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  • Korean J Anesthesiol
  • v.71(2); 2018 Apr

Introduction to systematic review and meta-analysis

1 Department of Anesthesiology and Pain Medicine, Inje University Seoul Paik Hospital, Seoul, Korea

2 Department of Anesthesiology and Pain Medicine, Chung-Ang University College of Medicine, Seoul, Korea

Systematic reviews and meta-analyses present results by combining and analyzing data from different studies conducted on similar research topics. In recent years, systematic reviews and meta-analyses have been actively performed in various fields including anesthesiology. These research methods are powerful tools that can overcome the difficulties in performing large-scale randomized controlled trials. However, the inclusion of studies with any biases or improperly assessed quality of evidence in systematic reviews and meta-analyses could yield misleading results. Therefore, various guidelines have been suggested for conducting systematic reviews and meta-analyses to help standardize them and improve their quality. Nonetheless, accepting the conclusions of many studies without understanding the meta-analysis can be dangerous. Therefore, this article provides an easy introduction to clinicians on performing and understanding meta-analyses.

Introduction

A systematic review collects all possible studies related to a given topic and design, and reviews and analyzes their results [ 1 ]. During the systematic review process, the quality of studies is evaluated, and a statistical meta-analysis of the study results is conducted on the basis of their quality. A meta-analysis is a valid, objective, and scientific method of analyzing and combining different results. Usually, in order to obtain more reliable results, a meta-analysis is mainly conducted on randomized controlled trials (RCTs), which have a high level of evidence [ 2 ] ( Fig. 1 ). Since 1999, various papers have presented guidelines for reporting meta-analyses of RCTs. Following the Quality of Reporting of Meta-analyses (QUORUM) statement [ 3 ], and the appearance of registers such as Cochrane Library’s Methodology Register, a large number of systematic literature reviews have been registered. In 2009, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 4 ] was published, and it greatly helped standardize and improve the quality of systematic reviews and meta-analyses [ 5 ].

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Levels of evidence.

In anesthesiology, the importance of systematic reviews and meta-analyses has been highlighted, and they provide diagnostic and therapeutic value to various areas, including not only perioperative management but also intensive care and outpatient anesthesia [6–13]. Systematic reviews and meta-analyses include various topics, such as comparing various treatments of postoperative nausea and vomiting [ 14 , 15 ], comparing general anesthesia and regional anesthesia [ 16 – 18 ], comparing airway maintenance devices [ 8 , 19 ], comparing various methods of postoperative pain control (e.g., patient-controlled analgesia pumps, nerve block, or analgesics) [ 20 – 23 ], comparing the precision of various monitoring instruments [ 7 ], and meta-analysis of dose-response in various drugs [ 12 ].

Thus, literature reviews and meta-analyses are being conducted in diverse medical fields, and the aim of highlighting their importance is to help better extract accurate, good quality data from the flood of data being produced. However, a lack of understanding about systematic reviews and meta-analyses can lead to incorrect outcomes being derived from the review and analysis processes. If readers indiscriminately accept the results of the many meta-analyses that are published, incorrect data may be obtained. Therefore, in this review, we aim to describe the contents and methods used in systematic reviews and meta-analyses in a way that is easy to understand for future authors and readers of systematic review and meta-analysis.

Study Planning

It is easy to confuse systematic reviews and meta-analyses. A systematic review is an objective, reproducible method to find answers to a certain research question, by collecting all available studies related to that question and reviewing and analyzing their results. A meta-analysis differs from a systematic review in that it uses statistical methods on estimates from two or more different studies to form a pooled estimate [ 1 ]. Following a systematic review, if it is not possible to form a pooled estimate, it can be published as is without progressing to a meta-analysis; however, if it is possible to form a pooled estimate from the extracted data, a meta-analysis can be attempted. Systematic reviews and meta-analyses usually proceed according to the flowchart presented in Fig. 2 . We explain each of the stages below.

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Flowchart illustrating a systematic review.

Formulating research questions

A systematic review attempts to gather all available empirical research by using clearly defined, systematic methods to obtain answers to a specific question. A meta-analysis is the statistical process of analyzing and combining results from several similar studies. Here, the definition of the word “similar” is not made clear, but when selecting a topic for the meta-analysis, it is essential to ensure that the different studies present data that can be combined. If the studies contain data on the same topic that can be combined, a meta-analysis can even be performed using data from only two studies. However, study selection via a systematic review is a precondition for performing a meta-analysis, and it is important to clearly define the Population, Intervention, Comparison, Outcomes (PICO) parameters that are central to evidence-based research. In addition, selection of the research topic is based on logical evidence, and it is important to select a topic that is familiar to readers without clearly confirmed the evidence [ 24 ].

