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# Write a summary of your understanding of the study (below abstract ). In your ownsummary (or...

Write a summary of your understanding of the study (below abstract ). In your ownsummary (or your first comment), you need to refer to the article and explain what the researchers did. You do not have to compute anything, but explain the idea central to the study.

Abstract: The Chi-square statistic is a non-parametric (distribution-free) tool designed to analyze group differences when the dependent variable is measured at a nominal level. Like all non-parametric statistics, the Chi-square is robust with respect to the distribution of the data. Specifically, it does not require equality of variances among the study groups or homoscedasticity in the data. It permits evaluation of both dichotomous independent variables, and of multiple group studies. Unlike many other non-parametric and some parametric statistics, the calculations needed to compute the Chi-square provide considerable information about how each of the groups performed in the study. This richness of detail allows the researcher to understand the results and thus to derive more detailed information from this statistic than from many others. The Chi-square is a significant statistic and should be followed with a strength statistic. The Cramer’s V is the most common strength test used to test the data when a significant Chi-square result has been obtained. Advantages of the Chi-square include its robustness with respect to the distribution of the data, its ease of computation, the detailed information that can be derived from the test, its use in studies for which parametric assumptions cannot be met, and its flexibility in handling data from both two group and multiple group studies. Limitations include its sample size requirements, the difficulty of interpretation when there are large numbers of categories (20 or more) in the independent or dependent variables, and tendency of the Cramer’s V to produce relative low correlation measures, even for highly signifi cant results. Key words: Chi-square; non-parametric; assumptions; categorical data; statistical analysis.

Summary and conclusions The Chi-square is a valuable analysis tool that provides considerable information about the nature of research data. It is a powerful statistic that enables researchers to test hypotheses about variables measured at the nominal level. As with all inferential statistics, the results are most reliable when the data are collected from randomly selected subjects, and when sample sizes are sufficiently large that they produce appropriate statistical power. The Chi-square is also an excellent tool to use when violations of assumptions of equal variances and homoscedasticity are violated and parametric statistics such as the t-test and ANOVA cannot provide reliable results. As the Chi-Square and its strength test, the Cramer’s V are both simple to compute, it is an especially convenient tool for researchers in the field where statistical programs may not be easily accessed. However, most statistical programs provide not only the Chi-square and Cramer’s V, but also a variety of other non-parametric tools for both significance and strength testing. Potential conflict of interest None declared.

The Chi Square test is Non Parametric test to test the significance difference between the dependent variable.The test is used when the data is measured in Nominal scale. A Chi-square test is designed to analyze categorical data. That means that the data has been counted and divided into categories. It will not work with parametric or continuous data. Also the test does not require any Distribution. This test is distribution free.As like non parametric tests the chi square test also use when the certain assumption is does not follow by the parametric test like equal variance,normality,independency etc.The chi square distribution can handle more than two categories. So the overall chi square test is good as compair to other test.