which can lead to building wrong strategies and loss of resources, therefore, it is very important to identify the different interpretation mistakes and avoid them. 
  • Sample size: A very common barrier to a reliable and efficient analysis process is the sample size. In order for the results to be trustworthy, the sample size should be representative of what you are analyzing. For example, imagine you have a company of 1000 employees and you ask the question “do you like working here?” to 50 employees of which 49 say yes, which means 95%. Now, imagine you ask the same question to the 1000 employees and 950 say yes, which also means 95%. Saying that 95% of employees like working in the company when the sample size was only 50 is not a representative or trustworthy conclusion. The significance of the results is way more accurate when surveying a bigger sample size.   
  • Privacy concerns: In some cases, data collection can be subjected to privacy regulations. Businesses gather all kinds of information from their customers from purchasing behaviors to addresses and phone numbers. If this falls into the wrong hands due to a breach, it can affect the security and confidentiality of your clients. To avoid this issue, you need to collect only the data that is needed for your research and, if you are using sensitive facts, make it anonymous so customers are protected. The misuse of customer data can severely damage a business's reputation, so it is important to keep an eye on privacy. 
  • Lack of communication between teams : When it comes to performing data analysis on a business level, it is very likely that each department and team will have different goals and strategies. However, they are all working for the same common goal of helping the business run smoothly and keep growing. When teams are not connected and communicating with each other, it can directly affect the way general strategies are built. To avoid these issues, tools such as data dashboards enable teams to stay connected through data in a visually appealing way. 
  • Innumeracy : Businesses are working with data more and more every day. While there are many BI tools available to perform effective analysis, data literacy is still a constant barrier. Not all employees know how to apply analysis techniques or extract insights from them. To prevent this from happening, you can implement different training opportunities that will prepare every relevant user to deal with data. 

Key Data Analysis Skills

As you've learned throughout this lengthy guide, analyzing data is a complex task that requires a lot of knowledge and skills. That said, thanks to the rise of self-service tools the process is way more accessible and agile than it once was. Regardless, there are still some key skills that are valuable to have when working with data, we list the most important ones below.

  • Critical and statistical thinking: To successfully analyze data you need to be creative and think out of the box. Yes, that might sound like a weird statement considering that data is often tight to facts. However, a great level of critical thinking is required to uncover connections, come up with a valuable hypothesis, and extract conclusions that go a step further from the surface. This, of course, needs to be complemented by statistical thinking and an understanding of numbers. 
  • Data cleaning: Anyone who has ever worked with data before will tell you that the cleaning and preparation process accounts for 80% of a data analyst's work, therefore, the skill is fundamental. But not just that, not cleaning the data adequately can also significantly damage the analysis which can lead to poor decision-making in a business scenario. While there are multiple tools that automate the cleaning process and eliminate the possibility of human error, it is still a valuable skill to dominate. 
  • Data visualization: Visuals make the information easier to understand and analyze, not only for professional users but especially for non-technical ones. Having the necessary skills to not only choose the right chart type but know when to apply it correctly is key. This also means being able to design visually compelling charts that make the data exploration process more efficient. 
  • SQL: The Structured Query Language or SQL is a programming language used to communicate with databases. It is fundamental knowledge as it enables you to update, manipulate, and organize data from relational databases which are the most common databases used by companies. It is fairly easy to learn and one of the most valuable skills when it comes to data analysis. 
  • Communication skills: This is a skill that is especially valuable in a business environment. Being able to clearly communicate analytical outcomes to colleagues is incredibly important, especially when the information you are trying to convey is complex for non-technical people. This applies to in-person communication as well as written format, for example, when generating a dashboard or report. While this might be considered a “soft” skill compared to the other ones we mentioned, it should not be ignored as you most likely will need to share analytical findings with others no matter the context. 

Data Analysis In The Big Data Environment

Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.

To inspire your efforts and put the importance of big data into context, here are some insights that you should know:

  • By 2026 the industry of big data is expected to be worth approximately $273.4 billion.
  • 94% of enterprises say that analyzing data is important for their growth and digital transformation. 
  • Companies that exploit the full potential of their data can increase their operating margins by 60% .
  • We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.

Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.

Key Takeaways From Data Analysis 

As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.

17 Essential Types of Data Analysis Methods:

  • Cluster analysis
  • Cohort analysis
  • Regression analysis
  • Factor analysis
  • Neural Networks
  • Data Mining
  • Text analysis
  • Time series analysis
  • Decision trees
  • Conjoint analysis 
  • Correspondence Analysis
  • Multidimensional Scaling 
  • Content analysis 
  • Thematic analysis
  • Narrative analysis 
  • Grounded theory analysis
  • Discourse analysis 

Top 17 Data Analysis Techniques:

  • Collaborate your needs
  • Establish your questions
  • Data democratization
  • Think of data governance 
  • Clean your data
  • Set your KPIs
  • Omit useless data
  • Build a data management roadmap
  • Integrate technology
  • Answer your questions
  • Visualize your data
  • Interpretation of data
  • Consider autonomous technology
  • Build a narrative
  • Share the load
  • Data Analysis tools
  • Refine your process constantly 

We’ve pondered the data analysis definition and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.

Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting .

And, if you’re ready to perform your own analysis, drill down into your facts and figures while interacting with your data on astonishing visuals, you can try our software for a free, 14-day trial .

Call Us Today! +91 99907 48956 | [email protected]

data presentation or analysis

It is the simplest form of data Presentation often used in schools or universities to provide a clearer picture to students, who are better able to capture the concepts effectively through a pictorial Presentation of simple data.

2. Column chart

data presentation or analysis

It is a simplified version of the pictorial Presentation which involves the management of a larger amount of data being shared during the presentations and providing suitable clarity to the insights of the data.

3. Pie Charts

pie-chart

Pie charts provide a very descriptive & a 2D depiction of the data pertaining to comparisons or resemblance of data in two separate fields.

4. Bar charts

Bar-Charts

A bar chart that shows the accumulation of data with cuboid bars with different dimensions & lengths which are directly proportionate to the values they represent. The bars can be placed either vertically or horizontally depending on the data being represented.

5. Histograms

data presentation or analysis

It is a perfect Presentation of the spread of numerical data. The main differentiation that separates data graphs and histograms are the gaps in the data graphs.

6. Box plots

box-plot

Box plot or Box-plot is a way of representing groups of numerical data through quartiles. Data Presentation is easier with this style of graph dealing with the extraction of data to the minutes of difference.

data presentation or analysis

Map Data graphs help you with data Presentation over an area to display the areas of concern. Map graphs are useful to make an exact depiction of data over a vast case scenario.

All these visual presentations share a common goal of creating meaningful insights and a platform to understand and manage the data in relation to the growth and expansion of one’s in-depth understanding of data & details to plan or execute future decisions or actions.

Importance of Data Presentation

Data Presentation could be both can be a deal maker or deal breaker based on the delivery of the content in the context of visual depiction.

Data Presentation tools are powerful communication tools that can simplify the data by making it easily understandable & readable at the same time while attracting & keeping the interest of its readers and effectively showcase large amounts of complex data in a simplified manner.

If the user can create an insightful presentation of the data in hand with the same sets of facts and figures, then the results promise to be impressive.

There have been situations where the user has had a great amount of data and vision for expansion but the presentation drowned his/her vision.

To impress the higher management and top brass of a firm, effective presentation of data is needed.

Data Presentation helps the clients or the audience to not spend time grasping the concept and the future alternatives of the business and to convince them to invest in the company & turn it profitable both for the investors & the company.

Although data presentation has a lot to offer, the following are some of the major reason behind the essence of an effective presentation:-

  • Many consumers or higher authorities are interested in the interpretation of data, not the raw data itself. Therefore, after the analysis of the data, users should represent the data with a visual aspect for better understanding and knowledge.
  • The user should not overwhelm the audience with a number of slides of the presentation and inject an ample amount of texts as pictures that will speak for themselves.
  • Data presentation often happens in a nutshell with each department showcasing their achievements towards company growth through a graph or a histogram.
  • Providing a brief description would help the user to attain attention in a small amount of time while informing the audience about the context of the presentation
  • The inclusion of pictures, charts, graphs and tables in the presentation help for better understanding the potential outcomes.
  • An effective presentation would allow the organization to determine the difference with the fellow organization and acknowledge its flaws. Comparison of data would assist them in decision making.

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Data Analysis 101: How to Make Your Presentations Practical and Effective

  • December 27, 2022
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data presentation or analysis

Understanding Importance of Data Analysis

The results of data analysis can give business the vital insights they need to turn in to successful and profitable ventures. It could be the difference between a successful business operation and a business operation that is in trouble.

Data analysis, though one of the most in-demand job roles globally, doesn’t require a degree in statistics or mathematics to do well, and employers from a wide variety of industries are very keen to recruit data analysts.

Businesses hire data analysts in the field of finance, marketing, administration, HR, IT and procurement, to name just a few.  Understand the big picture and provide answers. By engaging in data analysis, you can actually delve deep and discover hidden truths that most business people would never be able to do.

What skills you should master to be a data analyst?

While Data Analyst roles are on the rise, there are certain skills that are vital for anyone who wants to become a data analyst . Before the job, a candidate needs to have either a degree in statistics, business or computer science or a related subject, or work experience in these areas. 

If you’re interested in becoming a data analyst, you’ll need to know: 

  • Programming and algorithms
  • Data Visualization 
  • Open-source and cloud technologies 
  • No coding experience is required. 

How much is a data analyst worth?  Data analysts earn an average salary of £32,403 per annum, according to jobs site Glassdoor. This pays for a salary, with benefits such as medical insurance and paid leave included in the starting salary.  If you think you have the right skills, there are plenty of roles on offer.

What data analysis entails

Data analysis is an analytical process which involves recording and tabulating (recording and entering, entering and tabulating) the quantities of a product, such as numbers of units produced, costs of materials and expenses.

While data analyst can take different forms, for example in databases, in other structures such as spreadsheets, numbers are the main means of data entry. This involves entering and entering the required data in a data analysis system such as Excel.

For example, although a database doesn’t require a data analyst, it can still benefit from data analysis techniques such as binomial testing, ANOVA and Fisher’s exact tests.  Where is the data analysis courses in IT?  Given the ever-increasing reliance on technology in business, data analysis courses are vital skills.

What are the types of data analysis methods?

  • Cluster analysis 

The act of grouping a specific set of data in a manner that those elements are more similar to one another than to those in other groups – hence the term ‘cluster.’ Since there is no special target variable while doing clustering, the method is often used to find hidden patterns in the data. The approach is purposely used to offer additional context to a particular trend or dataset.  

  • Cohort analysis 

This type of data analysis method uses historical data to examine and compare a determined segment of users’ behavior, which can then be grouped with others with similar characteristics. By using this data analysis methodology, it’s possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

A dependent variable is an element of a complex system that is assumed to have a single cause, but it’s affected by multiple factors, thus giving researchers an indication as to how a complex system function.  

  • Regression analysis

The regression analysis is used to predict how the value of a dependent variable changes when one or more independent variables change, stay the same or the dependent variable is not moved. Regression is a sophisticated statistical method that includes mathematical functions that are typically called “segmentation,” “distribution,” and “intercept” functions.

Regression is a type of regression analysis that only contains linear and quadratic functions. You can change the types of factors (or the independent variables) that are selected in regression analysis (it’s typically called “nonlinear regression analysis”) by changing the order in which the models are constructed.To begin, let’s explain how regression analysis works.  

Examples in business world

The Oracle Corporation is one of the first multinational companies to adopt this type of analysis method, based on which the company was able to develop predictive modelling systems for marketing purposes.

In a more specific sense, a Regression analysis is a popular type of data analysis used for analyzing the likelihood that a random variable will move up or down a range of parameters in response to a change in a specific control variable.

Companies who use this type of analysis are looking for trends and patterned performance over time. For example, how a company may respond to a rising cost of labor and its effect on its business bottom line, a weather-related issue like an earthquake, a new advertising campaign, or even a surge in customer demand in some areas.

What are basic pointers to consider while presenting data

Recognize that presentation matters.

Too often, analysts make the mistake of presenting information in order to show an abstracted version of it.  For instance, say a B2B company has 4 ways to improve their sales funnel:

  • More Visually Engaging 
  • More Easily Transacted 
  • More Cost Effective 

Then, “informative” would mean that a B2B company needs to optimize their sales funnel to each of these to be more “convenient, faster, easier, more visually engaging, or most cost effective.” Sure, it would be nice if they all improved – they would all provide a competitive advantage in some way. But that’s not what the data tells us.

Don’t scare people with numbers

When you’re presenting data, show as many as possible, in as many charts as possible. Then, try to talk through the implications of the data, rather than overwhelming people with an overwhelming amount of data.

Why? Research suggests that when a number is presented in a visual, people become more likely to process it and learn from it.  I recommend using video, text, graphs, and pictures to represent your numbers. This creates a more visually appealing data set. The number of followers on Twitter is visually appealing. The number of followers on Facebook is visually appealing. But nobody looks at their Twitter followers. If you don’t know what your numbers mean, how will your audience?  That doesn’t mean numbers aren’t important.

Maximize the data pixel ratio

The more data you show to a critical stakeholder, the more likely they are to get lost and distracted from what you’re actually trying to communicate. This is especially important in the case of people in the sales and marketing function.

Do you have a sales person out in the field who is trying to close a deal? It would be a shame if that person got lost in your Excel analytics and lost out on the sale.  This problem also occurs on the web.

Consider how web visitors respond to large, colorful charts and graphs. If we’re talking about visualizations that depict web performance, a visual might be helpful. But how often do we see this done?  Research shows that people respond better to web-based data in a simplified, less complex format.

Save 3-D for the movies

There are great stories in the universe. This is an oversimplification, but if you look at history, humans only understand stories. We are great storytellers. We develop, through trial and error, our own intuition about the “right” way to tell stories.

 One of the most powerful and effective ways to present data is to go beyond the visual to the audible, that is, to tell stories in a way that people can relate to. Everything you hear about computers being a series of numbers is wrong. We visualize numbers in a precise, quantitative way. But the numbers are not a collection of isolated events. To understand them, we need to understand the broader context.

Friends don’t let friends use pie charts

Businesses and analysts have done this since pie charts first appeared on Microsoft Excel sheets. When presenting data, break down your pie chart into its component segments.

 As opposed to an equal-sized circle for the average earnings for all the employees, share a pie chart where the percentages for each individual segment are different, with a link to the corresponding chart.

 Pair with explanatory text, show their correlation, and make your choice based on your audience, not on whether you want to scare or “educate” them. The majority of audiences will see the same image, regardless of whether it’s presented in a bar chart, bar chart, line chart, or something else.

Choose the appropriate chart

Does the data make logical sense? Check your assumptions against the data.  Are the graphs charting only part of the story? Include other variables in the graphs.  Avoid using axis labels to mislead. Never rely on axes to infer, “logical” conclusions.  Trust your eyes: you know what information your brain can process.

Think of numbers like music — they are pleasing, but not overwhelming.  Save 3D for the movies. When everyone is enjoying 4K, 8K, and beyond, it’s hard to envision your audience without the new stuff. I remember the first time I got to see HDTV. At home, I sat behind a chair and kept turning around to watch the TV. But at the theatre, I didn’t need a chair. All I had to do was look up, and see the giant screen, the contrast, and the detail.

Don’t mix chart types for no reason

Excel chart s with colored areas help people focus. Arrows give us scale. Assume your audience doesn’t understand what you’re saying, even if they do. Nobody wants to open a recipe book to learn how to cook soup. Instead, we start with a recipe.

Use a formula to communicate your analysis with as few words as possible. Keep it simple.  Resist the urge to over-complicate your presentation. A word cloud is not a word cloud. A bar chart is not a bar chart. If you use a word cloud to illustrate a chart, consider replacing a few words with a gif. A bar chart doesn’t need clouds. And a bar chart doesn’t need clouds.  If there’s one thing that’s sure to confuse your audience, it’s bar charts.

Use color with intention

Use color with intention. It’s not about pretty. When it comes to presenting data clearly, “informative” is more important than “beautiful.” 

However, visualizations like maps, axes, or snapshots can help visual communication to avoid this pitfall. If you are going to show a few locations on a map, make sure each location has a voice and uses a distinct color. Avoid repeating colors from the map or bottom bar in all the visuals. Be consistent with how you present the data .  A pie chart is not very interesting if all it shows is a bunch of varying sizes of the pie.

Data analysis in the workplace, and how it will impact the future of business

Business leaders are taking note of the importance of data analysis skills in their organisation, as it can make an enormous impact on business.

 Larger organisations such as Google, Amazon and Facebook employ huge teams of analysts to create their data and statistics. We are already seeing the rise of the next generation of big data analysts – those who can write code that analyses and visualizes the data and report back information to a company to help it improve efficiency and increase revenue. 

The increasing need for high-level understanding of data analysis has already led to the role of data analyst becoming available at university level. It is no longer a mandatory business qualification but one that can enhance your CV.

By understanding the importance of each variable, you can improve your business by managing your time and creating more effective systems and processes for running your business. The focus shifts from just providing services to providing value to your customers, creating a better, more intuitive experience for them so they can work with your company for the long-term. 

Adopting these small steps will allow you to be more effective in your business and go from being an employee to an entrepreneur.

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Blog Data Visualization

10 Data Presentation Examples For Strategic Communication

By Krystle Wong , Sep 28, 2023

Data Presentation Examples

Knowing how to present data is like having a superpower. 

Data presentation today is no longer just about numbers on a screen; it’s storytelling with a purpose. It’s about captivating your audience, making complex stuff look simple and inspiring action. 

To help turn your data into stories that stick, influence decisions and make an impact, check out Venngage’s free chart maker or follow me on a tour into the world of data storytelling along with data presentation templates that work across different fields, from business boardrooms to the classroom and beyond. Keep scrolling to learn more! 

Click to jump ahead:

10 Essential data presentation examples + methods you should know

What should be included in a data presentation, what are some common mistakes to avoid when presenting data, faqs on data presentation examples, transform your message with impactful data storytelling.

Data presentation is a vital skill in today’s information-driven world. Whether you’re in business, academia, or simply want to convey information effectively, knowing the different ways of presenting data is crucial. For impactful data storytelling, consider these essential data presentation methods:

1. Bar graph

Ideal for comparing data across categories or showing trends over time.

Bar graphs, also known as bar charts are workhorses of data presentation. They’re like the Swiss Army knives of visualization methods because they can be used to compare data in different categories or display data changes over time. 

In a bar chart, categories are displayed on the x-axis and the corresponding values are represented by the height of the bars on the y-axis. 

data presentation or analysis

It’s a straightforward and effective way to showcase raw data, making it a staple in business reports, academic presentations and beyond.

Make sure your bar charts are concise with easy-to-read labels. Whether your bars go up or sideways, keep it simple by not overloading with too many categories.

data presentation or analysis

2. Line graph

Great for displaying trends and variations in data points over time or continuous variables.

Line charts or line graphs are your go-to when you want to visualize trends and variations in data sets over time.

One of the best quantitative data presentation examples, they work exceptionally well for showing continuous data, such as sales projections over the last couple of years or supply and demand fluctuations. 

data presentation or analysis

The x-axis represents time or a continuous variable and the y-axis represents the data values. By connecting the data points with lines, you can easily spot trends and fluctuations.

A tip when presenting data with line charts is to minimize the lines and not make it too crowded. Highlight the big changes, put on some labels and give it a catchy title.

data presentation or analysis

3. Pie chart

Useful for illustrating parts of a whole, such as percentages or proportions.

Pie charts are perfect for showing how a whole is divided into parts. They’re commonly used to represent percentages or proportions and are great for presenting survey results that involve demographic data. 

Each “slice” of the pie represents a portion of the whole and the size of each slice corresponds to its share of the total. 

data presentation or analysis

While pie charts are handy for illustrating simple distributions, they can become confusing when dealing with too many categories or when the differences in proportions are subtle.