Protocols and registration

In systematic reviews, prior registration of a detailed research plan is very important. In order to make the research process transparent, primary/secondary outcomes and methods are set in advance, and in the event of changes to the method, other researchers and readers are informed when, how, and why. Many studies are registered with an organization like PROSPERO ( http://www.crd.york.ac.uk/PROSPERO/ ), and the registration number is recorded when reporting the study, in order to share the protocol at the time of planning.

Defining inclusion and exclusion criteria

Information is included on the study design, patient characteristics, publication status (published or unpublished), language used, and research period. If there is a discrepancy between the number of patients included in the study and the number of patients included in the analysis, this needs to be clearly explained while describing the patient characteristics, to avoid confusing the reader.

Literature search and study selection

In order to secure proper basis for evidence-based research, it is essential to perform a broad search that includes as many studies as possible that meet the inclusion and exclusion criteria. Typically, the three bibliographic databases Medline, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) are used. In domestic studies, the Korean databases KoreaMed, KMBASE, and RISS4U may be included. Effort is required to identify not only published studies but also abstracts, ongoing studies, and studies awaiting publication. Among the studies retrieved in the search, the researchers remove duplicate studies, select studies that meet the inclusion/exclusion criteria based on the abstracts, and then make the final selection of studies based on their full text. In order to maintain transparency and objectivity throughout this process, study selection is conducted independently by at least two investigators. When there is a inconsistency in opinions, intervention is required via debate or by a third reviewer. The methods for this process also need to be planned in advance. It is essential to ensure the reproducibility of the literature selection process [ 25 ].

Quality of evidence

However, well planned the systematic review or meta-analysis is, if the quality of evidence in the studies is low, the quality of the meta-analysis decreases and incorrect results can be obtained [ 26 ]. Even when using randomized studies with a high quality of evidence, evaluating the quality of evidence precisely helps determine the strength of recommendations in the meta-analysis. One method of evaluating the quality of evidence in non-randomized studies is the Newcastle-Ottawa Scale, provided by the Ottawa Hospital Research Institute 1) . However, we are mostly focusing on meta-analyses that use randomized studies.

If the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) system ( http://www.gradeworkinggroup.org/ ) is used, the quality of evidence is evaluated on the basis of the study limitations, inaccuracies, incompleteness of outcome data, indirectness of evidence, and risk of publication bias, and this is used to determine the strength of recommendations [ 27 ]. As shown in Table 1 , the study limitations are evaluated using the “risk of bias” method proposed by Cochrane 2) . This method classifies bias in randomized studies as “low,” “high,” or “unclear” on the basis of the presence or absence of six processes (random sequence generation, allocation concealment, blinding participants or investigators, incomplete outcome data, selective reporting, and other biases) [ 28 ].

The Cochrane Collaboration’s Tool for Assessing the Risk of Bias [ 28 ]

Data extraction

Two different investigators extract data based on the objectives and form of the study; thereafter, the extracted data are reviewed. Since the size and format of each variable are different, the size and format of the outcomes are also different, and slight changes may be required when combining the data [ 29 ]. If there are differences in the size and format of the outcome variables that cause difficulties combining the data, such as the use of different evaluation instruments or different evaluation timepoints, the analysis may be limited to a systematic review. The investigators resolve differences of opinion by debate, and if they fail to reach a consensus, a third-reviewer is consulted.

Data Analysis

The aim of a meta-analysis is to derive a conclusion with increased power and accuracy than what could not be able to achieve in individual studies. Therefore, before analysis, it is crucial to evaluate the direction of effect, size of effect, homogeneity of effects among studies, and strength of evidence [ 30 ]. Thereafter, the data are reviewed qualitatively and quantitatively. If it is determined that the different research outcomes cannot be combined, all the results and characteristics of the individual studies are displayed in a table or in a descriptive form; this is referred to as a qualitative review. A meta-analysis is a quantitative review, in which the clinical effectiveness is evaluated by calculating the weighted pooled estimate for the interventions in at least two separate studies.

