The Chi-square test of independence

By M. McHugh. Biochemia Medica, 2013

2,488 citations recorded by the source corpus.

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 significance 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 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, 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 significant results.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/61b6fb4ae3879598b8bf281f9ed57c608806d8e9

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Citations in the corpus

Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine (2023, methodology): “The proposed feature selection model uses four distinct algorithms: (I) Recursive Feature Elimination (RFE) 24 , (II) Pearson Correlation 25 , (III) Chi-Square Test 26 , and IV) Analysis of Variance (ANOVA) 27 .”
Engineering stress culture: Relationships among mental health, engineering identity, and sense of inclusion (2021, methodology): “Cramer's V (φc) was used to measure effect size for the chi-square tests (McHugh, 2013).”
Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition (2018, methodology): “and a set of “real” glasses), we tested whether one is more inconspicuous via the χ(2) test of independence [37], conservatively corrected for multiple comparisons using the Bonferroni correction.”
Resting-State Retinotopic Organization in the Absence of Retinal Input and Visual Experience (2015, background): “Thus, a large 2 value suggests that group membership influences the distribution of values obtained for that parameter (McHugh, 2013).”