Don’t get too carried away with slices — label those slices with percentages or values so people know what’s what and consider using a legend for more categories.

data presentation or analysis

4. Scatter plot

Effective for showing the relationship between two variables and identifying correlations.

Scatter plots are all about exploring relationships between two variables. They’re great for uncovering correlations, trends or patterns in data. 

In a scatter plot, every data point appears as a dot on the chart, with one variable marked on the horizontal x-axis and the other on the vertical y-axis.

data presentation or analysis

By examining the scatter of points, you can discern the nature of the relationship between the variables, whether it’s positive, negative or no correlation at all.

If you’re using scatter plots to reveal relationships between two variables, be sure to add trendlines or regression analysis when appropriate to clarify patterns. Label data points selectively or provide tooltips for detailed information.

data presentation or analysis

5. Histogram

Best for visualizing the distribution and frequency of a single variable.

Histograms are your choice when you want to understand the distribution and frequency of a single variable. 

They divide the data into “bins” or intervals and the height of each bar represents the frequency or count of data points falling into that interval. 

data presentation or analysis

Histograms are excellent for helping to identify trends in data distributions, such as peaks, gaps or skewness.

Here’s something to take note of — ensure that your histogram bins are appropriately sized to capture meaningful data patterns. Using clear axis labels and titles can also help explain the distribution of the data effectively.

data presentation or analysis

6. Stacked bar chart

Useful for showing how different components contribute to a whole over multiple categories.

Stacked bar charts are a handy choice when you want to illustrate how different components contribute to a whole across multiple categories. 

Each bar represents a category and the bars are divided into segments to show the contribution of various components within each category. 

data presentation or analysis

This method is ideal for highlighting both the individual and collective significance of each component, making it a valuable tool for comparative analysis.

Stacked bar charts are like data sandwiches—label each layer so people know what’s what. Keep the order logical and don’t forget the paintbrush for snazzy colors. Here’s a data analysis presentation example on writers’ productivity using stacked bar charts:

data presentation or analysis

7. Area chart

Similar to line charts but with the area below the lines filled, making them suitable for showing cumulative data.

Area charts are close cousins of line charts but come with a twist. 

Imagine plotting the sales of a product over several months. In an area chart, the space between the line and the x-axis is filled, providing a visual representation of the cumulative total. 

data presentation or analysis

This makes it easy to see how values stack up over time, making area charts a valuable tool for tracking trends in data.

For area charts, use them to visualize cumulative data and trends, but avoid overcrowding the chart. Add labels, especially at significant points and make sure the area under the lines is filled with a visually appealing color gradient.

data presentation or analysis

8. Tabular presentation

Presenting data in rows and columns, often used for precise data values and comparisons.

Tabular data presentation is all about clarity and precision. Think of it as presenting numerical data in a structured grid, with rows and columns clearly displaying individual data points. 

A table is invaluable for showcasing detailed data, facilitating comparisons and presenting numerical information that needs to be exact. They’re commonly used in reports, spreadsheets and academic papers.

data presentation or analysis

When presenting tabular data, organize it neatly with clear headers and appropriate column widths. Highlight important data points or patterns using shading or font formatting for better readability.

9. Textual data

Utilizing written or descriptive content to explain or complement data, such as annotations or explanatory text.

Textual data presentation may not involve charts or graphs, but it’s one of the most used qualitative data presentation examples. 

It involves using written content to provide context, explanations or annotations alongside data visuals. Think of it as the narrative that guides your audience through the data. 

Well-crafted textual data can make complex information more accessible and help your audience understand the significance of the numbers and visuals.

Textual data is your chance to tell a story. Break down complex information into bullet points or short paragraphs and use headings to guide the reader’s attention.

10. Pictogram

Using simple icons or images to represent data is especially useful for conveying information in a visually intuitive manner.

Pictograms are all about harnessing the power of images to convey data in an easy-to-understand way. 

Instead of using numbers or complex graphs, you use simple icons or images to represent data points. 

For instance, you could use a thumbs up emoji to illustrate customer satisfaction levels, where each face represents a different level of satisfaction. 

data presentation or analysis

Pictograms are great for conveying data visually, so choose symbols that are easy to interpret and relevant to the data. Use consistent scaling and a legend to explain the symbols’ meanings, ensuring clarity in your presentation.

data presentation or analysis

Looking for more data presentation ideas? Use the Venngage graph maker or browse through our gallery of chart templates to pick a template and get started! 

A comprehensive data presentation should include several key elements to effectively convey information and insights to your audience. Here’s a list of what should be included in a data presentation:

1. Title and objective

  • Begin with a clear and informative title that sets the context for your presentation.
  • State the primary objective or purpose of the presentation to provide a clear focus.

data presentation or analysis

2. Key data points

  • Present the most essential data points or findings that align with your objective.
  • Use charts, graphical presentations or visuals to illustrate these key points for better comprehension.

data presentation or analysis

3. Context and significance

  • Provide a brief overview of the context in which the data was collected and why it’s significant.
  • Explain how the data relates to the larger picture or the problem you’re addressing.

4. Key takeaways

  • Summarize the main insights or conclusions that can be drawn from the data.
  • Highlight the key takeaways that the audience should remember.

5. Visuals and charts

  • Use clear and appropriate visual aids to complement the data.
  • Ensure that visuals are easy to understand and support your narrative.

data presentation or analysis

6. Implications or actions

  • Discuss the practical implications of the data or any recommended actions.
  • If applicable, outline next steps or decisions that should be taken based on the data.

data presentation or analysis

7. Q&A and discussion

  • Allocate time for questions and open discussion to engage the audience.
  • Address queries and provide additional insights or context as needed.

Presenting data is a crucial skill in various professional fields, from business to academia and beyond. To ensure your data presentations hit the mark, here are some common mistakes that you should steer clear of:

Overloading with data

Presenting too much data at once can overwhelm your audience. Focus on the key points and relevant information to keep the presentation concise and focused. Here are some free data visualization tools you can use to convey data in an engaging and impactful way. 

Assuming everyone’s on the same page

It’s easy to assume that your audience understands as much about the topic as you do. But this can lead to either dumbing things down too much or diving into a bunch of jargon that leaves folks scratching their heads. Take a beat to figure out where your audience is coming from and tailor your presentation accordingly.

Misleading visuals

Using misleading visuals, such as distorted scales or inappropriate chart types can distort the data’s meaning. Pick the right data infographics and understandable charts to ensure that your visual representations accurately reflect the data.

Not providing context

Data without context is like a puzzle piece with no picture on it. Without proper context, data may be meaningless or misinterpreted. Explain the background, methodology and significance of the data.

Not citing sources properly

Neglecting to cite sources and provide citations for your data can erode its credibility. Always attribute data to its source and utilize reliable sources for your presentation.

Not telling a story

Avoid simply presenting numbers. If your presentation lacks a clear, engaging story that takes your audience on a journey from the beginning (setting the scene) through the middle (data analysis) to the end (the big insights and recommendations), you’re likely to lose their interest.

Infographics are great for storytelling because they mix cool visuals with short and sweet text to explain complicated stuff in a fun and easy way. Create one with Venngage’s free infographic maker to create a memorable story that your audience will remember.

Ignoring data quality

Presenting data without first checking its quality and accuracy can lead to misinformation. Validate and clean your data before presenting it.

Simplify your visuals

Fancy charts might look cool, but if they confuse people, what’s the point? Go for the simplest visual that gets your message across. Having a dilemma between presenting data with infographics v.s data design? This article on the difference between data design and infographics might help you out. 

Missing the emotional connection

Data isn’t just about numbers; it’s about people and real-life situations. Don’t forget to sprinkle in some human touch, whether it’s through relatable stories, examples or showing how the data impacts real lives.

Skipping the actionable insights

At the end of the day, your audience wants to know what they should do with all the data. If you don’t wrap up with clear, actionable insights or recommendations, you’re leaving them hanging. Always finish up with practical takeaways and the next steps.

Can you provide some data presentation examples for business reports?

Business reports often benefit from data presentation through bar charts showing sales trends over time, pie charts displaying market share,or tables presenting financial performance metrics like revenue and profit margins.

What are some creative data presentation examples for academic presentations?

Creative data presentation ideas for academic presentations include using statistical infographics to illustrate research findings and statistical data, incorporating storytelling techniques to engage the audience or utilizing heat maps to visualize data patterns.

What are the key considerations when choosing the right data presentation format?

When choosing a chart format , consider factors like data complexity, audience expertise and the message you want to convey. Options include charts (e.g., bar, line, pie), tables, heat maps, data visualization infographics and interactive dashboards.

Knowing the type of data visualization that best serves your data is just half the battle. Here are some best practices for data visualization to make sure that the final output is optimized. 

How can I choose the right data presentation method for my data?

To select the right data presentation method, start by defining your presentation’s purpose and audience. Then, match your data type (e.g., quantitative, qualitative) with suitable visualization techniques (e.g., histograms, word clouds) and choose an appropriate presentation format (e.g., slide deck, report, live demo).

For more presentation ideas , check out this guide on how to make a good presentation or use a presentation software to simplify the process.  

How can I make my data presentations more engaging and informative?

To enhance data presentations, use compelling narratives, relatable examples and fun data infographics that simplify complex data. Encourage audience interaction, offer actionable insights and incorporate storytelling elements to engage and inform effectively.

The opening of your presentation holds immense power in setting the stage for your audience. To design a presentation and convey your data in an engaging and informative, try out Venngage’s free presentation maker to pick the right presentation design for your audience and topic. 

What is the difference between data visualization and data presentation?

Data presentation typically involves conveying data reports and insights to an audience, often using visuals like charts and graphs. Data visualization , on the other hand, focuses on creating those visual representations of data to facilitate understanding and analysis. 

Now that you’ve learned a thing or two about how to use these methods of data presentation to tell a compelling data story , it’s time to take these strategies and make them your own. 

But here’s the deal: these aren’t just one-size-fits-all solutions. Remember that each example we’ve uncovered here is not a rigid template but a source of inspiration. It’s all about making your audience go, “Wow, I get it now!”

Think of your data presentations as your canvas – it’s where you paint your story, convey meaningful insights and make real change happen. 

So, go forth, present your data with confidence and purpose and watch as your strategic influence grows, one compelling presentation at a time.

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Research Techniques for Computer Science, Information Systems and Cybersecurity pp 115–138 Cite as

Data Collection, Presentation and Analysis

  • Uche M. Mbanaso 4 ,
  • Lucienne Abrahams 5 &
  • Kennedy Chinedu Okafor 6  
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This chapter covers the topics of data collection, data presentation and data analysis. It gives attention to data collection for studies based on experiments, on data derived from existing published or unpublished data sets, on observation, on simulation and digital twins, on surveys, on interviews and on focus group discussions. One of the interesting features of this chapter is the section dealing with using measurement scales in quantitative research, including nominal scales, ordinal scales, interval scales and ratio scales. It explains key facets of qualitative research including ethical clearance requirements. The chapter discusses the importance of data visualization as key to effective presentation of data, including tabular forms, graphical forms and visual charts such as those generated by Atlas.ti analytical software.

  • Computer science data
  • Cybersecurity data analysis
  • Cybersecurity experiments
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Mbanaso, U.M., Abrahams, L., Okafor, K.C. (2023). Data Collection, Presentation and Analysis. In: Research Techniques for Computer Science, Information Systems and Cybersecurity. Springer, Cham. https://doi.org/10.1007/978-3-031-30031-8_7

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10 Tips for Presenting Data

10 tips for presenting Data

Big data. Analytics. Data science. Businesses are clamoring to use data to get a competitive edge, but all the data in the world won’t help if your stakeholders can’t understand, or if their eyes glaze over as you present your incredibly insightful analysis . This post outlines my top ten tips for presenting data.

It’s worth noting that these tips are tool agnostic—whether you use Data Studio, Domo, Tableau or another data viz tool, the principles are the same. However, don’t assume your vendors are in lock-step with data visualization best practices! Vendor defaults frequently violate key principles of data visualization, so it’s up to the analyst to put these principles in practice.

Here are my 10 tips for presenting data:

  • Recognize that presentation matters
  • Don’t scare people with numbers
  • Maximize the data pixel ratio
  • Save 3D for the movies
  • Friends don’t let friends use pie charts
  • Choose the appropriate chart
  • Don’t mix chart types for no reason
  • Don’t use axes to mislead
  • Never rely solely on color
  • Use color with intention

1) Recognize That Presentation Matters

The first step to presenting data is to understand that how you present data matters . It’s common for analysts to feel they’re not being heard by stakeholders, or that their analysis or recommendations never generate action. The problem is, if you’re not communicating data clearly for business users, it’s really easy for them to tune out.

Analysts may ask, “But I’m so busy with the actual work of putting together these reports. Why should I take the time to ‘make it pretty’?”

Because it’s not about “making things pretty.” It’s about making your data understandable.

My very first boss in Analytics told me, “As an analyst, you are an information architect.” It’s so true. Our job is to take a mass of information and architect it in such a way that people can easily comprehend it.

Take these two visuals. The infographic style shows Top 10 Salaries at Google. The first one is certainly “prettier.” However, the visual is pretty meaningless, and you have to actually read the information to understand any of it. (That defeats the purpose of a data viz!)

Pretty, but not helpful

On the flip side, the simpler (but far less pretty) visualization makes it very easy to see:

  • Which job category pays the most
  • Which pays the least
  • Which has the greatest range of salaries
  • Which roles have similar ranges

It’s not about pretty. When it comes to presenting data clearly, “informative” is more important than “beautiful.”

Just as we optimize our digital experiences, our analyses must be optimized to how people perceive and process information. You can think of this as a three-step process:

  • Information passes through the Visual Sensory Register . This is pre-attentive processing—it’s what we process before we’re even aware we’re doing so. Certain things will stand out to us, objects may get unconsciously grouped together.
  • From there, information passes to Short Term Memory. This is a limited capacity system, and information not considered “useful” will be discarded. We will only retain 3-9 “chunks” of visual information. However, a “chunk” can be defined differently based on how information is grouped. For example, we might be able to remember 3-9 letters. But, we could also remember 3-9 words, or 3-9 song lyrics! Your goal, therefore, is to present information in such a way that people can easily “chunk” information, to allow greater retention through short-term memory. (For example, a table of data ensures the numbers themselves can’t possibly all be retained, but a chart that shows our conversion rate trending down may be retained as one chunk of information—“trending down.”)
  • From short-term memory, information is passed to Long-Term Memory. The goal here is to retain meaningful information—but not the precise details.

2) Don’t Scare People with Numbers

Analysts like numbers. Not everybody does! Many of your stakeholders may feel overwhelmed by numbers, data, charts. But when presenting data, there are little things you can do to make numbers immediately more “friendly.”

Simple formatting

Don’t make people count zeros in numbers! (e.g. 1000000 vs. 100,000,000).

Skip unnecessary decimals

How many decimals are “necessary” depends on the range of your values. If your values range from 2 to 90 percent, you don’t need two decimals places.

But on the flip side, if you have numbers that are really close (for example, all values are within a few percent of each other) it’s important to include decimal places.

Too often, this comes from confusing “precision” with “accuracy.” Just because you are more precise (in including more decimal places) doesn’t make your data more accurate. It just gives the illusion of it.

Right align numbers

Always right-align columns of numbers. This is the default in many solutions, but not always. What it allows for is your data to form a “quasi bar chart” where people can easily scan for the biggest number, by the number of characters. This can be harder to do if you center-align.

3) Maximize the Data-Pixel Ratio

The Data-Pixel Ratio originally stems from Edward Tufte’s “Data-Ink Ratio”, later renamed the “Data-Pixel Ratio” by Stephen Few. The more complicated explanation (with an equation, GAH!) is:

A simpler way of thinking of it: Your pixels (or ink) should be used for data display, and not for fluff or decoration. (I like to explain that I’m just really stingy with printer ink—so, I don’t want to print a ton of wasted decorations.)

Here are some quick transformations to maximize the data-pixel ratio:

Avoid repeating information

For example, if you include the word “Region” in the column header, there’s no need to repeat the word in each cell within the column. You don’t even need to repeat the dollar sign. Once we know the column is in dollars, we know all the values are too.

Avoid repeating information when presenting data

For bar and column charts:

  • Remove borders (that Excel loves to put in by default, and Google Sheets still doesn’t let you remove them, grumble grumble.)
  • Display information horizontally. Choosing a bar over a column chart can make the axis easier to read.
  • Condense axes, to show values “in Millions” or “in K”, rather than unnecessarily repeating zeros (“,000”)

For line charts:

  • Remove unnecessary legends. If you only have one series in a line chart, the title will explain what the chart is—a legend is duplicated information.
  • Grey (or even remove) grid lines. While sometimes grid lines can be useful to help users track across to see the value on the y-axis, the lines don’t need to be heavy to guide the eyes (and certainly not as visually important as the data).

4) Save 3D for the Movies

These two charts have the same information. In the top left one, you can see at a glance that the bar is slightly above $150,000. In the bottom one, you can “kind of sort of tell” that it’s at $150,000, but you have to work much harder to figure that out. With a 3D chart you’re adding an extra cognitive step, where someone has to think about what they’re looking at.

And don't even get me started on this one:

However, I’ll concede: there is an exception to every rule. When is 3D okay? When it does a better job telling the story , and isn’t just there to make it “snazzy.” For example, take this recent chart from the 2016 election: 3D adds a critical element of information, that a 2D version would miss.

5) Friends Don’t Let Friends Use Pie Charts

It’s easy to hate on pie charts (and yet, every vendor is excited to announce that they have ZOMG EXPLODING DONUT CHARTS! just added in their recent release).

However, there are some justified reasons for the backlash against the use (and especially, the overuse) of pie charts when presenting data:

  • We aren’t as good at judging the relative differences in area or circles, versus lines . For example, if we look at a line, we’re more easily able to say “that line is about a third bigger.”We are not adept at doing this same thing with area or circles, so often a bar or column chart is simply easier for us to process.
  • They’re used incorrectly . Pie charts are intended to show “parts of a whole”, so a pie chart that adds up to more than 100% is a misuse of the visualization.
  • They have too many pieces . Perhaps they do add up to 100%, but there’s little a pie chart like this will do to help you understand the data.

With that understood, if you feel you must use pie charts, the following stipulations apply:

  • The pie chart shouldn’t represent more than three items.
  • The data has to represent parts of a whole (aka, the pieces must add to 100%).
  • You can only use one. As soon as you need to compare data (for example, three series across multiple years) then pie charts are a no-go. Instead, go for a stacked bar chart.

Like 3D, pie charts are acceptable when they are the best possible way for presenting data and getting your message across. This is an example of where, hands-down, a pie chart is the right visualization:

6) Choose the Appropriate Chart for Presenting Data

A chart should be carefully chosen, to convey the message you want someone to take from your data presentation. For example, are you trying to show that the United States and India’s average order value are similar? Or that India’s revenue is trending up more quickly? Or that Asia is twice the rest of the world?

For a more comprehensive guide, check out Extreme Presentation’s Chart Chooser. But in the meantime, here is a quick version for some commonly used charts:

Line charts

Use line charts to demonstrate trends. If there are important things that happened, you can also highlight specific point

Bar or column charts

Bar or column charts should be used to emphasize the differences between things.

If you don’t have much space, you might consider using sparklines for presenting data trends. Sparklines are a small chart contained within a single cell of a table. (You can also choose to use bar charts within your data table.)

Here are some resources on how to build sparklines into the different data viz platforms:

Google Sheets

7) Don’t Mix Chart Types for No Reason

I repeat. Don’t mix chart types for no reason . Presenting data sets together should tell a story or reveal insights together, that isn’t possible if left apart. Unfortunately, far too many charts involving cramming multiple data series on them is purely to conserve the space of adding another chart. The problem is, as soon as you put those two series of data together, your end users are going to assume there’s a connection between them (and waste valuable brain power trying to figure out what it is).

Below are good and bad examples of mixing chart types when presenting data. On the first, we have a column and line chart together, because we’re trying to demonstrate that the two metrics trend similarly. Together they are telling a story, that they wouldn’t tell on two separate charts.