The pooled estimate is the outcome of the meta-analysis, and is typically explained using a forest plot ( Figs. 3 and ​ and4). 4 ). The black squares in the forest plot are the odds ratios (ORs) and 95% confidence intervals in each study. The area of the squares represents the weight reflected in the meta-analysis. The black diamond represents the OR and 95% confidence interval calculated across all the included studies. The bold vertical line represents a lack of therapeutic effect (OR = 1); if the confidence interval includes OR = 1, it means no significant difference was found between the treatment and control groups.

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Forest plot analyzed by two different models using the same data. (A) Fixed-effect model. (B) Random-effect model. The figure depicts individual trials as filled squares with the relative sample size and the solid line as the 95% confidence interval of the difference. The diamond shape indicates the pooled estimate and uncertainty for the combined effect. The vertical line indicates the treatment group shows no effect (OR = 1). Moreover, if the confidence interval includes 1, then the result shows no evidence of difference between the treatment and control groups.

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Forest plot representing homogeneous data.

Dichotomous variables and continuous variables

In data analysis, outcome variables can be considered broadly in terms of dichotomous variables and continuous variables. When combining data from continuous variables, the mean difference (MD) and standardized mean difference (SMD) are used ( Table 2 ).

Summary of Meta-analysis Methods Available in RevMan [ 28 ]

The MD is the absolute difference in mean values between the groups, and the SMD is the mean difference between groups divided by the standard deviation. When results are presented in the same units, the MD can be used, but when results are presented in different units, the SMD should be used. When the MD is used, the combined units must be shown. A value of “0” for the MD or SMD indicates that the effects of the new treatment method and the existing treatment method are the same. A value lower than “0” means the new treatment method is less effective than the existing method, and a value greater than “0” means the new treatment is more effective than the existing method.

When combining data for dichotomous variables, the OR, risk ratio (RR), or risk difference (RD) can be used. The RR and RD can be used for RCTs, quasi-experimental studies, or cohort studies, and the OR can be used for other case-control studies or cross-sectional studies. However, because the OR is difficult to interpret, using the RR and RD, if possible, is recommended. If the outcome variable is a dichotomous variable, it can be presented as the number needed to treat (NNT), which is the minimum number of patients who need to be treated in the intervention group, compared to the control group, for a given event to occur in at least one patient. Based on Table 3 , in an RCT, if x is the probability of the event occurring in the control group and y is the probability of the event occurring in the intervention group, then x = c/(c + d), y = a/(a + b), and the absolute risk reduction (ARR) = x − y. NNT can be obtained as the reciprocal, 1/ARR.

Calculation of the Number Needed to Treat in the Dichotomous table

Fixed-effect models and random-effect models

In order to analyze effect size, two types of models can be used: a fixed-effect model or a random-effect model. A fixed-effect model assumes that the effect of treatment is the same, and that variation between results in different studies is due to random error. Thus, a fixed-effect model can be used when the studies are considered to have the same design and methodology, or when the variability in results within a study is small, and the variance is thought to be due to random error. Three common methods are used for weighted estimation in a fixed-effect model: 1) inverse variance-weighted estimation 3) , 2) Mantel-Haenszel estimation 4) , and 3) Peto estimation 5) .

A random-effect model assumes heterogeneity between the studies being combined, and these models are used when the studies are assumed different, even if a heterogeneity test does not show a significant result. Unlike a fixed-effect model, a random-effect model assumes that the size of the effect of treatment differs among studies. Thus, differences in variation among studies are thought to be due to not only random error but also between-study variability in results. Therefore, weight does not decrease greatly for studies with a small number of patients. Among methods for weighted estimation in a random-effect model, the DerSimonian and Laird method 6) is mostly used for dichotomous variables, as the simplest method, while inverse variance-weighted estimation is used for continuous variables, as with fixed-effect models. These four methods are all used in Review Manager software (The Cochrane Collaboration, UK), and are described in a study by Deeks et al. [ 31 ] ( Table 2 ). However, when the number of studies included in the analysis is less than 10, the Hartung-Knapp-Sidik-Jonkman method 7) can better reduce the risk of type 1 error than does the DerSimonian and Laird method [ 32 ].