The second, however, is an example of “just trying to fit two series onto a chart.”

For the second chart, a better option for presenting the data might be to have two side-by-side bar or column charts.

8) Don’t Use Axes to Mislead

“If you torture the data long enough, it will confess to anything” – Ronald Coase

One easy way to mislead readers is to change the axes of your data. Doing so quickly magnifies what might be small differences, and can distort the story your data is telling you. For example, starting the axis at 155,000 makes the differences between the highs and lows look more dramatic.

In the next example, the line chart doesn’t actually correspond to the axis! (Did you know 8.6 is more than 8.8?!)

The most truthful option is to always start your axes at zero. But sometimes, we need to show differences in metrics that don’t shift much over time. (For example, our conversion rate might range between 1.0% and 1.3% from month to month.) In that case, my recommendation would be to show the more truthful axis starting at zero, but provide a second view of the chart (a “zoomed in view”, so to speak) that shows a smaller range on the axis, so you can see the month-to-month change.

9) Never Rely Solely on Color When Presenting Data

Color is commonly used as a way to differentiate “good” vs. “bad” results, or “above” or “below” target. The problem is, about ten percent of the population is colorblind! And it’s not just red/green colorblind (though that’s the most common). There are many other kinds of colorblindness. As a result, ten percent of your stakeholders may actually not be comprehending your color scheme. (Not to mention, all black and white printers are “colorblind.”)

That doesn’t mean you can’t use any red or green (it can be an easily understood color scheme) when presenting data. But you do have to check that your data visualization is understandable by those with colorblindness, or if someone prints your document in black and white.

Additionally, there are also differences in how colors are perceived in different cultures. (For example, red means “death” in some cultures.) If you are distributing your data presentation globally, this is an additional factor to be conscious of.

10) Use Color with Intention

In the below chart, the colors are completely meaningless. (Or, as I like to call it, “rainbow barf.”)

Being careful with color also means using it consistently. If you are using multiple charts with the same values, you have to keep the colors consistent. Consider the tax on someone’s interpretation of your visualization if they constantly have to think “Okay, Facebook is blue on this chart, but it’s green on this other one.” Not only are you making them think really hard to do those comparisons, but more likely, they’re going to draw an incorrect conclusion.

So be thoughtful with how you use color! A good option can be to use brand colors. These are typically well-understood uses of color (for example, Facebook is blue, YouTube is red.) This may help readers understand the chart more intuitively.

(Data Studio only recently added a feature where you can keep the colors of data consistent across charts!)

Another user-friendly method of using color intentionally is to match your series color to your axis (where you have a dual-axis chart). This makes it very easy for a user to understand which series relates to which axis, without much thought.

Bonus Tip 11. Dashboards Should Follow The Above Data Visualization Rules

So, what about dashboards? Dashboards should follow all the same basic rules of presenting data, plus one important rule:

“A dashboard is a visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance.” -Stephen Few (Emphasis added.)

Key phrase: “on a single screen.” If you are expecting someone to look at your dashboard, and make connections between different data points, you are relying on their short-term memory. (Which, as discussed before, is a limited-capacity system.) So, dashboards must follow all the same data viz rules, but additionally, to be called a “dashboard”, it must be one page/screen/view. (So, that 8 page report is not a “dashboard”! You can have longer “reports”, but to truly be considered a “dashboard”, they must fit into one view.)

I hope these tips for presenting data have been useful! If you’re interested in learning more, these are some books I’d recommend checking out:

The Wall Street Journal Guide to Information Graphics

Information Dashboard Design

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Get Sensible about Sensitive Data

10 Superb Data Presentation Examples To Learn From

The best way to learn how to present data effectively is to see data presentation examples from the professionals in the field.

We collected superb examples of graphical presentation and visualization of data in statistics, research, sales, marketing, business management, and other areas.

On this page:

How to present data effectively? Clever tips.

  • 10 Real-life examples of data presentation with interpretation.

Download the above infographic in PDF

Your audience should be able to walk through the graphs and visualizations easily while enjoy and respond to the story.

[bctt tweet=”Your reports and graphical presentations should not just deliver statistics, numbers, and data. Instead, they must tell a story, illustrate a situation, provide proofs, win arguments, and even change minds.” username=””]

Before going to data presentation examples let’s see some essential tips to help you build powerful data presentations.

1. Keep it simple and clear

The presentation should be focused on your key message and you need to illustrate it very briefly.

Graphs and charts should communicate your core message, not distract from it. A complicated and overloaded chart can distract and confuse. Eliminate anything repetitive or decorative.

2. Pick up the right visuals for the job

A vast number of types of graphs and charts are available at your disposal – pie charts, line and bar graphs, scatter plot , Venn diagram , etc.

Choosing the right type of chart can be a tricky business. Practically, the choice depends on 2 major things: on the kind of analysis you want to present and on the data types you have.

Commonly, when we aim to facilitate a comparison, we use a bar chart or radar chart. When we want to show trends over time, we use a line chart or an area chart and etc.

3. Break the complex concepts into multiple graphics

It’s can be very hard for a public to understand a complicated graphical visualization. Don’t present it as a huge amount of visual data.

Instead, break the graphics into pieces and illustrate how each piece corresponds to the previous one.

4. Carefully choose the colors

Colors provoke different emotions and associations that affect the way your brand or story is perceived. Sometimes color choices can make or break your visuals.

It is no need to be a designer to make the right color selections. Some golden rules are to stick to 3 or 4 colors avoiding full-on rainbow look and to borrow ideas from relevant chart designs.

Another tip is to consider the brand attributes and your audience profile. You will see appropriate color use in the below data presentation examples.

5. Don’t leave a lot of room for words

The key point in graphical data presentation is to tell the story using visuals and images, not words. Give your audience visual facts, not text.

However, that doesn’t mean words have no importance.

A great advice here is to think that every letter is critical, and there’s no room for wasted and empty words. Also, don’t create generic titles and headlines, build them around the core message.

6. Use good templates and software tools

Building data presentation nowadays means using some kind of software programs and templates. There are many available options – from free graphing software solutions to advanced data visualization tools.

Choosing a good software gives you the power to create good and high-quality visualizations. Make sure you are using templates that provides characteristics like colors, fonts, and chart styles.

A small investment of time to research the software options prevents a large loss of productivity and efficiency at the end.

10 Superb data presentation examples 

Here we collected some of the best examples of data presentation made by one of the biggest names in the graphical data visualization software and information research.

These brands put a lot of money and efforts to investigate how professional graphs and charts should look.

1. Sales Stage History  Funnel Chart 

Data is beautiful and this sales stage funnel chart by Zoho Reports prove this. The above funnel chart represents the different stages in a sales process (Qualification, Need Analysis, Initial Offer, etc.) and shows the potential revenue for each stage for the last and this quarter.

The potential revenue for each sales stage is displayed by a different color and sized according to the amount. The chart is very colorful, eye-catching, and intriguing.

2. Facebook Ads Data Presentation Examples

These are other data presentation examples from Zoho Reports. The first one is a stacked bar chart that displays the impressions breakdown by months and types of Facebook campaigns.

Impressions are one of the vital KPI examples in digital marketing intelligence and business. The first graph is designed to help you compare and notice sharp differences at the Facebook campaigns that have the most influence on impression movements.

The second one is an area chart that shows the changes in the costs for the same Facebook campaigns over the months.

The 2 examples illustrate how multiple and complicated data can be presented clearly and simply in a visually appealing way.

3. Sales Opportunity Data Presentation

These two bar charts (stacked and horizontal bar charts) by Microsoft Power Bi are created to track sales opportunities and revenue by region and sales stage.

The stacked bar graph shows the revenue probability in percentage determined by the current sales stage (Lead, Quality, Solution…) over the months. The horizontal bar chart represents the size of the sales opportunity (Small, Medium, Large) according to regions (East, Central, West).

Both graphs are impressive ways for a sales manager to introduce the upcoming opportunity to C-level managers and stakeholders. The color combination is rich but easy to digest.

4. Power 100 Data Visualization 

Want to show hierarchical data? Treemaps can be perfect for the job. This is a stunning treemap example by Infogram.com that shows you who are the most influential industries. As you see the Government is on the top.

This treemap is a very compact and space-efficient visualization option for presenting hierarchies, that gives you a quick overview of the structure of the most powerful industries.

So beautiful way to compare the proportions between things via their area size.

When it comes to best research data presentation examples in statistics, Nielsen information company is an undoubted leader. The above professional looking line graph by Nielsen represent the slowing alcoholic grow of 4 alcohol categories (Beer, Wine, Spirits, CPG) for the period of 12 months.

The chart is an ideal example of a data visualization that incorporates all the necessary elements of an effective and engaging graph. It uses color to let you easily differentiate trends and allows you to get a global sense of the data. Additionally, it is incredibly simple to understand.

6. Digital Health Research Data Visualization Example

Digital health is a very hot topic nowadays and this stunning donut chart by IQVIA shows the proportion of different mobile health apps by therapy area (Mental Health, Diabetes, Kidney Disease, and etc.). 100% = 1749 unique apps.

This is a wonderful example of research data presentation that provides evidence of Digital Health’s accelerating innovation and app expansion.

Besides good-looking, this donut chart is very space-efficient because the blank space inside it is used to display information too.

7. Disease Research Data Visualization Examples

Presenting relationships among different variables is hard to understand and confusing -especially when there is a huge number of them. But using the appropriate visuals and colors, the IQVIA did a great job simplifying this data into a clear and digestible format.

The above stacked bar charts by IQVIA represents the distribution of oncology medicine spendings by years and product segments (Protected Brand Price, Protected Brand Volume, New Brands, etc.).

The chart allows you to clearly see the changes in spendings and where they occurred – a great example of telling a deeper story in a simple way.

8. Textual and Qualitative Data Presentation Example

When it comes to easy to understand and good looking textual and qualitative data visualization, pyramid graph has a top place. To know what is qualitative data see our post quantitative vs qualitative data .

9. Product Metrics Graph Example

If you are searching for excel data presentation examples, this stylish template from Smartsheet can give you good ideas for professional looking design.

The above stacked bar chart represents product revenue breakdown by months and product items. It reveals patterns and trends over the first half of the year that can be a good basis for data-driven decision-making .

10. Supply Chain Data Visualization Example 

This bar chart created by ClicData  is an excellent example of how trends over time can be effectively and professionally communicated through the use of well-presented visualization.

It shows the dynamics of pricing through the months based on units sold, units shipped, and current inventory. This type of graph pack a whole lot of information into a simple visual. In addition, the chart is connected to real data and is fully interactive.

The above data presentation examples aim to help you learn how to present data effectively and professionally.

About The Author

data presentation or analysis

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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Top 10 Data Analysis Templates with Samples and Examples

Top 10 Data Analysis Templates with Samples and Examples

Mohammed Sameer

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If people could eat data instead of food, we could end world hunger with enough spare data left over to tackle 3 famines.

This startling but obvious statement underscores the abundance of data available to the human race today and the humungous rate at which it has grown in our digital age. Just as sustenance nourishes our bodies, data fuels our intellect, satiating the hunger for insights and understanding. 

Data is the foundation upon which the structure of information stands tall. Imagine gazing at a puzzle's scattered pieces – each is important, might be beautiful and vital, but the true picture emerges only when the pieces interlock. Similarly, data is the root of knowledge for today’s businesses. Our new Data Analysis Templates are the masterful hands that bring all that scattered knowledge and wisdom together.

These PPT Presentations emerge as essential companions in a landscape where accurate decision-making means the difference between thriving and surviving. Understanding data is pivotal in the symphony of business strategies, marketing endeavors, and research pursuits. 

The 100% customizable nature of the templates provides you with the desired flexibility to edit your presentations. The content-ready slides give you the much-needed structure.

Let’s explore!

Template 1: Data Analysis Process PPT Set

Use this PPT Set to help stakeholders understand difficulties that mar the data analysis process and gain valuable insights. Explore the crucial stages of data analysis, from establishing data requirements and efficient data collection to thorough data processing and cleaning. This PPT Design highlights the often underestimated yet pivotal phase of data cleaning. With this template, you'll understand how data lays the foundation for seamless analysis, leading to more accurate results and impactful communication. Download now!

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This holistic PPT Bundle guides you through the complex stages of visualization and presentation while offering a profound understanding of each crucial phase. Use this presentation template to understand the essence of successful data analysis, as it breaks down the process into digestible segments. From the initial steps of business issue comprehension and data understanding to data preparation, exploratory analysis, monitoring, validation, and finally, captivating visualization and presentation – every facet is covered. This PPT Preset goes beyond mere process explanation, offering a robust framework for the holistic development of data conceptualization, collection, analysis, and cleaning procedures. Get it today!

Data Analysis Business Evaluation Process for Visualization and Presentation

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Template 3: Data Requirement Analysis PPT Bundle

Navigating challenges of problem-solving, prioritization, and data insight, this PPT Presentation presents a strategic roadmap that transforms raw information into actionable intelligence. It starts with a deep dive into the heart of your business challenges. Focusing on defining the core problems, this presentation template guides you through the process of setting priorities, ensuring every move is a step closer to your objectives. Data collection, a crucial cornerstone, is explained through insightful visual aids and organized segments. Witness the transformation of disparate data points into a coherent narrative, empowering you to decipher trends, anomalies, and opportunities.

This PPT Template equips you with the tools to not only gather data but also comprehend its implications, turning information into true knowledge. Navigating the challenges of data requirement analysis is no longer a daunting task. From security gaps that demand attention to complex data systems that require expertise, our template ensures you're prepared to overcome these hurdles with confidence. The high costs that often come with data analysis are confronted head-on, unraveling budget-friendly strategies that don't compromise on quality. Get this template today!

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Template 4: Big Data Analysis PPT Set

This comprehensive PPT Deck presents a pre-made Big Data Analysis funnel that guides you through the rather complex process of turning data into gold. Gain a competitive edge by understanding effective data analysis techniques of association rule learning, classification tree analysis, genetic algorithm, regression analysis, and sentiment analysis. It's more than a run-of-the-mill PPT Presentation; it's a transformative tool. Invest in a big data analysis PPT like resource that's not just about graphs and numbers; get it now. Download now!

Big Data Analysis PPT Set

Template 5: Data Management Analysis PPT Framework

For achieving business excellence, the quest for efficient and time-saving solutions is a universal endeavor. Recognizing your aspirations, we present the Data Management Analysis PowerPoint Presentation — an invaluable asset for seamless change management and effective data analysis. It incorporates PPT Slides designed to provide an effortless avenue for embracing change management and conducting incisive data analysis. It offers a cohesive platform for centralizing your objectives, ready to be shared with your team. The judicious use of text boxes empowers you to articulate your perspectives with precision on each pertinent subject. Download today!

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Template 6: Predictive Data Analysis PPT Layout

Get this PPT Preset to consolidate your stakeholder's grasp on predictive analytics, a discipline that uses statistical methodologies, cutting-edge machine learning algorithms, and a suite of tools to dissect historical data. This PPT Layout guides you through a well-structured journey, unfolding the essentials of predictive analytics, its foundational framework, and a suite of models that constitute its core. The significance of predictive analytics takes center stage, underscored by its multifaceted applications. Additionally, this resource has an Estimation Model PPT Slide, which explains the key tenets of diverse predictive analytics tools and their closely-knit workflows. The demarcation between the four pivotal categories of advanced analytics in this PPT deck receives careful attention. It sheds light on predictive analytics models – from classification to clustering models and beyond. Download now!

Predictive Data Analysis PPT Layout

Template 7: Dashboard For IT Operations Data Analysis

This PPT Template Dashboard is a dynamic representation of your operational landscape. This PPT Set helps track the total number of cases from inception to resolution. Visualize trends with a graph showcasing the weekly ebb and flow of opened and closed cases. Prioritize effectively, allocating resources where they matter most, as the presentation template depicts it across departments. Efficiency meets clarity as you explore the time distribution of tickets on a day-by-day basis. Gain a better understanding of workflow patterns and resource utilization. Analyze open case statuses, fostering an environment of proactive response and swift action. Download now!

Dashboard For IT Operations Data Analysis

Template 8: Quarterly Sales Data Analysis Report

Visualize your progress with ease using this PPT Template's intuitive presentation of monthly sales data. Get a clear view of team-wise statistics that showcase individual contributions, fostering a culture of recognition and growth. Uncover finer details through the nuanced comparison of total versus actual sales values, empowering you to identify trends and opportunities. Engage stakeholders in strategy evaluation as you assess team goals versus actual achievements. Pinpoint areas of excellence and those warranting attention, refining your approach. Download now!

Quarterly Sales Data Analysis Report

Template 9: Real-Time  Marketing Data Analysis

Here's a dynamic marketing analysis tool blending insights and aesthetics. It presents a pie chart comparing planned vs. actual budgets while diving deep into sections showcasing real-time marketing benefits: Elevated customer experiences, surging conversions, enhanced retention, and refined brand perception. Navigate budget allocation through intuitive bar graphs. Improve your strategy with data symphony, moving a step closer to success through informed choices. Download now!

Real-Time Marketing Data Analysis

Template 10: Data Analysis Process for Visualization and Presentation

Embark on a data-driven journey with this PPT Set. Learn the process of Data Analysis, Visualization, and Presentation to address complex business challenges. This PPT Design walks you through these stages, from issue identification and data preparation to exploratory analysis modeling. Witness raw data transform into insights through rigorous validation. Culminate in captivating visualizations and masterful presentations, setting new standards for impactful communication. Download now!

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Bridging Numbers and Narratives: Your Journey Through Data Analysis

In a world where data weaves the fabric of progress, our journey through this blog comes to an inspiring end. As you venture into data analysis armed with our templates, remember that each graph, each layout, and each piece of information is a brushstroke on the canvas of understanding. With every mouse click, you’re not just navigating slides; you're charting the course for informed decisions, breakthrough discoveries, and transformative strategies.

FAQs on Data Analysis

What is data analysis.

Data analysis involves inspecting, cleansing, transforming, and modeling data to derive meaningful insights, draw conclusions, and support decision-making. It encompasses various techniques, including statistical methods, machine learning, and visualization, to uncover patterns, trends, and relationships within datasets.

What are the four types of data analysis?

There are four main types of data analysis:

  • Descriptive Analysis: This type of analysis focuses on summarizing and describing the main features of a dataset. It involves statistical measures such as mean, median, mode, range, and standard deviation. Descriptive analysis aims to clearly understand the data's characteristics but doesn't involve drawing conclusions or making predictions.
  • Diagnostic Analysis: Diagnostic analysis involves digging deeper into data to understand why certain patterns or outcomes occurred. It aims to identify the root causes of specific events or trends. Techniques used in diagnostic analysis often include data visualization, exploratory data analysis, and statistical tests to uncover relationships and correlations.
  • Predictive Analysis: Predictive analysis involves using historical data to predict future events or outcomes. This type of analysis uses statistical models, machine learning algorithms, and data mining techniques to identify patterns and trends that can be used to forecast future trends. It's widely used in finance, marketing, and healthcare for making informed decisions.
  • Prescriptive Analysis: Prescriptive analysis goes beyond predicting future outcomes. It provides recommendations or solutions for specific situations based on historical and current data analysis. This type of analysis considers different possible actions and their potential outcomes to guide decision-making. Prescriptive analysis is often used in complex scenarios involving multiple variables and options.

Where is data analysis used?

Data analysis is used in a wide range of fields and industries, including but not limited to:

  • Business: Analyzing customer behavior, market trends, and financial performance.
  • Healthcare: Analyzing patient records, medical research data, and disease trends.
  • Science: Analyzing experimental results, simulations, and observations.
  • Finance: Analyzing investment trends, risk assessment, and portfolio management.
  • Marketing: Analyzing campaign effectiveness, consumer preferences, and market segmentation.
  • Social Sciences: Analyzing survey data, demographic trends, and human behavior.
  • Sports: Analyzing player performance, game statistics, and strategy optimization.

What is the main tool for data analysis?

There isn't a single "main" tool for data analysis, as the choice of tools depends on the specific tasks and the preferences of the analyst. However, some widely used tools for data analysis include:

  • Spreadsheet Software: Like Microsoft Excel or Google Sheets, used for basic data manipulation and visualization.
  • Statistical Software: Such as R and Python's libraries (e.g., pandas, numpy, scipy), used for in-depth statistical analysis and modeling.
  • Data Visualization Tools: Like Tableau, Power BI, or matplotlib/seaborn in Python, used to create visual representations of data.
  • Database Management Systems (DBMS): Such as SQL-based systems for querying and managing large datasets.
  • Machine Learning Libraries: Such as scikit-learn, TensorFlow, and PyTorch for building predictive models.

Why is data analysis important?

Data analysis is crucial for several reasons:

  • Informed Decision-Making: It provides insights that help individuals and organizations make informed decisions based on evidence rather than intuition.
  • Identifying Patterns and Trends: It helps to uncover hidden patterns, trends, and correlations in large datasets that might not be apparent on the surface.
  • Problem Solving: Data analysis aids in solving complex problems by providing a structured approach to understanding and addressing issues.
  • Improving Efficiency and Performance: It allows businesses to optimize processes, improve efficiency, and enhance performance based on data-driven insights.
  • Innovation and Research: Data analysis is essential in scientific research and innovation, helping to validate hypotheses and drive discoveries.
  • Competitive Advantage: Organizations that effectively use data analysis gain a competitive edge by better understanding their customers, markets, and internal operations.
  • Risk Management: Data analysis enables better risk assessment and management by identifying potential issues or anomalies early on.
  • Resource Allocation: It helps allocate resources effectively by understanding where investments are most likely to yield positive outcomes.

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National Research Council; Division of Behavioral and Social Sciences and Education; Commission on Behavioral and Social Sciences and Education; Committee on Basic Research in the Behavioral and Social Sciences; Gerstein DR, Luce RD, Smelser NJ, et al., editors. The Behavioral and Social Sciences: Achievements and Opportunities. Washington (DC): National Academies Press (US); 1988.

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The Behavioral and Social Sciences: Achievements and Opportunities.

  • Hardcopy Version at National Academies Press

5 Methods of Data Collection, Representation, and Analysis

This chapter concerns research on collecting, representing, and analyzing the data that underlie behavioral and social sciences knowledge. Such research, methodological in character, includes ethnographic and historical approaches, scaling, axiomatic measurement, and statistics, with its important relatives, econometrics and psychometrics. The field can be described as including the self-conscious study of how scientists draw inferences and reach conclusions from observations. Since statistics is the largest and most prominent of methodological approaches and is used by researchers in virtually every discipline, statistical work draws the lion’s share of this chapter’s attention.

Problems of interpreting data arise whenever inherent variation or measurement fluctuations create challenges to understand data or to judge whether observed relationships are significant, durable, or general. Some examples: Is a sharp monthly (or yearly) increase in the rate of juvenile delinquency (or unemployment) in a particular area a matter for alarm, an ordinary periodic or random fluctuation, or the result of a change or quirk in reporting method? Do the temporal patterns seen in such repeated observations reflect a direct causal mechanism, a complex of indirect ones, or just imperfections in the data? Is a decrease in auto injuries an effect of a new seat-belt law? Are the disagreements among people describing some aspect of a subculture too great to draw valid inferences about that aspect of the culture?

Such issues of inference are often closely connected to substantive theory and specific data, and to some extent it is difficult and perhaps misleading to treat methods of data collection, representation, and analysis separately. This report does so, as do all sciences to some extent, because the methods developed often are far more general than the specific problems that originally gave rise to them. There is much transfer of new ideas from one substantive field to another—and to and from fields outside the behavioral and social sciences. Some of the classical methods of statistics arose in studies of astronomical observations, biological variability, and human diversity. The major growth of the classical methods occurred in the twentieth century, greatly stimulated by problems in agriculture and genetics. Some methods for uncovering geometric structures in data, such as multidimensional scaling and factor analysis, originated in research on psychological problems, but have been applied in many other sciences. Some time-series methods were developed originally to deal with economic data, but they are equally applicable to many other kinds of data.

  • In economics: large-scale models of the U.S. economy; effects of taxation, money supply, and other government fiscal and monetary policies; theories of duopoly, oligopoly, and rational expectations; economic effects of slavery.
  • In psychology: test calibration; the formation of subjective probabilities, their revision in the light of new information, and their use in decision making; psychiatric epidemiology and mental health program evaluation.
  • In sociology and other fields: victimization and crime rates; effects of incarceration and sentencing policies; deployment of police and fire-fighting forces; discrimination, antitrust, and regulatory court cases; social networks; population growth and forecasting; and voting behavior.

Even such an abridged listing makes clear that improvements in methodology are valuable across the spectrum of empirical research in the behavioral and social sciences as well as in application to policy questions. Clearly, methodological research serves many different purposes, and there is a need to develop different approaches to serve those different purposes, including exploratory data analysis, scientific inference about hypotheses and population parameters, individual decision making, forecasting what will happen in the event or absence of intervention, and assessing causality from both randomized experiments and observational data.

This discussion of methodological research is divided into three areas: design, representation, and analysis. The efficient design of investigations must take place before data are collected because it involves how much, what kind of, and how data are to be collected. What type of study is feasible: experimental, sample survey, field observation, or other? What variables should be measured, controlled, and randomized? How extensive a subject pool or observational period is appropriate? How can study resources be allocated most effectively among various sites, instruments, and subsamples?

The construction of useful representations of the data involves deciding what kind of formal structure best expresses the underlying qualitative and quantitative concepts that are being used in a given study. For example, cost of living is a simple concept to quantify if it applies to a single individual with unchanging tastes in stable markets (that is, markets offering the same array of goods from year to year at varying prices), but as a national aggregate for millions of households and constantly changing consumer product markets, the cost of living is not easy to specify clearly or measure reliably. Statisticians, economists, sociologists, and other experts have long struggled to make the cost of living a precise yet practicable concept that is also efficient to measure, and they must continually modify it to reflect changing circumstances.

Data analysis covers the final step of characterizing and interpreting research findings: Can estimates of the relations between variables be made? Can some conclusion be drawn about correlation, cause and effect, or trends over time? How uncertain are the estimates and conclusions and can that uncertainty be reduced by analyzing the data in a different way? Can computers be used to display complex results graphically for quicker or better understanding or to suggest different ways of proceeding?

Advances in analysis, data representation, and research design feed into and reinforce one another in the course of actual scientific work. The intersections between methodological improvements and empirical advances are an important aspect of the multidisciplinary thrust of progress in the behavioral and social sciences.

  • Designs for Data Collection

Four broad kinds of research designs are used in the behavioral and social sciences: experimental, survey, comparative, and ethnographic.

Experimental designs, in either the laboratory or field settings, systematically manipulate a few variables while others that may affect the outcome are held constant, randomized, or otherwise controlled. The purpose of randomized experiments is to ensure that only one or a few variables can systematically affect the results, so that causes can be attributed. Survey designs include the collection and analysis of data from censuses, sample surveys, and longitudinal studies and the examination of various relationships among the observed phenomena. Randomization plays a different role here than in experimental designs: it is used to select members of a sample so that the sample is as representative of the whole population as possible. Comparative designs involve the retrieval of evidence that is recorded in the flow of current or past events in different times or places and the interpretation and analysis of this evidence. Ethnographic designs, also known as participant-observation designs, involve a researcher in intensive and direct contact with a group, community, or population being studied, through participation, observation, and extended interviewing.

Experimental Designs

Laboratory experiments.

Laboratory experiments underlie most of the work reported in Chapter 1 , significant parts of Chapter 2 , and some of the newest lines of research in Chapter 3 . Laboratory experiments extend and adapt classical methods of design first developed, for the most part, in the physical and life sciences and agricultural research. Their main feature is the systematic and independent manipulation of a few variables and the strict control or randomization of all other variables that might affect the phenomenon under study. For example, some studies of animal motivation involve the systematic manipulation of amounts of food and feeding schedules while other factors that may also affect motivation, such as body weight, deprivation, and so on, are held constant. New designs are currently coming into play largely because of new analytic and computational methods (discussed below, in “Advances in Statistical Inference and Analysis”).

Two examples of empirically important issues that demonstrate the need for broadening classical experimental approaches are open-ended responses and lack of independence of successive experimental trials. The first concerns the design of research protocols that do not require the strict segregation of the events of an experiment into well-defined trials, but permit a subject to respond at will. These methods are needed when what is of interest is how the respondent chooses to allocate behavior in real time and across continuously available alternatives. Such empirical methods have long been used, but they can generate very subtle and difficult problems in experimental design and subsequent analysis. As theories of allocative behavior of all sorts become more sophisticated and precise, the experimental requirements become more demanding, so the need to better understand and solve this range of design issues is an outstanding challenge to methodological ingenuity.

The second issue arises in repeated-trial designs when the behavior on successive trials, even if it does not exhibit a secular trend (such as a learning curve), is markedly influenced by what has happened in the preceding trial or trials. The more naturalistic the experiment and the more sensitive the meas urements taken, the more likely it is that such effects will occur. But such sequential dependencies in observations cause a number of important conceptual and technical problems in summarizing the data and in testing analytical models, which are not yet completely understood. In the absence of clear solutions, such effects are sometimes ignored by investigators, simplifying the data analysis but leaving residues of skepticism about the reliability and significance of the experimental results. With continuing development of sensitive measures in repeated-trial designs, there is a growing need for more advanced concepts and methods for dealing with experimental results that may be influenced by sequential dependencies.

Randomized Field Experiments

The state of the art in randomized field experiments, in which different policies or procedures are tested in controlled trials under real conditions, has advanced dramatically over the past two decades. Problems that were once considered major methodological obstacles—such as implementing randomized field assignment to treatment and control groups and protecting the randomization procedure from corruption—have been largely overcome. While state-of-the-art standards are not achieved in every field experiment, the commitment to reaching them is rising steadily, not only among researchers but also among customer agencies and sponsors.

The health insurance experiment described in Chapter 2 is an example of a major randomized field experiment that has had and will continue to have important policy reverberations in the design of health care financing. Field experiments with the negative income tax (guaranteed minimum income) conducted in the 1970s were significant in policy debates, even before their completion, and provided the most solid evidence available on how tax-based income support programs and marginal tax rates can affect the work incentives and family structures of the poor. Important field experiments have also been carried out on alternative strategies for the prevention of delinquency and other criminal behavior, reform of court procedures, rehabilitative programs in mental health, family planning, and special educational programs, among other areas.

In planning field experiments, much hinges on the definition and design of the experimental cells, the particular combinations needed of treatment and control conditions for each set of demographic or other client sample characteristics, including specification of the minimum number of cases needed in each cell to test for the presence of effects. Considerations of statistical power, client availability, and the theoretical structure of the inquiry enter into such specifications. Current important methodological thresholds are to find better ways of predicting recruitment and attrition patterns in the sample, of designing experiments that will be statistically robust in the face of problematic sample recruitment or excessive attrition, and of ensuring appropriate acquisition and analysis of data on the attrition component of the sample.

Also of major significance are improvements in integrating detailed process and outcome measurements in field experiments. To conduct research on program effects under field conditions requires continual monitoring to determine exactly what is being done—the process—how it corresponds to what was projected at the outset. Relatively unintrusive, inexpensive, and effective implementation measures are of great interest. There is, in parallel, a growing emphasis on designing experiments to evaluate distinct program components in contrast to summary measures of net program effects.

Finally, there is an important opportunity now for further theoretical work to model organizational processes in social settings and to design and select outcome variables that, in the relatively short time of most field experiments, can predict longer-term effects: For example, in job-training programs, what are the effects on the community (role models, morale, referral networks) or on individual skills, motives, or knowledge levels that are likely to translate into sustained changes in career paths and income levels?

Survey Designs

Many people have opinions about how societal mores, economic conditions, and social programs shape lives and encourage or discourage various kinds of behavior. People generalize from their own cases, and from the groups to which they belong, about such matters as how much it costs to raise a child, the extent to which unemployment contributes to divorce, and so on. In fact, however, effects vary so much from one group to another that homespun generalizations are of little use. Fortunately, behavioral and social scientists have been able to bridge the gaps between personal perspectives and collective realities by means of survey research. In particular, governmental information systems include volumes of extremely valuable survey data, and the facility of modern computers to store, disseminate, and analyze such data has significantly improved empirical tests and led to new understandings of social processes.

Within this category of research designs, two major types are distinguished: repeated cross-sectional surveys and longitudinal panel surveys. In addition, and cross-cutting these types, there is a major effort under way to improve and refine the quality of survey data by investigating features of human memory and of question formation that affect survey response.

Repeated cross-sectional designs can either attempt to measure an entire population—as does the oldest U.S. example, the national decennial census—or they can rest on samples drawn from a population. The general principle is to take independent samples at two or more times, measuring the variables of interest, such as income levels, housing plans, or opinions about public affairs, in the same way. The General Social Survey, collected by the National Opinion Research Center with National Science Foundation support, is a repeated cross sectional data base that was begun in 1972. One methodological question of particular salience in such data is how to adjust for nonresponses and “don’t know” responses. Another is how to deal with self-selection bias. For example, to compare the earnings of women and men in the labor force, it would be mistaken to first assume that the two samples of labor-force participants are randomly selected from the larger populations of men and women; instead, one has to consider and incorporate in the analysis the factors that determine who is in the labor force.

In longitudinal panels, a sample is drawn at one point in time and the relevant variables are measured at this and subsequent times for the same people. In more complex versions, some fraction of each panel may be replaced or added to periodically, such as expanding the sample to include households formed by the children of the original sample. An example of panel data developed in this way is the Panel Study of Income Dynamics (PSID), conducted by the University of Michigan since 1968 (discussed in Chapter 3 ).

Comparing the fertility or income of different people in different circumstances at the same time to find correlations always leaves a large proportion of the variability unexplained, but common sense suggests that much of the unexplained variability is actually explicable. There are systematic reasons for individual outcomes in each person’s past achievements, in parental models, upbringing, and earlier sequences of experiences. Unfortunately, asking people about the past is not particularly helpful: people remake their views of the past to rationalize the present and so retrospective data are often of uncertain validity. In contrast, generation-long longitudinal data allow readings on the sequence of past circumstances uncolored by later outcomes. Such data are uniquely useful for studying the causes and consequences of naturally occurring decisions and transitions. Thus, as longitudinal studies continue, quantitative analysis is becoming feasible about such questions as: How are the decisions of individuals affected by parental experience? Which aspects of early decisions constrain later opportunities? And how does detailed background experience leave its imprint? Studies like the two-decade-long PSID are bringing within grasp a complete generational cycle of detailed data on fertility, work life, household structure, and income.

Advances in Longitudinal Designs

Large-scale longitudinal data collection projects are uniquely valuable as vehicles for testing and improving survey research methodology. In ways that lie beyond the scope of a cross-sectional survey, longitudinal studies can sometimes be designed—without significant detriment to their substantive interests—to facilitate the evaluation and upgrading of data quality; the analysis of relative costs and effectiveness of alternative techniques of inquiry; and the standardization or coordination of solutions to problems of method, concept, and measurement across different research domains.

Some areas of methodological improvement include discoveries about the impact of interview mode on response (mail, telephone, face-to-face); the effects of nonresponse on the representativeness of a sample (due to respondents’ refusal or interviewers’ failure to contact); the effects on behavior of continued participation over time in a sample survey; the value of alternative methods of adjusting for nonresponse and incomplete observations (such as imputation of missing data, variable case weighting); the impact on response of specifying different recall periods, varying the intervals between interviews, or changing the length of interviews; and the comparison and calibration of results obtained by longitudinal surveys, randomized field experiments, laboratory studies, onetime surveys, and administrative records.

It should be especially noted that incorporating improvements in methodology and data quality has been and will no doubt continue to be crucial to the growing success of longitudinal studies. Panel designs are intrinsically more vulnerable than other designs to statistical biases due to cumulative item non-response, sample attrition, time-in-sample effects, and error margins in repeated measures, all of which may produce exaggerated estimates of change. Over time, a panel that was initially representative may become much less representative of a population, not only because of attrition in the sample, but also because of changes in immigration patterns, age structure, and the like. Longitudinal studies are also subject to changes in scientific and societal contexts that may create uncontrolled drifts over time in the meaning of nominally stable questions or concepts as well as in the underlying behavior. Also, a natural tendency to expand over time the range of topics and thus the interview lengths, which increases the burdens on respondents, may lead to deterioration of data quality or relevance. Careful methodological research to understand and overcome these problems has been done, and continued work as a component of new longitudinal studies is certain to advance the overall state of the art.

Longitudinal studies are sometimes pressed for evidence they are not designed to produce: for example, in important public policy questions concerning the impact of government programs in such areas as health promotion, disease prevention, or criminal justice. By using research designs that combine field experiments (with randomized assignment to program and control conditions) and longitudinal surveys, one can capitalize on the strongest merits of each: the experimental component provides stronger evidence for casual statements that are critical for evaluating programs and for illuminating some fundamental theories; the longitudinal component helps in the estimation of long-term program effects and their attenuation. Coupling experiments to ongoing longitudinal studies is not often feasible, given the multiple constraints of not disrupting the survey, developing all the complicated arrangements that go into a large-scale field experiment, and having the populations of interest overlap in useful ways. Yet opportunities to join field experiments to surveys are of great importance. Coupled studies can produce vital knowledge about the empirical conditions under which the results of longitudinal surveys turn out to be similar to—or divergent from—those produced by randomized field experiments. A pattern of divergence and similarity has begun to emerge in coupled studies; additional cases are needed to understand why some naturally occurring social processes and longitudinal design features seem to approximate formal random allocation and others do not. The methodological implications of such new knowledge go well beyond program evaluation and survey research. These findings bear directly on the confidence scientists—and others—can have in conclusions from observational studies of complex behavioral and social processes, particularly ones that cannot be controlled or simulated within the confines of a laboratory environment.

Memory and the Framing of Questions

A very important opportunity to improve survey methods lies in the reduction of nonsampling error due to questionnaire context, phrasing of questions, and, generally, the semantic and social-psychological aspects of surveys. Survey data are particularly affected by the fallibility of human memory and the sensitivity of respondents to the framework in which a question is asked. This sensitivity is especially strong for certain types of attitudinal and opinion questions. Efforts are now being made to bring survey specialists into closer contact with researchers working on memory function, knowledge representation, and language in order to uncover and reduce this kind of error.

Memory for events is often inaccurate, biased toward what respondents believe to be true—or should be true—about the world. In many cases in which data are based on recollection, improvements can be achieved by shifting to techniques of structured interviewing and calibrated forms of memory elicitation, such as specifying recent, brief time periods (for example, in the last seven days) within which respondents recall certain types of events with acceptable accuracy.

  • “Taking things altogether, how would you describe your marriage? Would you say that your marriage is very happy, pretty happy, or not too happy?”
  • “Taken altogether how would you say things are these days—would you say you are very happy, pretty happy, or not too happy?”

Presenting this sequence in both directions on different forms showed that the order affected answers to the general happiness question but did not change the marital happiness question: responses to the specific issue swayed subsequent responses to the general one, but not vice versa. The explanations for and implications of such order effects on the many kinds of questions and sequences that can be used are not simple matters. Further experimentation on the design of survey instruments promises not only to improve the accuracy and reliability of survey research, but also to advance understanding of how people think about and evaluate their behavior from day to day.

Comparative Designs

Both experiments and surveys involve interventions or questions by the scientist, who then records and analyzes the responses. In contrast, many bodies of social and behavioral data of considerable value are originally derived from records or collections that have accumulated for various nonscientific reasons, quite often administrative in nature, in firms, churches, military organizations, and governments at all levels. Data of this kind can sometimes be subjected to careful scrutiny, summary, and inquiry by historians and social scientists, and statistical methods have increasingly been used to develop and evaluate inferences drawn from such data. Some of the main comparative approaches are cross-national aggregate comparisons, selective comparison of a limited number of cases, and historical case studies.