Fig. 3 shows the results of analyzing outcome data using a fixed-effect model (A) and a random-effect model (B). As shown in Fig. 3 , while the results from large studies are weighted more heavily in the fixed-effect model, studies are given relatively similar weights irrespective of study size in the random-effect model. Although identical data were being analyzed, as shown in Fig. 3 , the significant result in the fixed-effect model was no longer significant in the random-effect model. One representative example of the small study effect in a random-effect model is the meta-analysis by Li et al. [ 33 ]. In a large-scale study, intravenous injection of magnesium was unrelated to acute myocardial infarction, but in the random-effect model, which included numerous small studies, the small study effect resulted in an association being found between intravenous injection of magnesium and myocardial infarction. This small study effect can be controlled for by using a sensitivity analysis, which is performed to examine the contribution of each of the included studies to the final meta-analysis result. In particular, when heterogeneity is suspected in the study methods or results, by changing certain data or analytical methods, this method makes it possible to verify whether the changes affect the robustness of the results, and to examine the causes of such effects [ 34 ].

Heterogeneity

Homogeneity test is a method whether the degree of heterogeneity is greater than would be expected to occur naturally when the effect size calculated from several studies is higher than the sampling error. This makes it possible to test whether the effect size calculated from several studies is the same. Three types of homogeneity tests can be used: 1) forest plot, 2) Cochrane’s Q test (chi-squared), and 3) Higgins I 2 statistics. In the forest plot, as shown in Fig. 4 , greater overlap between the confidence intervals indicates greater homogeneity. For the Q statistic, when the P value of the chi-squared test, calculated from the forest plot in Fig. 4 , is less than 0.1, it is considered to show statistical heterogeneity and a random-effect can be used. Finally, I 2 can be used [ 35 ].

I 2 , calculated as shown above, returns a value between 0 and 100%. A value less than 25% is considered to show strong homogeneity, a value of 50% is average, and a value greater than 75% indicates strong heterogeneity.

Even when the data cannot be shown to be homogeneous, a fixed-effect model can be used, ignoring the heterogeneity, and all the study results can be presented individually, without combining them. However, in many cases, a random-effect model is applied, as described above, and a subgroup analysis or meta-regression analysis is performed to explain the heterogeneity. In a subgroup analysis, the data are divided into subgroups that are expected to be homogeneous, and these subgroups are analyzed. This needs to be planned in the predetermined protocol before starting the meta-analysis. A meta-regression analysis is similar to a normal regression analysis, except that the heterogeneity between studies is modeled. This process involves performing a regression analysis of the pooled estimate for covariance at the study level, and so it is usually not considered when the number of studies is less than 10. Here, univariate and multivariate regression analyses can both be considered.

Publication bias

Publication bias is the most common type of reporting bias in meta-analyses. This refers to the distortion of meta-analysis outcomes due to the higher likelihood of publication of statistically significant studies rather than non-significant studies. In order to test the presence or absence of publication bias, first, a funnel plot can be used ( Fig. 5 ). Studies are plotted on a scatter plot with effect size on the x-axis and precision or total sample size on the y-axis. If the points form an upside-down funnel shape, with a broad base that narrows towards the top of the plot, this indicates the absence of a publication bias ( Fig. 5A ) [ 29 , 36 ]. On the other hand, if the plot shows an asymmetric shape, with no points on one side of the graph, then publication bias can be suspected ( Fig. 5B ). Second, to test publication bias statistically, Begg and Mazumdar’s rank correlation test 8) [ 37 ] or Egger’s test 9) [ 29 ] can be used. If publication bias is detected, the trim-and-fill method 10) can be used to correct the bias [ 38 ]. Fig. 6 displays results that show publication bias in Egger’s test, which has then been corrected using the trim-and-fill method using Comprehensive Meta-Analysis software (Biostat, USA).

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Funnel plot showing the effect size on the x-axis and sample size on the y-axis as a scatter plot. (A) Funnel plot without publication bias. The individual plots are broader at the bottom and narrower at the top. (B) Funnel plot with publication bias. The individual plots are located asymmetrically.

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Funnel plot adjusted using the trim-and-fill method. White circles: comparisons included. Black circles: inputted comparisons using the trim-and-fill method. White diamond: pooled observed log risk ratio. Black diamond: pooled inputted log risk ratio.

Result Presentation

When reporting the results of a systematic review or meta-analysis, the analytical content and methods should be described in detail. First, a flowchart is displayed with the literature search and selection process according to the inclusion/exclusion criteria. Second, a table is shown with the characteristics of the included studies. A table should also be included with information related to the quality of evidence, such as GRADE ( Table 4 ). Third, the results of data analysis are shown in a forest plot and funnel plot. Fourth, if the results use dichotomous data, the NNT values can be reported, as described above.

The GRADE Evidence Quality for Each Outcome

N: number of studies, ROB: risk of bias, PON: postoperative nausea, POV: postoperative vomiting, PONV: postoperative nausea and vomiting, CI: confidence interval, RR: risk ratio, AR: absolute risk.

When Review Manager software (The Cochrane Collaboration, UK) is used for the analysis, two types of P values are given. The first is the P value from the z-test, which tests the null hypothesis that the intervention has no effect. The second P value is from the chi-squared test, which tests the null hypothesis for a lack of heterogeneity. The statistical result for the intervention effect, which is generally considered the most important result in meta-analyses, is the z-test P value.

A common mistake when reporting results is, given a z-test P value greater than 0.05, to say there was “no statistical significance” or “no difference.” When evaluating statistical significance in a meta-analysis, a P value lower than 0.05 can be explained as “a significant difference in the effects of the two treatment methods.” However, the P value may appear non-significant whether or not there is a difference between the two treatment methods. In such a situation, it is better to announce “there was no strong evidence for an effect,” and to present the P value and confidence intervals. Another common mistake is to think that a smaller P value is indicative of a more significant effect. In meta-analyses of large-scale studies, the P value is more greatly affected by the number of studies and patients included, rather than by the significance of the results; therefore, care should be taken when interpreting the results of a meta-analysis.

When performing a systematic literature review or meta-analysis, if the quality of studies is not properly evaluated or if proper methodology is not strictly applied, the results can be biased and the outcomes can be incorrect. However, when systematic reviews and meta-analyses are properly implemented, they can yield powerful results that could usually only be achieved using large-scale RCTs, which are difficult to perform in individual studies. As our understanding of evidence-based medicine increases and its importance is better appreciated, the number of systematic reviews and meta-analyses will keep increasing. However, indiscriminate acceptance of the results of all these meta-analyses can be dangerous, and hence, we recommend that their results be received critically on the basis of a more accurate understanding.

1) http://www.ohri.ca .

2) http://methods.cochrane.org/bias/assessing-risk-bias-included-studies .

3) The inverse variance-weighted estimation method is useful if the number of studies is small with large sample sizes.

4) The Mantel-Haenszel estimation method is useful if the number of studies is large with small sample sizes.

5) The Peto estimation method is useful if the event rate is low or one of the two groups shows zero incidence.

6) The most popular and simplest statistical method used in Review Manager and Comprehensive Meta-analysis software.

7) Alternative random-effect model meta-analysis that has more adequate error rates than does the common DerSimonian and Laird method, especially when the number of studies is small. However, even with the Hartung-Knapp-Sidik-Jonkman method, when there are less than five studies with very unequal sizes, extra caution is needed.

8) The Begg and Mazumdar rank correlation test uses the correlation between the ranks of effect sizes and the ranks of their variances [ 37 ].

9) The degree of funnel plot asymmetry as measured by the intercept from the regression of standard normal deviates against precision [ 29 ].

10) If there are more small studies on one side, we expect the suppression of studies on the other side. Trimming yields the adjusted effect size and reduces the variance of the effects by adding the original studies back into the analysis as a mirror image of each study.

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Definition: A literature review is a systematic examination and synthesis of existing scholarly research on a specific topic or subject.

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International Symposium in Management Innovation for Sustainable Management and Entrepreneurship

SIM 2019: Innovation in Sustainable Management and Entrepreneurship pp 229–244 Cite as

A Review of the Research on Financial Performance and Its Determinants

  • Mihaela Brindusa Tudose 5 &
  • Silvia Avasilcai 5  
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  • First Online: 30 May 2020

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Part of the Springer Proceedings in Business and Economics book series (SPBE)

To carry out the review, the study was designed in such a manner as to enable us to: (a) identify the degree of interest that researchers displayed for scientific grounding of concepts they operate with and (b) identify the degree to which new lines of research have been shaped on determinants of financial performance. Based on a sample of 45 articles which analyzed the corporate financial performance, published during 2014–2019, was established a database which details: the researches’ topic; dependent and independent analyzed variables (and the indicators used for their assessment); samples; sources of data and periods in which they have been collected; results of the research; and authors’ contributions in defining the concept of performance. In terms of study’s first aim, we have shown that authors are concerned with grounding concepts with which they operate, but they mostly focus on the determinants and not on the financial performance. In terms of determinants of the financial performance, the study reveals that the research is more detailed and they extend the analyses with new variables (such as ethics of stakeholders, corporate lobbying, corporate culture, green credit or non-financial reporting) for explaining the dynamics of the financial performance.