Among the more striking problems facing the scientist using such data are the vast differences in what has been recorded by different agencies whose behavior is being compared (this is especially true for parallel agencies in different nations), the highly unrepresentative or idiosyncratic sampling that can occur in the collection of such data, and the selective preservation and destruction of records. Means to overcome these problems form a substantial methodological research agenda in comparative research. An example of the method of cross-national aggregative comparisons is found in investigations by political scientists and sociologists of the factors that underlie differences in the vitality of institutions of political democracy in different societies. Some investigators have stressed the existence of a large middle class, others the level of education of a population, and still others the development of systems of mass communication. In cross-national aggregate comparisons, a large number of nations are arrayed according to some measures of political democracy and then attempts are made to ascertain the strength of correlations between these and the other variables. In this line of analysis it is possible to use a variety of statistical cluster and regression techniques to isolate and assess the possible impact of certain variables on the institutions under study. While this kind of research is cross-sectional in character, statements about historical processes are often invoked to explain the correlations.

More limited selective comparisons, applied by many of the classic theorists, involve asking similar kinds of questions but over a smaller range of societies. Why did democracy develop in such different ways in America, France, and England? Why did northeastern Europe develop rational bourgeois capitalism, in contrast to the Mediterranean and Asian nations? Modern scholars have turned their attention to explaining, for example, differences among types of fascism between the two World Wars, and similarities and differences among modern state welfare systems, using these comparisons to unravel the salient causes. The questions asked in these instances are inevitably historical ones.

Historical case studies involve only one nation or region, and so they may not be geographically comparative. However, insofar as they involve tracing the transformation of a society’s major institutions and the role of its main shaping events, they involve a comparison of different periods of a nation’s or a region’s history. The goal of such comparisons is to give a systematic account of the relevant differences. Sometimes, particularly with respect to the ancient societies, the historical record is very sparse, and the methods of history and archaeology mesh in the reconstruction of complex social arrangements and patterns of change on the basis of few fragments.

Like all research designs, comparative ones have distinctive vulnerabilities and advantages: One of the main advantages of using comparative designs is that they greatly expand the range of data, as well as the amount of variation in those data, for study. Consequently, they allow for more encompassing explanations and theories that can relate highly divergent outcomes to one another in the same framework. They also contribute to reducing any cultural biases or tendencies toward parochialism among scientists studying common human phenomena.

One main vulnerability in such designs arises from the problem of achieving comparability. Because comparative study involves studying societies and other units that are dissimilar from one another, the phenomena under study usually occur in very different contexts—so different that in some cases what is called an event in one society cannot really be regarded as the same type of event in another. For example, a vote in a Western democracy is different from a vote in an Eastern bloc country, and a voluntary vote in the United States means something different from a compulsory vote in Australia. These circumstances make for interpretive difficulties in comparing aggregate rates of voter turnout in different countries.

The problem of achieving comparability appears in historical analysis as well. For example, changes in laws and enforcement and recording procedures over time change the definition of what is and what is not a crime, and for that reason it is difficult to compare the crime rates over time. Comparative researchers struggle with this problem continually, working to fashion equivalent measures; some have suggested the use of different measures (voting, letters to the editor, street demonstration) in different societies for common variables (political participation), to try to take contextual factors into account and to achieve truer comparability.

A second vulnerability is controlling variation. Traditional experiments make conscious and elaborate efforts to control the variation of some factors and thereby assess the causal significance of others. In surveys as well as experiments, statistical methods are used to control sources of variation and assess suspected causal significance. In comparative and historical designs, this kind of control is often difficult to attain because the sources of variation are many and the number of cases few. Scientists have made efforts to approximate such control in these cases of “many variables, small N.” One is the method of paired comparisons. If an investigator isolates 15 American cities in which racial violence has been recurrent in the past 30 years, for example, it is helpful to match them with 15 cities of similar population size, geographical region, and size of minorities—such characteristics are controls—and then search for systematic differences between the two sets of cities. Another method is to select, for comparative purposes, a sample of societies that resemble one another in certain critical ways, such as size, common language, and common level of development, thus attempting to hold these factors roughly constant, and then seeking explanations among other factors in which the sampled societies differ from one another.

Ethnographic Designs

Traditionally identified with anthropology, ethnographic research designs are playing increasingly significant roles in most of the behavioral and social sciences. The core of this methodology is participant-observation, in which a researcher spends an extended period of time with the group under study, ideally mastering the local language, dialect, or special vocabulary, and participating in as many activities of the group as possible. This kind of participant-observation is normally coupled with extensive open-ended interviewing, in which people are asked to explain in depth the rules, norms, practices, and beliefs through which (from their point of view) they conduct their lives. A principal aim of ethnographic study is to discover the premises on which those rules, norms, practices, and beliefs are built.

The use of ethnographic designs by anthropologists has contributed significantly to the building of knowledge about social and cultural variation. And while these designs continue to center on certain long-standing features—extensive face-to-face experience in the community, linguistic competence, participation, and open-ended interviewing—there are newer trends in ethnographic work. One major trend concerns its scale. Ethnographic methods were originally developed largely for studying small-scale groupings known variously as village, folk, primitive, preliterate, or simple societies. Over the decades, these methods have increasingly been applied to the study of small groups and networks within modern (urban, industrial, complex) society, including the contemporary United States. The typical subjects of ethnographic study in modern society are small groups or relatively small social networks, such as outpatient clinics, medical schools, religious cults and churches, ethnically distinctive urban neighborhoods, corporate offices and factories, and government bureaus and legislatures.

As anthropologists moved into the study of modern societies, researchers in other disciplines—particularly sociology, psychology, and political science—began using ethnographic methods to enrich and focus their own insights and findings. At the same time, studies of large-scale structures and processes have been aided by the use of ethnographic methods, since most large-scale changes work their way into the fabric of community, neighborhood, and family, affecting the daily lives of people. Ethnographers have studied, for example, the impact of new industry and new forms of labor in “backward” regions; the impact of state-level birth control policies on ethnic groups; and the impact on residents in a region of building a dam or establishing a nuclear waste dump. Ethnographic methods have also been used to study a number of social processes that lend themselves to its particular techniques of observation and interview—processes such as the formation of class and racial identities, bureaucratic behavior, legislative coalitions and outcomes, and the formation and shifting of consumer tastes.

Advances in structured interviewing (see above) have proven especially powerful in the study of culture. Techniques for understanding kinship systems, concepts of disease, color terminologies, ethnobotany, and ethnozoology have been radically transformed and strengthened by coupling new interviewing methods with modem measurement and scaling techniques (see below). These techniques have made possible more precise comparisons among cultures and identification of the most competent and expert persons within a culture. The next step is to extend these methods to study the ways in which networks of propositions (such as boys like sports, girls like babies) are organized to form belief systems. Much evidence suggests that people typically represent the world around them by means of relatively complex cognitive models that involve interlocking propositions. The techniques of scaling have been used to develop models of how people categorize objects, and they have great potential for further development, to analyze data pertaining to cultural propositions.

Ideological Systems

Perhaps the most fruitful area for the application of ethnographic methods in recent years has been the systematic study of ideologies in modern society. Earlier studies of ideology were in small-scale societies that were rather homogeneous. In these studies researchers could report on a single culture, a uniform system of beliefs and values for the society as a whole. Modern societies are much more diverse both in origins and number of subcultures, related to different regions, communities, occupations, or ethnic groups. Yet these subcultures and ideologies share certain underlying assumptions or at least must find some accommodation with the dominant value and belief systems in the society.

The challenge is to incorporate this greater complexity of structure and process into systematic descriptions and interpretations. One line of work carried out by researchers has tried to track the ways in which ideologies are created, transmitted, and shared among large populations that have traditionally lacked the social mobility and communications technologies of the West. This work has concentrated on large-scale civilizations such as China, India, and Central America. Gradually, the focus has generalized into a concern with the relationship between the great traditions—the central lines of cosmopolitan Confucian, Hindu, or Mayan culture, including aesthetic standards, irrigation technologies, medical systems, cosmologies and calendars, legal codes, poetic genres, and religious doctrines and rites—and the little traditions, those identified with rural, peasant communities. How are the ideological doctrines and cultural values of the urban elites, the great traditions, transmitted to local communities? How are the little traditions, the ideas from the more isolated, less literate, and politically weaker groups in society, transmitted to the elites?

India and southern Asia have been fruitful areas for ethnographic research on these questions. The great Hindu tradition was present in virtually all local contexts through the presence of high-caste individuals in every community. It operated as a pervasive standard of value for all members of society, even in the face of strong little traditions. The situation is surprisingly akin to that of modern, industrialized societies. The central research questions are the degree and the nature of penetration of dominant ideology, even in groups that appear marginal and subordinate and have no strong interest in sharing the dominant value system. In this connection the lowest and poorest occupational caste—the untouchables—serves as an ultimate test of the power of ideology and cultural beliefs to unify complex hierarchical social systems.

Historical Reconstruction

Another current trend in ethnographic methods is its convergence with archival methods. One joining point is the application of descriptive and interpretative procedures used by ethnographers to reconstruct the cultures that created historical documents, diaries, and other records, to interview history, so to speak. For example, a revealing study showed how the Inquisition in the Italian countryside between the 1570s and 1640s gradually worked subtle changes in an ancient fertility cult in peasant communities; the peasant beliefs and rituals assimilated many elements of witchcraft after learning them from their persecutors. A good deal of social history—particularly that of the family—has drawn on discoveries made in the ethnographic study of primitive societies. As described in Chapter 4 , this particular line of inquiry rests on a marriage of ethnographic, archival, and demographic approaches.

Other lines of ethnographic work have focused on the historical dimensions of nonliterate societies. A strikingly successful example in this kind of effort is a study of head-hunting. By combining an interpretation of local oral tradition with the fragmentary observations that were made by outside observers (such as missionaries, traders, colonial officials), historical fluctuations in the rate and significance of head-hunting were shown to be partly in response to such international forces as the great depression and World War II. Researchers are also investigating the ways in which various groups in contemporary societies invent versions of traditions that may or may not reflect the actual history of the group. This process has been observed among elites seeking political and cultural legitimation and among hard-pressed minorities (for example, the Basque in Spain, the Welsh in Great Britain) seeking roots and political mobilization in a larger society.

Ethnography is a powerful method to record, describe, and interpret the system of meanings held by groups and to discover how those meanings affect the lives of group members. It is a method well adapted to the study of situations in which people interact with one another and the researcher can interact with them as well, so that information about meanings can be evoked and observed. Ethnography is especially suited to exploration and elucidation of unsuspected connections; ideally, it is used in combination with other methods—experimental, survey, or comparative—to establish with precision the relative strengths and weaknesses of such connections. By the same token, experimental, survey, and comparative methods frequently yield connections, the meaning of which is unknown; ethnographic methods are a valuable way to determine them.

  • Models for Representing Phenomena

The objective of any science is to uncover the structure and dynamics of the phenomena that are its subject, as they are exhibited in the data. Scientists continuously try to describe possible structures and ask whether the data can, with allowance for errors of measurement, be described adequately in terms of them. Over a long time, various families of structures have recurred throughout many fields of science; these structures have become objects of study in their own right, principally by statisticians, other methodological specialists, applied mathematicians, and philosophers of logic and science. Methods have evolved to evaluate the adequacy of particular structures to account for particular types of data. In the interest of clarity we discuss these structures in this section and the analytical methods used for estimation and evaluation of them in the next section, although in practice they are closely intertwined.

A good deal of mathematical and statistical modeling attempts to describe the relations, both structural and dynamic, that hold among variables that are presumed to be representable by numbers. Such models are applicable in the behavioral and social sciences only to the extent that appropriate numerical measurement can be devised for the relevant variables. In many studies the phenomena in question and the raw data obtained are not intrinsically numerical, but qualitative, such as ethnic group identifications. The identifying numbers used to code such questionnaire categories for computers are no more than labels, which could just as well be letters or colors. One key question is whether there is some natural way to move from the qualitative aspects of such data to a structural representation that involves one of the well-understood numerical or geometric models or whether such an attempt would be inherently inappropriate for the data in question. The decision as to whether or not particular empirical data can be represented in particular numerical or more complex structures is seldom simple, and strong intuitive biases or a priori assumptions about what can and cannot be done may be misleading.

Recent decades have seen rapid and extensive development and application of analytical methods attuned to the nature and complexity of social science data. Examples of nonnumerical modeling are increasing. Moreover, the widespread availability of powerful computers is probably leading to a qualitative revolution, it is affecting not only the ability to compute numerical solutions to numerical models, but also to work out the consequences of all sorts of structures that do not involve numbers at all. The following discussion gives some indication of the richness of past progress and of future prospects although it is by necessity far from exhaustive.

In describing some of the areas of new and continuing research, we have organized this section on the basis of whether the representations are fundamentally probabilistic or not. A further useful distinction is between representations of data that are highly discrete or categorical in nature (such as whether a person is male or female) and those that are continuous in nature (such as a person’s height). Of course, there are intermediate cases involving both types of variables, such as color stimuli that are characterized by discrete hues (red, green) and a continuous luminance measure. Probabilistic models lead very naturally to questions of estimation and statistical evaluation of the correspondence between data and model. Those that are not probabilistic involve additional problems of dealing with and representing sources of variability that are not explicitly modeled. At the present time, scientists understand some aspects of structure, such as geometries, and some aspects of randomness, as embodied in probability models, but do not yet adequately understand how to put the two together in a single unified model. Table 5-1 outlines the way we have organized this discussion and shows where the examples in this section lie.

Table 5-1. A Classification of Structural Models.

A Classification of Structural Models.

Probability Models

Some behavioral and social sciences variables appear to be more or less continuous, for example, utility of goods, loudness of sounds, or risk associated with uncertain alternatives. Many other variables, however, are inherently categorical, often with only two or a few values possible: for example, whether a person is in or out of school, employed or not employed, identifies with a major political party or political ideology. And some variables, such as moral attitudes, are typically measured in research with survey questions that allow only categorical responses. Much of the early probability theory was formulated only for continuous variables; its use with categorical variables was not really justified, and in some cases it may have been misleading. Recently, very significant advances have been made in how to deal explicitly with categorical variables. This section first describes several contemporary approaches to models involving categorical variables, followed by ones involving continuous representations.

Log-Linear Models for Categorical Variables

Many recent models for analyzing categorical data of the kind usually displayed as counts (cell frequencies) in multidimensional contingency tables are subsumed under the general heading of log-linear models, that is, linear models in the natural logarithms of the expected counts in each cell in the table. These recently developed forms of statistical analysis allow one to partition variability due to various sources in the distribution of categorical attributes, and to isolate the effects of particular variables or combinations of them.

Present log-linear models were first developed and used by statisticians and sociologists and then found extensive application in other social and behavioral sciences disciplines. When applied, for instance, to the analysis of social mobility, such models separate factors of occupational supply and demand from other factors that impede or propel movement up and down the social hierarchy. With such models, for example, researchers discovered the surprising fact that occupational mobility patterns are strikingly similar in many nations of the world (even among disparate nations like the United States and most of the Eastern European socialist countries), and from one time period to another, once allowance is made for differences in the distributions of occupations. The log-linear and related kinds of models have also made it possible to identify and analyze systematic differences in mobility among nations and across time. As another example of applications, psychologists and others have used log-linear models to analyze attitudes and their determinants and to link attitudes to behavior. These methods have also diffused to and been used extensively in the medical and biological sciences.

Regression Models for Categorical Variables

Models that permit one variable to be explained or predicted by means of others, called regression models, are the workhorses of much applied statistics; this is especially true when the dependent (explained) variable is continuous. For a two-valued dependent variable, such as alive or dead, models and approximate theory and computational methods for one explanatory variable were developed in biometry about 50 years ago. Computer programs able to handle many explanatory variables, continuous or categorical, are readily available today. Even now, however, the accuracy of the approximate theory on given data is an open question.

Using classical utility theory, economists have developed discrete choice models that turn out to be somewhat related to the log-linear and categorical regression models. Models for limited dependent variables, especially those that cannot take on values above or below a certain level (such as weeks unemployed, number of children, and years of schooling) have been used profitably in economics and in some other areas. For example, censored normal variables (called tobits in economics), in which observed values outside certain limits are simply counted, have been used in studying decisions to go on in school. It will require further research and development to incorporate information about limited ranges of variables fully into the main multivariate methodologies. In addition, with respect to the assumptions about distribution and functional form conventionally made in discrete response models, some new methods are now being developed that show promise of yielding reliable inferences without making unrealistic assumptions; further research in this area promises significant progress.

One problem arises from the fact that many of the categorical variables collected by the major data bases are ordered. For example, attitude surveys frequently use a 3-, 5-, or 7-point scale (from high to low) without specifying numerical intervals between levels. Social class and educational levels are often described by ordered categories. Ignoring order information, which many traditional statistical methods do, may be inefficient or inappropriate, but replacing the categories by successive integers or other arbitrary scores may distort the results. (For additional approaches to this question, see sections below on ordered structures.) Regression-like analysis of ordinal categorical variables is quite well developed, but their multivariate analysis needs further research. New log-bilinear models have been proposed, but to date they deal specifically with only two or three categorical variables. Additional research extending the new models, improving computational algorithms, and integrating the models with work on scaling promise to lead to valuable new knowledge.

Models for Event Histories

Event-history studies yield the sequence of events that respondents to a survey sample experience over a period of time; for example, the timing of marriage, childbearing, or labor force participation. Event-history data can be used to study educational progress, demographic processes (migration, fertility, and mortality), mergers of firms, labor market behavior, and even riots, strikes, and revolutions. As interest in such data has grown, many researchers have turned to models that pertain to changes in probabilities over time to describe when and how individuals move among a set of qualitative states.

Much of the progress in models for event-history data builds on recent developments in statistics and biostatistics for life-time, failure-time, and hazard models. Such models permit the analysis of qualitative transitions in a population whose members are undergoing partially random organic deterioration, mechanical wear, or other risks over time. With the increased complexity of event-history data that are now being collected, and the extension of event-history data bases over very long periods of time, new problems arise that cannot be effectively handled by older types of analysis. Among the problems are repeated transitions, such as between unemployment and employment or marriage and divorce; more than one time variable (such as biological age, calendar time, duration in a stage, and time exposed to some specified condition); latent variables (variables that are explicitly modeled even though not observed); gaps in the data; sample attrition that is not randomly distributed over the categories; and respondent difficulties in recalling the exact timing of events.

Models for Multiple-Item Measurement

For a variety of reasons, researchers typically use multiple measures (or multiple indicators) to represent theoretical concepts. Sociologists, for example, often rely on two or more variables (such as occupation and education) to measure an individual’s socioeconomic position; educational psychologists ordinarily measure a student’s ability with multiple test items. Despite the fact that the basic observations are categorical, in a number of applications this is interpreted as a partitioning of something continuous. For example, in test theory one thinks of the measures of both item difficulty and respondent ability as continuous variables, possibly multidimensional in character.

Classical test theory and newer item-response theories in psychometrics deal with the extraction of information from multiple measures. Testing, which is a major source of data in education and other areas, results in millions of test items stored in archives each year for purposes ranging from college admissions to job-training programs for industry. One goal of research on such test data is to be able to make comparisons among persons or groups even when different test items are used. Although the information collected from each respondent is intentionally incomplete in order to keep the tests short and simple, item-response techniques permit researchers to reconstitute the fragments into an accurate picture of overall group proficiencies. These new methods provide a better theoretical handle on individual differences, and they are expected to be extremely important in developing and using tests. For example, they have been used in attempts to equate different forms of a test given in successive waves during a year, a procedure made necessary in large-scale testing programs by legislation requiring disclosure of test-scoring keys at the time results are given.