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Tudose, M.B., Avasilcai, S. (2020). A Review of the Research on Financial Performance and Its Determinants. In: Prostean, G., Lavios Villahoz, J., Brancu, L., Bakacsi, G. (eds) Innovation in Sustainable Management and Entrepreneurship. SIM 2019. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-44711-3_17

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The Analysis and Use of Financial Ratios: A Review Article

Profile image of Paul Barnes

1987, Journal of Business Finance & Accounting

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The certainty of the reliance on the use of financial ratio analysis in making investment decisions by potential investors still remained a mystery. This has to do with the choice of ratios to select when making investment decisions. Many shareholders in Nigeria are uneducated or illiterate, and due to their ignorance, they cannot use ratio analysis in evaluating firms for investment decisions. Thus, this paper explores the concept of financial ratio analysis in terms of the decision usefulness of financial ratios. The paper suggests that the relevant financial information needed for the purposes of making investment decision can be sourced through the use of financial ratio analysis. Therefore, management must ensure that disclosure of comprehensive financial ratios form part of financial statement prepared for the overall appraisal of firms.

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Financial ratios are assumed to be very useful predictors of business performance, growth and financial status. This study evaluates the collinearity and correlation of financial ratios to establish a pattern of relationship that could serve s n empirically supported guide for financial ratios reliance in business evaluation decisions. The research data were gathered from the annual account report of vita foam Plc and analyzed using collinearity and correlation to test the research propositions. The paper found the existence of correlation and collinearity in the financial ratios, and concludes that financial ratios are reliable business status indicators. We therefore recommend the application of financial ratios in the evaluation of business performance, growth and financial strength.

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The current business environment has forced business units to devise innovative means and mechanisms so as to stay competitive in the market. This necessitates the need for constantly evaluating their performance measures. Business units have identified several measures which depict the performance yet ambiguities remain with respect to what is appropriate measure for performance measurement. Ratio analysis is one such measure which business units have been using for sufficient period of time. Yet there exists certain points of consideration which form the basis for this paper and their convergence into research question namely (a) What constitutes ratio analysis and (b) Which of the component of ratio analysis, has the greatest contribution? While addressing these questions, HUDCO’s secondary data is used as a base. Ratio analysis is used to depict the measure of performance while descriptive statistics is used for data interpretation. Findings indicate that there is no single comp...

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Ratios are routinely used for extracting information from accounting reports. However, ratios present only part of the information available and can be easily complemented provided that information-technology facilities are accessible. This study infers the functional form of the information discarded by ratios. Then it develops extensions of ratios incorporating the discarded information, showing examples of their use and discussing the benefits obtained. Extensions of ratios seem promising as a facility attached to computerized databases of accounting reports. The concepts developed here are a step toward a more technology-supported analysis of financial statements.

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Empirical studies have shown distributions of financial ratios are skewed. An explanation for this is given and it is argued that in such circumstances comparison of a fmancial ratio with some norm (e.g. industry average) is likely to misinform. It is also shown that where financial ratios are used as inputs to statistical models normality is irrelevant but a method of transformation into a normal distribution is provided whereby original interrelationships are preserved. Finally, because of the inadequacies of financial ratios, it is shown how regression analysis may be used in financial statement analysis.

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This study identifies general postulates underlying the validity of the financial ratio measurement. Then, new relationships are suggested obeying the same postulates, which may replace the ratio form in the case of non-proportionality. Where proportionality holds, these relationships revert to the traditional ratio. The paper also reviews the reasons for expecting non-proportional components in ratios and presents application examples of the new relationships.

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A Company will be interested in investors if its financial report is relevant and reliable. The relevance and reliable financial report can be reflected in the Asset Number and Liability Report on a Financial Position Statement (Balance Sheet). The success of a company can be measured by company ability, which is reflected in management performance. A company's performance parameter that commonly used is profit change. Profit itself is important to the company since this will be decision basis for investors to invest in the company. Profit Change reflected in financial statements using fair value will bring benefits to market participants, as the financial statements itself reflects the real market value. This research aims to examine the influence of Current Ratio, Total Asset Turnover, Price Earnings Ratio, and Return on Asset, towards profit changes that based on fair value. Research problem discussed in this study was how CR, TATO, PER, and ROA influence profit changes of a property company, real estate, and building construction, in period 2013-2016. The study was based on fair value. The population was company property, real estate, and building construction, that have been listed in Indonesia Stock Exchange (ISE). It consisted of 63 companies. Through Purposive Sampling technique, 12 companies were selected to be examined. Data analysis was conducted through a multiple linear regression analysis. Results of multiple linear regression analysis showed that the CR, TATO, and PER had an influence towards Profit Changes, while ROA had no influence toward it.