An example of the use of item-response theory in a significant research effort is the National Assessment of Educational Progress (NAEP). The goal of this project is to provide accurate, nationally representative information on the average (rather than individual) proficiency of American children in a wide variety of academic subjects as they progress through elementary and secondary school. This approach is an improvement over the use of trend data on university entrance exams, because NAEP estimates of academic achievements (by broad characteristics such as age, grade, region, ethnic background, and so on) are not distorted by the self-selected character of those students who seek admission to college, graduate, and professional programs.

Item-response theory also forms the basis of many new psychometric instruments, known as computerized adaptive testing, currently being implemented by the U.S. military services and under additional development in many testing organizations. In adaptive tests, a computer program selects items for each examinee based upon the examinee’s success with previous items. Generally, each person gets a slightly different set of items and the equivalence of scale scores is established by using item-response theory. Adaptive testing can greatly reduce the number of items needed to achieve a given level of measurement accuracy.

Nonlinear, Nonadditive Models

Virtually all statistical models now in use impose a linearity or additivity assumption of some kind, sometimes after a nonlinear transformation of variables. Imposing these forms on relationships that do not, in fact, possess them may well result in false descriptions and spurious effects. Unwary users, especially of computer software packages, can easily be misled. But more realistic nonlinear and nonadditive multivariate models are becoming available. Extensive use with empirical data is likely to force many changes and enhancements in such models and stimulate quite different approaches to nonlinear multivariate analysis in the next decade.

Geometric and Algebraic Models

Geometric and algebraic models attempt to describe underlying structural relations among variables. In some cases they are part of a probabilistic approach, such as the algebraic models underlying regression or the geometric representations of correlations between items in a technique called factor analysis. In other cases, geometric and algebraic models are developed without explicitly modeling the element of randomness or uncertainty that is always present in the data. Although this latter approach to behavioral and social sciences problems has been less researched than the probabilistic one, there are some advantages in developing the structural aspects independent of the statistical ones. We begin the discussion with some inherently geometric representations and then turn to numerical representations for ordered data.

Although geometry is a huge mathematical topic, little of it seems directly applicable to the kinds of data encountered in the behavioral and social sciences. A major reason is that the primitive concepts normally used in geometry—points, lines, coincidence—do not correspond naturally to the kinds of qualitative observations usually obtained in behavioral and social sciences contexts. Nevertheless, since geometric representations are used to reduce bodies of data, there is a real need to develop a deeper understanding of when such representations of social or psychological data make sense. Moreover, there is a practical need to understand why geometric computer algorithms, such as those of multidimensional scaling, work as well as they apparently do. A better understanding of the algorithms will increase the efficiency and appropriateness of their use, which becomes increasingly important with the widespread availability of scaling programs for microcomputers.

Over the past 50 years several kinds of well-understood scaling techniques have been developed and widely used to assist in the search for appropriate geometric representations of empirical data. The whole field of scaling is now entering a critical juncture in terms of unifying and synthesizing what earlier appeared to be disparate contributions. Within the past few years it has become apparent that several major methods of analysis, including some that are based on probabilistic assumptions, can be unified under the rubric of a single generalized mathematical structure. For example, it has recently been demonstrated that such diverse approaches as nonmetric multidimensional scaling, principal-components analysis, factor analysis, correspondence analysis, and log-linear analysis have more in common in terms of underlying mathematical structure than had earlier been realized.

Nonmetric multidimensional scaling is a method that begins with data about the ordering established by subjective similarity (or nearness) between pairs of stimuli. The idea is to embed the stimuli into a metric space (that is, a geometry with a measure of distance between points) in such a way that distances between points corresponding to stimuli exhibit the same ordering as do the data. This method has been successfully applied to phenomena that, on other grounds, are known to be describable in terms of a specific geometric structure; such applications were used to validate the procedures. Such validation was done, for example, with respect to the perception of colors, which are known to be describable in terms of a particular three-dimensional structure known as the Euclidean color coordinates. Similar applications have been made with Morse code symbols and spoken phonemes. The technique is now used in some biological and engineering applications, as well as in some of the social sciences, as a method of data exploration and simplification.

One question of interest is how to develop an axiomatic basis for various geometries using as a primitive concept an observable such as the subject’s ordering of the relative similarity of one pair of stimuli to another, which is the typical starting point of such scaling. The general task is to discover properties of the qualitative data sufficient to ensure that a mapping into the geometric structure exists and, ideally, to discover an algorithm for finding it. Some work of this general type has been carried out: for example, there is an elegant set of axioms based on laws of color matching that yields the three-dimensional vectorial representation of color space. But the more general problem of understanding the conditions under which the multidimensional scaling algorithms are suitable remains unsolved. In addition, work is needed on understanding more general, non-Euclidean spatial models.

Ordered Factorial Systems

One type of structure common throughout the sciences arises when an ordered dependent variable is affected by two or more ordered independent variables. This is the situation to which regression and analysis-of-variance models are often applied; it is also the structure underlying the familiar physical identities, in which physical units are expressed as products of the powers of other units (for example, energy has the unit of mass times the square of the unit of distance divided by the square of the unit of time).

There are many examples of these types of structures in the behavioral and social sciences. One example is the ordering of preference of commodity bundles—collections of various amounts of commodities—which may be revealed directly by expressions of preference or indirectly by choices among alternative sets of bundles. A related example is preferences among alternative courses of action that involve various outcomes with differing degrees of uncertainty; this is one of the more thoroughly investigated problems because of its potential importance in decision making. A psychological example is the trade-off between delay and amount of reward, yielding those combinations that are equally reinforcing. In a common, applied kind of problem, a subject is given descriptions of people in terms of several factors, for example, intelligence, creativity, diligence, and honesty, and is asked to rate them according to a criterion such as suitability for a particular job.

In all these cases and a myriad of others like them the question is whether the regularities of the data permit a numerical representation. Initially, three types of representations were studied quite fully: the dependent variable as a sum, a product, or a weighted average of the measures associated with the independent variables. The first two representations underlie some psychological and economic investigations, as well as a considerable portion of physical measurement and modeling in classical statistics. The third representation, averaging, has proved most useful in understanding preferences among uncertain outcomes and the amalgamation of verbally described traits, as well as some physical variables.

For each of these three cases—adding, multiplying, and averaging—researchers know what properties or axioms of order the data must satisfy for such a numerical representation to be appropriate. On the assumption that one or another of these representations exists, and using numerical ratings by subjects instead of ordering, a scaling technique called functional measurement (referring to the function that describes how the dependent variable relates to the independent ones) has been developed and applied in a number of domains. What remains problematic is how to encompass at the ordinal level the fact that some random error intrudes into nearly all observations and then to show how that randomness is represented at the numerical level; this continues to be an unresolved and challenging research issue.

During the past few years considerable progress has been made in understanding certain representations inherently different from those just discussed. The work has involved three related thrusts. The first is a scheme of classifying structures according to how uniquely their representation is constrained. The three classical numerical representations are known as ordinal, interval, and ratio scale types. For systems with continuous numerical representations and of scale type at least as rich as the ratio one, it has been shown that only one additional type can exist. A second thrust is to accept structural assumptions, like factorial ones, and to derive for each scale the possible functional relations among the independent variables. And the third thrust is to develop axioms for the properties of an order relation that leads to the possible representations. Much is now known about the possible nonadditive representations of both the multifactor case and the one where stimuli can be combined, such as combining sound intensities.

Closely related to this classification of structures is the question: What statements, formulated in terms of the measures arising in such representations, can be viewed as meaningful in the sense of corresponding to something empirical? Statements here refer to any scientific assertions, including statistical ones, formulated in terms of the measures of the variables and logical and mathematical connectives. These are statements for which asserting truth or falsity makes sense. In particular, statements that remain invariant under certain symmetries of structure have played an important role in classical geometry, dimensional analysis in physics, and in relating measurement and statistical models applied to the same phenomenon. In addition, these ideas have been used to construct models in more formally developed areas of the behavioral and social sciences, such as psychophysics. Current research has emphasized the communality of these historically independent developments and is attempting both to uncover systematic, philosophically sound arguments as to why invariance under symmetries is as important as it appears to be and to understand what to do when structures lack symmetry, as, for example, when variables have an inherent upper bound.

Many subjects do not seem to be correctly represented in terms of distances in continuous geometric space. Rather, in some cases, such as the relations among meanings of words—which is of great interest in the study of memory representations—a description in terms of tree-like, hierarchial structures appears to be more illuminating. This kind of description appears appropriate both because of the categorical nature of the judgments and the hierarchial, rather than trade-off, nature of the structure. Individual items are represented as the terminal nodes of the tree, and groupings by different degrees of similarity are shown as intermediate nodes, with the more general groupings occurring nearer the root of the tree. Clustering techniques, requiring considerable computational power, have been and are being developed. Some successful applications exist, but much more refinement is anticipated.

Network Models

Several other lines of advanced modeling have progressed in recent years, opening new possibilities for empirical specification and testing of a variety of theories. In social network data, relationships among units, rather than the units themselves, are the primary objects of study: friendships among persons, trade ties among nations, cocitation clusters among research scientists, interlocking among corporate boards of directors. Special models for social network data have been developed in the past decade, and they give, among other things, precise new measures of the strengths of relational ties among units. A major challenge in social network data at present is to handle the statistical dependence that arises when the units sampled are related in complex ways.

  • Statistical Inference and Analysis

As was noted earlier, questions of design, representation, and analysis are intimately intertwined. Some issues of inference and analysis have been discussed above as related to specific data collection and modeling approaches. This section discusses some more general issues of statistical inference and advances in several current approaches to them.

Causal Inference

Behavioral and social scientists use statistical methods primarily to infer the effects of treatments, interventions, or policy factors. Previous chapters included many instances of causal knowledge gained this way. As noted above, the large experimental study of alternative health care financing discussed in Chapter 2 relied heavily on statistical principles and techniques, including randomization, in the design of the experiment and the analysis of the resulting data. Sophisticated designs were necessary in order to answer a variety of questions in a single large study without confusing the effects of one program difference (such as prepayment or fee for service) with the effects of another (such as different levels of deductible costs), or with effects of unobserved variables (such as genetic differences). Statistical techniques were also used to ascertain which results applied across the whole enrolled population and which were confined to certain subgroups (such as individuals with high blood pressure) and to translate utilization rates across different programs and types of patients into comparable overall dollar costs and health outcomes for alternative financing options.

A classical experiment, with systematic but randomly assigned variation of the variables of interest (or some reasonable approach to this), is usually considered the most rigorous basis from which to draw such inferences. But random samples or randomized experimental manipulations are not always feasible or ethically acceptable. Then, causal inferences must be drawn from observational studies, which, however well designed, are less able to ensure that the observed (or inferred) relationships among variables provide clear evidence on the underlying mechanisms of cause and effect.

Certain recurrent challenges have been identified in studying causal inference. One challenge arises from the selection of background variables to be measured, such as the sex, nativity, or parental religion of individuals in a comparative study of how education affects occupational success. The adequacy of classical methods of matching groups in background variables and adjusting for covariates needs further investigation. Statistical adjustment of biases linked to measured background variables is possible, but it can become complicated. Current work in adjustment for selectivity bias is aimed at weakening implausible assumptions, such as normality, when carrying out these adjustments. Even after adjustment has been made for the measured background variables, other, unmeasured variables are almost always still affecting the results (such as family transfers of wealth or reading habits). Analyses of how the conclusions might change if such unmeasured variables could be taken into account is essential in attempting to make causal inferences from an observational study, and systematic work on useful statistical models for such sensitivity analyses is just beginning.

The third important issue arises from the necessity for distinguishing among competing hypotheses when the explanatory variables are measured with different degrees of precision. Both the estimated size and significance of an effect are diminished when it has large measurement error, and the coefficients of other correlated variables are affected even when the other variables are measured perfectly. Similar results arise from conceptual errors, when one measures only proxies for a theoretical construct (such as years of education to represent amount of learning). In some cases, there are procedures for simultaneously or iteratively estimating both the precision of complex measures and their effect on a particular criterion.

Although complex models are often necessary to infer causes, once their output is available, it should be translated into understandable displays for evaluation. Results that depend on the accuracy of a multivariate model and the associated software need to be subjected to appropriate checks, including the evaluation of graphical displays, group comparisons, and other analyses.

New Statistical Techniques

Internal resampling.

One of the great contributions of twentieth-century statistics was to demonstrate how a properly drawn sample of sufficient size, even if it is only a tiny fraction of the population of interest, can yield very good estimates of most population characteristics. When enough is known at the outset about the characteristic in question—for example, that its distribution is roughly normal—inference from the sample data to the population as a whole is straightforward, and one can easily compute measures of the certainty of inference, a common example being the 95 percent confidence interval around an estimate. But population shapes are sometimes unknown or uncertain, and so inference procedures cannot be so simple. Furthermore, more often than not, it is difficult to assess even the degree of uncertainty associated with complex data and with the statistics needed to unravel complex social and behavioral phenomena.

Internal resampling methods attempt to assess this uncertainty by generating a number of simulated data sets similar to the one actually observed. The definition of similar is crucial, and many methods that exploit different types of similarity have been devised. These methods provide researchers the freedom to choose scientifically appropriate procedures and to replace procedures that are valid under assumed distributional shapes with ones that are not so restricted. Flexible and imaginative computer simulation is the key to these methods. For a simple random sample, the “bootstrap” method repeatedly resamples the obtained data (with replacement) to generate a distribution of possible data sets. The distribution of any estimator can thereby be simulated and measures of the certainty of inference be derived. The “jackknife” method repeatedly omits a fraction of the data and in this way generates a distribution of possible data sets that can also be used to estimate variability. These methods can also be used to remove or reduce bias. For example, the ratio-estimator, a statistic that is commonly used in analyzing sample surveys and censuses, is known to be biased, and the jackknife method can usually remedy this defect. The methods have been extended to other situations and types of analysis, such as multiple regression.

There are indications that under relatively general conditions, these methods, and others related to them, allow more accurate estimates of the uncertainty of inferences than do the traditional ones that are based on assumed (usually, normal) distributions when that distributional assumption is unwarranted. For complex samples, such internal resampling or subsampling facilitates estimating the sampling variances of complex statistics.

An older and simpler, but equally important, idea is to use one independent subsample in searching the data to develop a model and at least one separate subsample for estimating and testing a selected model. Otherwise, it is next to impossible to make allowances for the excessively close fitting of the model that occurs as a result of the creative search for the exact characteristics of the sample data—characteristics that are to some degree random and will not predict well to other samples.

Robust Techniques

Many technical assumptions underlie the analysis of data. Some, like the assumption that each item in a sample is drawn independently of other items, can be weakened when the data are sufficiently structured to admit simple alternative models, such as serial correlation. Usually, these models require that a few parameters be estimated. Assumptions about shapes of distributions, normality being the most common, have proved to be particularly important, and considerable progress has been made in dealing with the consequences of different assumptions.

More recently, robust techniques have been designed that permit sharp, valid discriminations among possible values of parameters of central tendency for a wide variety of alternative distributions by reducing the weight given to occasional extreme deviations. It turns out that by giving up, say, 10 percent of the discrimination that could be provided under the rather unrealistic assumption of normality, one can greatly improve performance in more realistic situations, especially when unusually large deviations are relatively common.

These valuable modifications of classical statistical techniques have been extended to multiple regression, in which procedures of iterative reweighting can now offer relatively good performance for a variety of underlying distributional shapes. They should be extended to more general schemes of analysis.

In some contexts—notably the most classical uses of analysis of variance—the use of adequate robust techniques should help to bring conventional statistical practice closer to the best standards that experts can now achieve.

Many Interrelated Parameters

In trying to give a more accurate representation of the real world than is possible with simple models, researchers sometimes use models with many parameters, all of which must be estimated from the data. Classical principles of estimation, such as straightforward maximum-likelihood, do not yield reliable estimates unless either the number of observations is much larger than the number of parameters to be estimated or special designs are used in conjunction with strong assumptions. Bayesian methods do not draw a distinction between fixed and random parameters, and so may be especially appropriate for such problems.

A variety of statistical methods have recently been developed that can be interpreted as treating many of the parameters as or similar to random quantities, even if they are regarded as representing fixed quantities to be estimated. Theory and practice demonstrate that such methods can improve the simpler fixed-parameter methods from which they evolved, especially when the number of observations is not large relative to the number of parameters. Successful applications include college and graduate school admissions, where quality of previous school is treated as a random parameter when the data are insufficient to separately estimate it well. Efforts to create appropriate models using this general approach for small-area estimation and undercount adjustment in the census are important potential applications.

Missing Data

In data analysis, serious problems can arise when certain kinds of (quantitative or qualitative) information is partially or wholly missing. Various approaches to dealing with these problems have been or are being developed. One of the methods developed recently for dealing with certain aspects of missing data is called multiple imputation: each missing value in a data set is replaced by several values representing a range of possibilities, with statistical dependence among missing values reflected by linkage among their replacements. It is currently being used to handle a major problem of incompatibility between the 1980 and previous Bureau of Census public-use tapes with respect to occupation codes. The extension of these techniques to address such problems as nonresponse to income questions in the Current Population Survey has been examined in exploratory applications with great promise.

Computer Packages and Expert Systems

The development of high-speed computing and data handling has fundamentally changed statistical analysis. Methodologies for all kinds of situations are rapidly being developed and made available for use in computer packages that may be incorporated into interactive expert systems. This computing capability offers the hope that much data analyses will be more carefully and more effectively done than previously and that better strategies for data analysis will move from the practice of expert statisticians, some of whom may not have tried to articulate their own strategies, to both wide discussion and general use.

But powerful tools can be hazardous, as witnessed by occasional dire misuses of existing statistical packages. Until recently the only strategies available were to train more expert methodologists or to train substantive scientists in more methodology, but without the updating of their training it tends to become outmoded. Now there is the opportunity to capture in expert systems the current best methodological advice and practice. If that opportunity is exploited, standard methodological training of social scientists will shift to emphasizing strategies in using good expert systems—including understanding the nature and importance of the comments it provides—rather than in how to patch together something on one’s own. With expert systems, almost all behavioral and social scientists should become able to conduct any of the more common styles of data analysis more effectively and with more confidence than all but the most expert do today. However, the difficulties in developing expert systems that work as hoped for should not be underestimated. Human experts cannot readily explicate all of the complex cognitive network that constitutes an important part of their knowledge. As a result, the first attempts at expert systems were not especially successful (as discussed in Chapter 1 ). Additional work is expected to overcome these limitations, but it is not clear how long it will take.

Exploratory Analysis and Graphic Presentation

The formal focus of much statistics research in the middle half of the twentieth century was on procedures to confirm or reject precise, a priori hypotheses developed in advance of collecting data—that is, procedures to determine statistical significance. There was relatively little systematic work on realistically rich strategies for the applied researcher to use when attacking real-world problems with their multiplicity of objectives and sources of evidence. More recently, a species of quantitative detective work, called exploratory data analysis, has received increasing attention. In this approach, the researcher seeks out possible quantitative relations that may be present in the data. The techniques are flexible and include an important component of graphic representations. While current techniques have evolved for single responses in situations of modest complexity, extensions to multiple responses and to single responses in more complex situations are now possible.

Graphic and tabular presentation is a research domain in active renaissance, stemming in part from suggestions for new kinds of graphics made possible by computer capabilities, for example, hanging histograms and easily assimilated representations of numerical vectors. Research on data presentation has been carried out by statisticians, psychologists, cartographers, and other specialists, and attempts are now being made to incorporate findings and concepts from linguistics, industrial and publishing design, aesthetics, and classification studies in library science. Another influence has been the rapidly increasing availability of powerful computational hardware and software, now available even on desktop computers. These ideas and capabilities are leading to an increasing number of behavioral experiments with substantial statistical input. Nonetheless, criteria of good graphic and tabular practice are still too much matters of tradition and dogma, without adequate empirical evidence or theoretical coherence. To broaden the respective research outlooks and vigorously develop such evidence and coherence, extended collaborations between statistical and mathematical specialists and other scientists are needed, a major objective being to understand better the visual and cognitive processes (see Chapter 1 ) relevant to effective use of graphic or tabular approaches.

Combining Evidence

Combining evidence from separate sources is a recurrent scientific task, and formal statistical methods for doing so go back 30 years or more. These methods include the theory and practice of combining tests of individual hypotheses, sequential design and analysis of experiments, comparisons of laboratories, and Bayesian and likelihood paradigms.

There is now growing interest in more ambitious analytical syntheses, which are often called meta-analyses. One stimulus has been the appearance of syntheses explicitly combining all existing investigations in particular fields, such as prison parole policy, classroom size in primary schools, cooperative studies of therapeutic treatments for coronary heart disease, early childhood education interventions, and weather modification experiments. In such fields, a serious approach to even the simplest question—how to put together separate estimates of effect size from separate investigations—leads quickly to difficult and interesting issues. One issue involves the lack of independence among the available studies, due, for example, to the effect of influential teachers on the research projects of their students. Another issue is selection bias, because only some of the studies carried out, usually those with “significant” findings, are available and because the literature search may not find out all relevant studies that are available. In addition, experts agree, although informally, that the quality of studies from different laboratories and facilities differ appreciably and that such information probably should be taken into account. Inevitably, the studies to be included used different designs and concepts and controlled or measured different variables, making it difficult to know how to combine them.

Rich, informal syntheses, allowing for individual appraisal, may be better than catch-all formal modeling, but the literature on formal meta-analytic models is growing and may be an important area of discovery in the next decade, relevant both to statistical analysis per se and to improved syntheses in the behavioral and social and other sciences.

  • Opportunities and Needs

This chapter has cited a number of methodological topics associated with behavioral and social sciences research that appear to be particularly active and promising at the present time. As throughout the report, they constitute illustrative examples of what the committee believes to be important areas of research in the coming decade. In this section we describe recommendations for an additional $16 million annually to facilitate both the development of methodologically oriented research and, equally important, its communication throughout the research community.

Methodological studies, including early computer implementations, have for the most part been carried out by individual investigators with small teams of colleagues or students. Occasionally, such research has been associated with quite large substantive projects, and some of the current developments of computer packages, graphics, and expert systems clearly require large, organized efforts, which often lie at the boundary between grant-supported work and commercial development. As such research is often a key to understanding complex bodies of behavioral and social sciences data, it is vital to the health of these sciences that research support continue on methods relevant to problems of modeling, statistical analysis, representation, and related aspects of behavioral and social sciences data. Researchers and funding agencies should also be especially sympathetic to the inclusion of such basic methodological work in large experimental and longitudinal studies. Additional funding for work in this area, both in terms of individual research grants on methodological issues and in terms of augmentation of large projects to include additional methodological aspects, should be provided largely in the form of investigator-initiated project grants.

Ethnographic and comparative studies also typically rely on project grants to individuals and small groups of investigators. While this type of support should continue, provision should also be made to facilitate the execution of studies using these methods by research teams and to provide appropriate methodological training through the mechanisms outlined below.

Overall, we recommend an increase of $4 million in the level of investigator-initiated grant support for methodological work. An additional $1 million should be devoted to a program of centers for methodological research.

Many of the new methods and models described in the chapter, if and when adopted to any large extent, will demand substantially greater amounts of research devoted to appropriate analysis and computer implementation. New user interfaces and numerical algorithms will need to be designed and new computer programs written. And even when generally available methods (such as maximum-likelihood) are applicable, model application still requires skillful development in particular contexts. Many of the familiar general methods that are applied in the statistical analysis of data are known to provide good approximations when sample sizes are sufficiently large, but their accuracy varies with the specific model and data used. To estimate the accuracy requires extensive numerical exploration. Investigating the sensitivity of results to the assumptions of the models is important and requires still more creative, thoughtful research. It takes substantial efforts of these kinds to bring any new model on line, and the need becomes increasingly important and difficult as statistical models move toward greater realism, usefulness, complexity, and availability in computer form. More complexity in turn will increase the demand for computational power. Although most of this demand can be satisfied by increasingly powerful desktop computers, some access to mainframe and even supercomputers will be needed in selected cases. We recommend an additional $4 million annually to cover the growth in computational demands for model development and testing.

Interaction and cooperation between the developers and the users of statistical and mathematical methods need continual stimulation—both ways. Efforts should be made to teach new methods to a wider variety of potential users than is now the case. Several ways appear effective for methodologists to communicate to empirical scientists: running summer training programs for graduate students, faculty, and other researchers; encouraging graduate students, perhaps through degree requirements, to make greater use of the statistical, mathematical, and methodological resources at their own or affiliated universities; associating statistical and mathematical research specialists with large-scale data collection projects; and developing statistical packages that incorporate expert systems in applying the methods.

Methodologists, in turn, need to become more familiar with the problems actually faced by empirical scientists in the laboratory and especially in the field. Several ways appear useful for communication in this direction: encouraging graduate students in methodological specialties, perhaps through degree requirements, to work directly on empirical research; creating postdoctoral fellowships aimed at integrating such specialists into ongoing data collection projects; and providing for large data collection projects to engage relevant methodological specialists. In addition, research on and development of statistical packages and expert systems should be encouraged to involve the multidisciplinary collaboration of experts with experience in statistical, computer, and cognitive sciences.

A final point has to do with the promise held out by bringing different research methods to bear on the same problems. As our discussions of research methods in this and other chapters have emphasized, different methods have different powers and limitations, and each is designed especially to elucidate one or more particular facets of a subject. An important type of interdisciplinary work is the collaboration of specialists in different research methodologies on a substantive issue, examples of which have been noted throughout this report. If more such research were conducted cooperatively, the power of each method pursued separately would be increased. To encourage such multidisciplinary work, we recommend increased support for fellowships, research workshops, and training institutes.

Funding for fellowships, both pre-and postdoctoral, should be aimed at giving methodologists experience with substantive problems and at upgrading the methodological capabilities of substantive scientists. Such targeted fellowship support should be increased by $4 million annually, of which $3 million should be for predoctoral fellowships emphasizing the enrichment of methodological concentrations. The new support needed for research workshops is estimated to be $1 million annually. And new support needed for various kinds of advanced training institutes aimed at rapidly diffusing new methodological findings among substantive scientists is estimated to be $2 million annually.

  • Cite this Page National Research Council; Division of Behavioral and Social Sciences and Education; Commission on Behavioral and Social Sciences and Education; Committee on Basic Research in the Behavioral and Social Sciences; Gerstein DR, Luce RD, Smelser NJ, et al., editors. The Behavioral and Social Sciences: Achievements and Opportunities. Washington (DC): National Academies Press (US); 1988. 5, Methods of Data Collection, Representation, and Analysis.
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Data Analysis, Interpretation, and Presentation Techniques: A Guide to Making Sense of Your Research Data

by Prince Kumar

Last updated: 27 February 2023

Table of Contents

Data analysis, interpretation, and presentation are crucial aspects of conducting high-quality research. Data analysis involves processing and analyzing the data to derive meaningful insights, while data interpretation involves making sense of the insights and drawing conclusions. Data presentation involves presenting the data in a clear and concise way to communicate the research findings. In this article, we will discuss the techniques for data analysis, interpretation, and presentation.

1. Data Analysis Techniques

Data analysis techniques involve processing and analyzing the data to derive meaningful insights. The choice of data analysis technique depends on the research question and objectives. Some common data analysis techniques are:

a. Descriptive Statistics

Descriptive statistics involves summarizing and describing the data using measures such as mean, median, and standard deviation.

b. Inferential Statistics

Inferential statistics involves making inferences about the population based on the sample data. This technique involves hypothesis testing, confidence intervals, and regression analysis.

c. Content Analysis

Content analysis involves analyzing the text, images, or videos to identify patterns and themes.

d. Data Mining

Data mining involves using statistical and machine learning techniques to analyze large datasets and identify patterns.

2. Data Interpretation Techniques

Data interpretation involves making sense of the insights derived from the data analysis. The choice of data interpretation technique depends on the research question and objectives. Some common data interpretation techniques are:

a. Data Visualization

Data visualization involves presenting the data in a visual format, such as charts, graphs, or tables, to communicate the insights effectively.

b. Storytelling

Storytelling involves presenting the data in a narrative format, such as a story, to make the insights more relatable and memorable.

c. Comparative Analysis

Comparative analysis involves comparing the research findings with the existing literature or benchmarks to draw conclusions.

3. Data Presentation Techniques

Data presentation involves presenting the data in a clear and concise way to communicate the research findings. The choice of data presentation technique depends on the research question and objectives. Some common data presentation techniques are:

a. Tables and Graphs

Tables and graphs are effective data presentation techniques for presenting numerical data.

b. Infographics

Infographics are effective data presentation techniques for presenting complex data in a visual and easy-to-understand format.

c. Data Storytelling

Data storytelling involves presenting the data in a narrative format to communicate the research findings effectively.

In conclusion, data analysis, interpretation, and presentation are crucial aspects of conducting high-quality research. By using the appropriate data analysis, interpretation, and presentation techniques, researchers can derive meaningful insights, make sense of the insights, and communicate the research findings effectively. By conducting high-quality data analysis, interpretation, and presentation in research, researchers can provide valuable insights into the research question and objectives.

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Syllabus – Research Methodology

01 Introduction To Research Methodology

  • Meaning and objectives of Research
  • Types of Research
  • Research Approaches
  • Significance of Research
  • Research methods vs Methodology
  • Research Process
  • Criteria of Good Research
  • Problems faced by Researchers
  • Techniques Involved in defining a problem

02 Research Design

  • Meaning and Need for Research Design
  • Features and important concepts relating to research design
  • Different Research design
  • Important Experimental Designs

03 Sample Design

  • Introduction to Sample design
  • Censure and sample survey
  • Implications of Sample design
  • Steps in sampling design
  • Criteria for selecting a sampling procedure
  • Characteristics of a good sample design
  • Different types of Sample design
  • Measurement Scales
  • Important scaling Techniques

04 Methods of Data Collection

  • Introduction
  • Collection of Primary Data
  • Collection through Questionnaire and schedule collection of secondary data
  • Differences in Questionnaire and schedule
  • Different methods to collect secondary data

05 Data Analysis Interpretation and Presentation Techniques

  • Hypothesis Testing
  • Basic concepts concerning Hypothesis Testing
  • Procedure and flow diagram for Hypothesis Testing
  • Test of Significance
  • Chi-Square Analysis
  • Report Presentation Techniques
  • Open access
  • Published: 06 February 2024

Developing public health surveillance dashboards: a scoping review on the design principles

  • Reza Rabiei   ORCID: orcid.org/0000-0003-0771-7306 1 ,
  • Peivand Bastani   ORCID: orcid.org/0000-0002-0412-0267 2 ,
  • Hossein Ahmadi   ORCID: orcid.org/0000-0002-4437-3517 3 ,
  • Shirin Dehghan   ORCID: orcid.org/0000-0002-9616-9251 1 &
  • Sohrab Almasi   ORCID: orcid.org/0000-0003-0707-8399 1  

BMC Public Health volume  24 , Article number:  392 ( 2024 ) Cite this article

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Public Health Dashboards (PHDs) facilitate the monitoring and prediction of disease outbreaks by continuously monitoring the health status of the community. This study aimed to identify design principles and determinants for developing public health surveillance dashboards.

Methodology

This scoping review is based on Arksey and O'Malley's framework as included in JBI guidance. Four databases were used to review and present the proposed principles of designing PHDs: IEEE, PubMed, Web of Science, and Scopus. We considered articles published between January 1, 2010 and November 30, 2022. The final search of articles was done on November 30, 2022. Only articles in the English language were included. Qualitative synthesis and trend analysis were conducted.

Findings from sixty-seven articles out of 543 retrieved articles, which were eligible for analysis, indicate that most of the dashboards designed from 2020 onwards were at the national level for managing and monitoring COVID-19. Design principles for the public health dashboard were presented in five groups, i.e., considering aim and target users, appropriate content, interface, data analysis and presentation types, and infrastructure.

Effective and efficient use of dashboards in public health surveillance requires implementing design principles to improve the functionality of these systems in monitoring and decision-making. Considering user requirements, developing a robust infrastructure for improving data accessibility, developing, and applying Key Performance Indicators (KPIs) for data processing and reporting purposes, and designing interactive and intuitive interfaces are key for successful design and development.

Peer Review reports

Introduction

Public health surveillance is the continuous and systematic collection, analysis, and interpretation of health-related data essential for planning, implementing, and evaluating public health performance. It is a tool for estimating the health and behavior of a society, enabling the determination of health status and identification of interventions and their effects. Health monitoring empowers decision-makers for effective management based on valuable and up-to-date evidence [ 1 ]. Interpreting the obtained results and sharing the information helps the stakeholders take quick and appropriate measures to reduce morbidity and mortality and improve the welfare of society [ 2 ]. This process requires the cooperation of many stakeholders, from the community level to the senior management of the health system, who should work systematically and complementarily to promote public health security [ 3 ]. Public health goals include preventing epidemics, protecting against environmental hazards, encouraging and promoting healthy behaviors, managing natural disasters, assisting in community recovery, and ensuring quality and access to health services [ 4 ]. One of the essential services public health organizations provide is monitoring health status and identifying community health problems [ 4 , 5 ].

History of surveillance system and challenges

For this purpose, the public health monitoring system employs continuous monitoring systems to assess the health status of the community and utilizes the data for planning, implementation, and evaluation [ 4 ]. Initially, telephone reporting was used for public health monitoring. However, this method faced numerous challenges in analyzing and extracting valuable information for timely decision-making due to the production of large and complex data sets.

Evidence shows that the generation of substantial data amounts led to information overload at high organizational levels. Consequently, these data sets were rarely utilized for decision-making in practice in an effective way. The process of reporting, collecting, and analyzing the data often extended over several weeks, impeding a targeted and timely response [ 5 , 6 ].

With the advent and popularization of the Internet, a suitable platform was provided for the swift collection of society health-related data from a wide range of available electronic data sources. The first initiative of this kind was the Program for Monitoring Emerging Diseases (ProMED-Mail), launched in 1994 as the communication system of the Program for Monitoring Emerging Diseases. Subsequently, the World Health Organization (WHO) established an effectively organized infrastructure called the Global Outbreak Alert Response Network (GOARN) [ 7 ].

Today, public health monitoring systems can swiftly collect necessary data from different parts of society, including remote areas, to obtain essential information for identifying early events and preparing for them [ 7 , 8 ]. Studies demonstrate that, despite the clear advantages these systems offer compared to traditional surveillance systems, they still face unresolved limitations. The key limitation of other surveillance systems, in contrast to dashboards, is their inability to analyze and extract valuable information for timely decision-making and the lack of integration and collection of information from different sources. Given the large volume of data and the unstructured nature of data sources, methods are required to extract, process, and analyze the data, presenting the interpreted information most effectively to users [ 9 ].

Dashboard in public health surveillance

Considering the extensive data sources and the diversity of potential users in public health monitoring systems, dashboards can serve as a suitable tool to facilitate production and provide information to managers and policymakers in this field. In recent years, with the increase in the global spread of infectious diseases that have the potential to become epidemics and pandemics [ 10 , 11 ], the importance of utilizing Public Health Dashboards (PHDs) in continuously monitoring the health status of communities, timely diagnosis, and proper management of these diseases has significantly increased. The advent of the COVID-19 pandemic further emphasized the importance of using real-time data to manage and control this disease at the societal level, making the role of PHDs more prominent [ 12 , 13 ].

Dashboards serve as decision support tools, presenting essential business information in graphic and visual form. They can retrieve and analyze a large amount of data by interacting with various data sources, extracting information from databases, and delivering results based on Key Performance Indicators (KPIs). As a result, dashboard users can quickly gain insights into the current situation and progress of the business. When designing dashboards, it is necessary to choose KPIs that align with users' needs. Appropriate KPIs should be selected and organized based on the dashboard`s objectives and its users. The effectiveness of KPIs is maximized when the dashboard displays indicators that resonate with users' understanding and knowledge. Furthermore, the careful consideration of the number of selected KPIs for monitoring by the dashboard is essential [ 14 , 15 , 16 ].

The PHDs aim to facilitate the continuous monitoring of the health status of the community and the monitoring and prediction of disease outbreaks by collecting and integrating real-time data from various data sources. They assist in managing and controlling diseases by displaying KPIs in a well-designed user interface [ 17 ]. Therefore, considering the volume of data and the need for real-time monitoring and response in public health situations, attention to dashboard design principles for public health surveillance is essential [ 18 ].

Studies on PHDs principles primarily focus the content and user interface of these systems. The suggested design principles in these studies include a customizable, actionable "launch pad" [ 19 , 20 ], supporting correct data interpretation [ 20 , 21 ], information aggregation [ 22 ], minimalist aesthetics [ 21 ], user workload reduction [ 21 ], GIS interface [ 23 ], minimal cognitive processing, and the use of temporal trend analysis techniques [ 24 ]. In other words, the design principles suggested in the studies primarily focus on the content and user interface of these systems. Additionally, our study's results section highlights other features that should be considered in the design of public health dashboards.

This study was conducted to identify the design principles of PHDs not only focusing on the content and user interface aspects but also presenting a comprehensive view of all key design principles of PHDs. The aim is to provide insight for public health policymakers to facilitate and accelerate decision-making in epidemics and medical crises by extracting data from various systems and sources and providing timely reports.

Study design

Scoping reviews try to identify, retrieve, and summarize information from studies relevant to a particular topic to find key concepts. They are conducted to map the body of the literature on a topic area [ 25 ]. One of their advantages is determining the feasibility and necessity of conducting a systematic review in a specific domain [ 25 ]. Available knowledge indicates that the research question could be considered a dominant factor in designing a scoping review or a systematic one. With a research question addressing the feasibility, appropriateness, meaningfulness or effectiveness of a specific treatment or practice, systematic review is preferred. In contrast, when the authors aim to identify specific characteristics/concepts in the studies, mapping, reporting or discussing these characteristics/concepts, a scoping review is preferred [ 26 ].

Based on the present RQ, Arksey and O’Malley’s framework (2005), as an influential framework suggested by the JBI guidance, was applied to conduct this scoping review [ 25 ]. Six following stages are recommended based on this framework; the first five are compulsory for the robustness and trustworthiness of the review, while the last stage is indicated as an optional one.

Identifying the research question

The question should incorporate the population (or participants) /concept /context (PCC) elements per the guideline. This study included all the published papers about PHDs. The context refers to all the principles and determinants that impact designing such dashboards, and it also refers to applying PHDs in decision-making and monitoring the health status. Accordingly, the main research question is: “What are the key design principles of a public health dashboard?”.

Identification of relevant studies

Searches were conducted in PubMed, Web of Science, IEEE, and Scopus. A combination of MeSH terms and related keywords was used for the search strategy. The search strategy was carried out with the following keywords.

(("Surveillance"[Title/Abstract] OR "Public Health Surveillance"[Mesh] OR "public health"[Mesh] OR "public health"[Title/Abstract]) AND) dashboard [Title/Abstract] OR “Web-based surveillance system” [Title/Abstract])(

The search was carried out for articles published between January 1, 2010 and November 30, 2022. The final search of articles was conducted on November 30, 2022. EndNote version 20.2.1 was applied to manage the articles` inclusion and screening process.

Study selection

For this purpose, first, the retrieved articles were screened based on their title and abstract. Two authors reviewed all these titles and abstracts independently, and the senior author (RR) finalized the cases of disagreement. After the approval of the remaining articles by the senior author, the articles` full text was independently reviewed by two authors based on the inclusion and exclusion criteria of the study (Table  1 ). Any disagreement regarding the selection of articles was discussed with the senior author. Preferred Reporting Items for Systematic Reviews and Meta-Analyzes Extension for Scoping Review (PRISMA-ScR) guideline [ 27 ] was used to manage the eligible articles at this stage.