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Jagran Josh

ISC Class 12 English Literature Paper Analysis 2024: Student Feedback, Difficulty Level and Expert Review

i mg src="https://img.jagranjosh.com/images/2024/February/1322024/isc-english-literature-paper-review-analysis-2024.jpg" width="1200" height="675" />

ISC Class 12 English Literature Exam Analysis 2024: The Council for the Indian School Certificate Examinations (CISCE) board is conducting its Indian School Certificate (ISC) examinations 2023-24. The ISC exams for 2024 started on February 13, 2024. Today, February 13, 2024, the English Literature (Paper-2) successfully concluded. The paper brought out the creative language skills in students as the questions were from poetry, drama, and prose. 

The exam started as per the schedule and finished on time. The ISC English Literature 2024 exam was conducted in the evening shift from 2:00 p.m. to 5:00 p.m. As per the students’ reactions, the paper was a bit lengthy and moderate to solve. To learn more about the CISCE Class 12 English Paper 2, check out this report.

ISC Class 12 English Literature Exam 2024 Highlights

Check important highlights from ISC 12th English Paper 2, 2024 in the table below:

ISC Class 12 English Literature 2024 Analysis

Understanding a paper is important to analyse its difficulty and expect the result. Students, tutors, and schools check the paper review for the above reasons. This also helps teachers make variations in their teaching strategies to prepare the next batch well. 

The 2024 ISC English Paper 2 format was as follows:

  • Questions 1 and 2 of Section A were from the drama: The Tempest: William Shakespeare: Acts 3, 4, and 5.
  • Question 1 had ten MCQs.
  • Question 2 had ten fill-in-the-blank questions.
  • Question 3 was from The Tempest by William Shakespeare. It had three subparts with one internal choice. 
  • Question 4 was from Echoes: Prose, with three subparts and one internal choice.
  • Question 5 was from Reverie: Poetry, with three subparts and one internal choice.

ISC English Literature Pupil Analysis 2024

As per the pupil’s reaction, the ISC Class 12th English Literature paper was easy to moderate as the questions were direct. Some students felt that the paper was a bit lengthy, but they were able to solve all the questions till the end. Check out some of the frequent responses by students:

  • Solving the specimen papers and previous year's questions helped the students.
  • No questions were irrelevant and were asked from the syllabus.
  • The paper pattern was similar to the specimen paper.
  • Long-answer questions were comparatively safer, as students were able to express their thoughts properly. 

This ISC English 2 pupil analysis was based on the interaction with only a limited number of students. Therefore, the thoughts of other students might vary based on their knowledge, speed of writing, and exam preparation.

ISC Class 12 English Literature Paper 2024: Review by Expert

The experts rated the paper as moderate. They say that the questions were from the topics that were taught well to students and covered as per the syllabus. Students who have used creative and easy language will get good marks.

  • ISC Class 12 Date Sheet 2024
  • ISC Class 12 English Language Paper Review 2024
  • ISC Class 12 Syllabus for 2024 Exams
  • ISC Class 12 Specimen Papers for 2024 Exams

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    Efficiency ratios vary widely across retailers and over time. Historically, a lot of analysis has been done in the retail sector, but the focus was solely on inventory. On the other hand, some researchers employed ratio analysis to analyze general procedures and failures prediction.

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    Sub-group analysis and meta-regression further explored heterogeneity (PSMD < 0.05, I2 > 50%, n ≥ 10). Supplementation with tannins reduced the feed conversion ratio (p < 0.01) but increased the final body weight (p < 0.01) of weaned piglets. Chestnut and grape seed proanthocyanidin tannin sources yielded higher effects on growth performance.

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    Pelvic exenteration represents a radical procedure aimed at achieving complete tumor resection with negative margins. Although it is the only therapeutic option for some cases of advanced tumors, it is associated with several perioperative complications. We believe that careful patient selection is related to better oncologic outcomes and lower complication rates. The objectives of this review ...

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