Charting data

The descriptive data extracted from the articles, including the year of publication, public health category, study setting, and dashboard implementation level, was inserted into Microsoft Excel Version 16 (Microsoft Corporation, Redmont, WA) for combination and analysis.

In this step, two data analysis methods, quantitative descriptive analysis and qualitative content analysis, were applied. Excel software (version 16) was used to summarize the distribution and frequency of the included articles based on year of publication, public health category, setting of the study, place of conducting the study, and dashboard implementation level (level of implementation of the dashboard at the global, national, or local levels). Then, the design principles of the PHDs were extracted by reviewing the content of the articles (Table  2 ).

For qualitative thematic analysis, the findings of the studies were examined line by line, and the primary codes were extracted for formulating the research question. After extracting the initial codes and reviewing these, the final codes were emerged and subsequently categorized to create subsidiary principles that ultimately led to a higher conceptual level.

Microsoft Packages Office 360 was used to categorize the design principles of dashboards. This scoping review also utilized trend analysis to illustrate the trends of publications in each of the public health categories. The number of articles published in different years was drawn using Microsoft Excel (Version 16).

A total of 543 articles were retrieved after searching the databases. The PRISMA flow diagram illustrates that 67 articles were eligible for analysis based on the inclusion and exclusion criteria after eliminating the duplications and screening the articles. (Fig.  1 ).

figure 1

Flow diagram of conducting searches, filtering and paper selection

Characteristics of included studies

The geographical distribution of the designed dashboards showed that most of the selected studies were conducted in North America ( N  = 29, 43%), Europe, Asia, and Africa, respectively (Fig.  2 ). About the studies conducted on PHDs, there was an increasing trend in the number of published articles from 2020 to 2022. Regarding implementation scale, the designed dashboards were mainly reported at the national level (58%) (Regional 27%, local 11%, and global 4%). In addition, ( N  = 23, 30%) of dashboards were designed to monitor and control for COVID-19; followed by dashboards developed for maternal and newborn health ( N  = 8, 12%) and AIDS ( N  = 6, 9%) (Fig.  3 ).

figure 2

Geographical distribution of studies

figure 3

A ) Public health category, B ) Number of articles published per year, C ) Level implementation of PHDs

Principles of designing PHDs

Considering the objective and target users.

First, the purpose of designing a dashboard and the target users should be considered. The dashboard's design, visualization tools, content, and how the information is represented vary based on the dashboard users. In the study of Véronique et al. in the Netherlands, to investigate the development and actionability of the COVID-19 dashboard, it is important to specify the purpose and users of the dashboard in designing the dashboard [ 28 ]. In a review of 158 dashboards from 53 countries, Ivankovi et al. identified seven common features among them. “Know their audience and information needs” is mentioned as the first feature in the principles of designing PHDs [ 29 ]. Therefore, the need for compatibility between the content and information displayed by the dashboard and the tasks and needs of users can impact the use of the dashboard [ 28 , 29 , 30 ].

Appropriate content

Véronique et al. [28), introduced content and data, and Ivanković et al. [ 29 ], presented managing the type, volume, and flow of displayed information, as public health dashboard design features.

In the reviewed dashboards, KPIs were placed on the dashboard's main page, allowing for timely monitoring and display of the current situation at a glance. KPIs' placement and display in the dashboard is top-down so that macro indicators (global, national) (for example, number of deaths due to COVID-19 global or by county) are placed on the main screen. KPIs and global indicators can be compared at this level. Mezzo (urban, regional) (for example number of deaths due to covid-19 at global or by region or cities) indicators are at the next level, which can compare cities and regions. Micro indicators (for example, the number of deaths due to COVID-19 at hospitals) are on the third level, which are performance indicators at the level of institutions. Managing the amount of information displayed on the dashboard is also essential [ 28 , 29 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ].

The dashboard user interface consists of two parts: interactive tools and visual tools.

Interactive tools

In the reviewed dashboards, the summary view feature was first used to monitor macro indicators at a glance, and unnecessary details were not displayed. This feature helps summarize data and reduce complexity. The indicators' details can be accessed using the drill-up and drill-down features if needed. The pan-and-zoom feature can be used to magnify or reduce the details. The customizable feature enables users to customize information display based on indicators according to their needs. If real-time monitoring is needed, the reports based on the determined KPIs are displayed in real-time [ 4 , 28 , 31 , 34 , 36 , 37 , 38 , 40 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ].

Visual tools

Using appropriate visualization techniques based on KPIs' nature and users' experience and skill will improve dashboard design. Choosing the correct type of visualization tool that matches the type and nature of KPIs is essential in designing dashboards. In the reviewed dashboards, different visual techniques (including data tables, pie charts, bars, histograms, lines, areas, scatter bubbles, and a series of multiple and interactive maps) were used based on the nature of the indicators [ 29 , 34 , 35 , 37 ]. In the study of Ivanković et al., by examining 158 PHDs, various types of visualization to display information in the dashboard include time trend analysis availability, use of time trend analysis, geographic levels (scales) of analysis, disaggregation options, use of narratives to interpret data [ 29 ].

In Lee et al. study, visual summarizations (e.g., heat map and time series chart) and interactive tools (e.g., year selection, automatic year play, map zoom, copy or print data, ranking data by name or value, and data search) were implemented to enhance user experience [ 37 ]. Correspondingly, data interpretation tools (For example, using color coding to indicate urgent/emergency alerts with red, normal situations with green, and warnings with yellow; minimizing distractions; avoiding unnecessary visual decorations in dashboard design) are essential in dashboard design [ 28 , 29 , 34 , 35 , 37 , 42 , 44 , 47 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ].

Considering the types of data analysis and presentation

Data analysis helps users understand the relationships between data and trends in the dashboard [ 29 , 34 ]. Various types of analysis were in the reviewed dashboards, including analysis at different geographic levels, comparing global and local KPIs, comparing indicators with standard values, and presenting data or reports in the format required by the users, such as Word or PDF [ 29 , 34 , 36 , 38 , 45 , 53 , 55 , 60 , 61 , 62 ]. In the study by Cheng et al., the features for efficient data presentation are suggested: (1) provision of information that viewers need quickly and clearly, (2) organization of information to support meaning and usability, (3) minimization of distractions, clichés, and unnecessary embellishments that could create confusion, (4) creation of an aesthetically pleasing viewing experience, and (5) consistency of design for easy data comparison [ 45 ]. Artificial intelligence and data mining techniques can be used to predict trends and patterns in data over time [ 53 , 55 , 60 , 61 , 62 ].

Infrastructure

The infrastructure and implementation of the data warehouse are vital in designing dashboards and facilitating the collection and management of data from different sources. Data warehouses are central repositories of integrated data from one or more disparate sources. A dashboard pulls the data from your data warehouse and transforms it into a series of charts, graphs and other visualizations that update in real-time. The data warehouse is used to collect and manage data from various sources, and it can be used for reporting, reviewing, and analyzing data if equipped with a dashboard [ 45 , 46 , 51 , 63 ]. High-quality data is essential for an effective data warehouse. It is crucial to have a standard for data transfer and check the data quality before storing it in the data warehouse. Data quality aspects in the examined dashboards included data completeness (e.g., missing data), correctness (e.g., accuracy), currency (e.g., timeliness), and provenance (e.g., reliability of the source). The standards included content, transmission, structural, and security [ 28 , 30 , 35 , 37 , 40 , 42 , 52 , 55 , 56 , 59 ]. Transferring data between systems and creating interactions between data sources requires attention to security and data access. For security measures, all users are assigned a level based on their performance and duties in the authorization system. Three levels of data security were implemented in the reviewed dashboards, i.e., client level, data transfer level, and server level. At the client level, user authentication is checked every 10 min to prevent cyber-attacks and interference in database queries through SQL injection [ 40 , 42 , 52 ]. The client and server data were encrypted through NoSSL open-source software at the data transfer level [ 52 , 55 ].

Given that the web server is open to public access, a backup computer in the middle (intermediary computer) is needed for filtering access to the database [ 28 , 30 , 40 ] to ensure proper security standards and protect the central database. This means all requests are passed through the web server to the intermediary computer, then to the central database, and vice versa. The dashboard design should consider easy access to the dashboard via phone, tablet, and laptop for real-time monitoring and checking KPIs at a glance [ 55 , 56 , 59 ].

Main findings

This scoping review study aimed to determine the design principles of PHDs. The included articles explained the details of the design and development of PHDs and their design criteria. The study findings revealed that the production rate of PHDs has been increasing in the past few years. The emergence of COVID-19 and the efforts to manage and control the outbreak/pandemic have significantly impacted this increasing trend. Several institutions worldwide have designed and developed COVID-19 dashboards to report epidemiologic statistics on a county, state, or national scale. Almost all states and most major cities in the USA had deployed a COVID-19 dashboard by the end of 2020. By 2021, all dashboards designed for this purpose had been updated to include information on vaccination or separate dashboards had been created to track COVID-19 vaccination [ 13 ]. Due to the massive amount of data and the need for real-time monitoring and response in public health situations, it is essential to pay attention to dashboard design principles to support the goals of public health surveillance [ 18 ]. After examining the indicators presented in the reviewed studies, dashboard design objectives and target users, dashboard content, dashboard user interface, data analysis and display, and infrastructure were identified as five general and essential principles in designing PHDs. Studies have also discussed the requirements and design principles of PHDs. Identifying users and their needs, using narrative information in addition to quantitative information in the dashboard, using a geographic map to display location data better, and stating the source of the data reported by the dashboard are mentioned criteria for designing a dashboard [ 66 ].

Likewise, the necessary components to support and facilitate implementing dashboards in public health organizations have been mentioned, including storage and management of data and information from different sources, coordination of data from different sources, standards support, analysis, defining and identifying KPIs, and information visualization [ 13 ]. Rasmussen et al. suggested four general principles for designing dashboards: presentation format, integration, interface design, and development and implementation [ 67 ]. These researchers remarked that inadequate attention to these principles could result in challenges for PHDs [ 67 ]. Furthermore, Ghazi Saeedi et al. mentioned KPI development, data sources, data generation, integration of dashboards to source systems, and information presentation issues as the challenges of implementing PHDs [ 68 ].

Purpose and users

The purpose of designing a dashboard is to provide a suitable tool for exploring a data set and finding the information the user needs. Therefore, paying attention to the user's needs and designing the appropriate dashboard is particularly important. Considering that a variety of users use dashboards, it is impossible to design a dashboard that fits the personality and ability of each user. However, identifying the primary goal of designing a dashboard and its target user group is the first step in choosing the correct and accurate KPIs, defining appropriate interactive and visual tools, and considering related data analysis methods. Marshal et al. have also emphasized the importance of this principle in designing PHDs in two separate studies [ 69 ].

KPIs are the main content component of a health dashboard. Therefore, choosing the type and number of indicators the dashboard should monitor and display is essential in designing and developing dashboards [ 32 , 70 , 71 ]. Every organization must measure the indicators that fit its objectives [ 72 ]. After identifying the main objective and target users, it is necessary to determine the appropriate measurement indicators. Determining a specific and adequate number of indicators emphasizes the available information, and users can review all the indicators at a glance. These findings are consistent with Peters et al.'s study, which indicated that moderate use of indicators can display information in various ways and effectively guide the user's visual flow by creating a particular order [ 73 , 74 ]. Serb et al. also suggested the importance of organizing indicators in the dashboard according to the level of use (macro, mezzo, micro level). Their study showed that at least 15 to 25 indicators are required for monitoring purposes in dashboards [ 75 ].

In user interface design, attention to the principles of information visualization and interaction with the user interface is essential [ 76 , 77 ]. Uniform techniques were not used to visualize functional indicators in the reviewed studies. Uniform visualization techniques are ineffective in dashboard design since it is necessary to consider users' preferences, abilities, knowledge, and skills in visualizing dashboards. Besides, Steichen and Mawad pointed out in separate studies that creating adaptive and personalized visualization systems tailored to users' cognitive and individual abilities can lead to a better understanding of displayed information [ 78 ]. The nature of data and human factors such as experience, skill, cognitive styles, and user preferences are also influential in selecting visualization and interactive techniques [ 79 , 80 ]. In Shneiderman's study, interactive techniques included "overview, zoom, filter, details-on-demand, relate, history, and extract" [ 81 ]. Khan et al. indicated that interactive techniques included "zoom and pan, overview and detail, and filtering" [ 82 ]. In Dal et al. 's study, interactive techniques for the dashboard included controlling the level of detail, filtering, searching, and customizing the display [ 83 ]. Yi et al. similarly implied interactive features included "select, explore, reconfigure, encode, abstract/elaborate, filter, and connect" [ 76 ].

Types of analysis and data presentation

The main application of dashboards is data analysis to provide appropriate insights into the regional distribution of disease burden and help allocate resources correctly. This analysis can help policymakers and healthcare providers make appropriate decisions. In most studies, timely data reporting and a suitable time trend in data analysis have been proposed as essential indicators in dashboard design. These findings align with the results of Curriero et al., emphasizing the importance of providing up-to-date data reports [ 57 ]. Another critical indicator in dashboard design is the ability to analyze data based on geographic location, age, gender, social status, ethnicity, and race. By collecting, registering, and using data related to meaningful subgroups of the population, these critical (and changeable) differences might be noticed. Brehaut et al. also showed that as far as infrastructure limitations and legal barriers allow, these indicators are vital and should be considered in designing a dashboard. Finally, some studies used descriptive approaches, machine learning prediction models, and simulations to predict future situations [ 84 ]. This indicator can help control diseases, especially pandemics [ 85 ]. This issue was also raised as one of the indicators that can help increase the efficiency of these dashboards in Brehaut's research [ 84 ].

Infrastructure is the backbone of every system, and the successful adoption of any eHealth system depends on the infrastructural arrangements [ 86 ].

The findings of this study revealed that a high percentage of studies had mentioned data warehousing and appropriate web service architecture as necessary infrastructures for dashboard design [ 67 , 87 ]. Given the diversity of systems and data in different formats, the dashboard infrastructure's main challenge is data integration, and creating data warehouses is an appropriate solution to this challenge [ 88 , 89 ]. Access to appropriate software and hardware, use of modern technology, sharing reliable and up-to-date data, and the need for a capable workforce to create and maintain dashboards are other identified components related to dashboard infrastructure [ 90 ].

In addition, the necessary infrastructure for creating a dashboard includes access to modern IT software and hardware, continuous and reliable data sharing, and the need for a capable workforce to create and maintain dashboards [ 13 ]. Among the challenges associated with PHDs are data quality, big data, information architecture, privacy, and security [ 91 ]. The quality of stored data is also one of the critical issues in dashboard infrastructure. Given the importance of data in decision-making at the public health level, the quality of stored data is also an essential prerequisite for dashboard infrastructure. Fadahunsi et al. also considered data quality an essential dashboard infrastructure component in two separate studies [ 92 ].

Informativeness (accuracy, completeness, interpretability, plausibility, provenance, and relevance), availability (accessibility, portability, security, and timeliness), and usability (conformance, consistency, and maintainability) are key features indicated in these two studies [ 92 , 93 ]. Transparency about data sources and how indicators are calculated are critical for reports' overall quality, credibility, and reliability. Identifying the sources used and calculating indicators in PHDs are essential for transparency about data collection and would help to understand the logic behind the reports [ 73 , 94 ].

Regarding infrastructure, information security was also one of the issues mentioned in a considerable number of sources. Given the integration of various systems at the organizational level and their connection to the dashboard, using data exchange standards for system interaction is an issue that should be considered [ 95 ]. These findings were in line with a study by Li Y-CJ et al., who considered electronic data exchange in standard data formats essential for improving data accessibility [ 96 ]. Moreover, this study showed that these standards preserve data security, reduce resource waste, and improve the quality of care [ 96 ]. Based on the importance and quality of the disclosed information, access control should exist at multiple levels of security/privacy [ 97 ].

Implications for policy, practice, and future research

This study extracts the public health dashboard's design criteria and proposes some design principles based on the available knowledge in the area. Given the enormous volume of data and the need for quick response in public health situations, this study is a potentially vital source for helping policymakers, developers, public healthcare organizations, and managers to design and develop PHDs as a prerequisite for early response, particularly during the probable pandemic. As pandemic response requires early and robust verifications, identifying this potentiality of dashboards in data management can be helpful. The lesson learned from the COVID-19 pandemic indicates that public health organizations must equip themselves with dashboards for emerging pandemics and many other vital activities for public health promotion. In other words, investing in dashboard software tools and systems, processes, and people who support PHDs, could be a tailored practice and intervention for the public health policymakers. Exchanging information between healthcare providers and public health organizations and developing an appropriate infrastructure for data exchange is critical for more effective monitoring of epidemic diseases. Clinical information systems should exchange information in real-time at a national level to effectively use dashboards at the public health level for monitoring and managing epidemic diseases and taking timely actions. Therefore, it is suggested that the government examines the technical infrastructure (data architectures, structural and content standards, data exchange, security, and data resources) for appropriate data exchange between various clinical systems and the dashboard.

Strengths and limitations

The present study addresses the principles of designing PHDs and provides a comprehensive view of designing dashboards. In addition, this study investigated all aspects of PHDs design, including purposes, content, user interface, types of analysis, and infrastructure, and proposed sub-criteria for each criterion. However, the study needed further access to some articles' full text and the search was also restricted to articles published in English.

Although the scoping reviews are mainly designed to help policymakers figure out the key concepts underpinning a research area and help them to have clear working definitions, and/or the conceptual boundaries of a topic, the results of this study need to be customized and tailored based on the local public health priorities of the countries through Focus Group Discussions (FGDs) and feasibility assessment panels before applying at the implementation phases. It is also suggested to conduct a study regarding the design and implementation of PHDs according to the income level of the countries. The results of this scoping review can open a new window for conducting future systematic reviews to address the feasibility, appropriateness, meaningfulness, or effectiveness of public health surveillance dashboards. Finally, as the descriptive results present a geographical distribution of PHDs implementation to create a general understanding and illustrate a map to policymakers, stakeholders and researchers to figure out the concentration hotspots and healthcare system`s attention to the topic, it is important to interpret the results conservatively to avoid any kind of misinterpretation about the place or type of the included studies. The same limitation could be considered as the present results were not broken down by country (low, middle and high income), so the findings should be generalized conservatively to the setting of low-income countries as most of the included studies were conducted in high income countries.

Monitoring health, managing epidemics, and taking timely action requires real-time information exchange between clinical information systems and PHDs. Therefore, given the volume of data, the need for real-time monitoring and response in public health situations, and disease surveillance during epidemics, it is necessary to pay attention to dashboard design principles to achieve public health surveillance goals. Findings of the current indicated that design principles for the PHDs could be presented in five groups, i.e., considering aim and target users, appropriate content, interface, data analysis and presentation types, and infrastructure.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

Abbreviations

Public Health Dashboards

Program for Monitoring Emerging Diseases

Global Outbreak Alert Response Network

Population (or participants)/Concept/Context

Geographic Information System

Key Performance Indicators

Acquired Immunodeficiency Syndrome

Service-Oriented Architecture

Focus Group Discussions

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The initial project idea was conceived, managed, and supervised by S.A. and R.R. All authors then contributed to the refinement of the idea, scoping review process, and to the formal analysis of the results. S.A., R.R., and S.D. conducted the initial and full-text screenings. S.A. and S.D. drafted the first version of the manuscript with significant revisions and feedback from R.R., H.A., and P.B. All authors contributed to discussions on the direction of the scoping review, and subsequent manuscript revisions and all agreed to the final manuscript version.

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data presentation or analysis

How technology is reinventing education

Stanford Graduate School of Education Dean Dan Schwartz and other education scholars weigh in on what's next for some of the technology trends taking center stage in the classroom.

data presentation or analysis

Image credit: Claire Scully

New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of Stanford Graduate School of Education (GSE), who is also a professor of educational technology at the GSE and faculty director of the Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately worried that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or coach students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the AI + Education initiative at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of CRAFT (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the Digital Learning initiative at the Stanford Accelerator for Learning, which runs a program exploring the use of virtual field trips to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

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