Statistics Corner: Chi-squared Test
Kamal Kishore, Vidushi Jaswal
Abstract
CORNERbetween the levels of designation and statistical anxiety.To further clarify, the "faculty, research staff, and students" are the three levels of designation (independent variable), and mild, moderate, and high statistics anxiety is the outcome variable in the study. DisclaimerFor demonstration, Excel® was used to generate the data for the analysis.However, the SATS-36 questionnaire genuinely assesses statistics anxiety. Chi-squared Test (χ 2 )Chi-square is a test of significance when the dependent variable is nominal-it does not tell the strength of the association.Further, the order of categories does not affect Chi-square-it is affected only by differences between groups.The Chi-squared test can be extended to interval or ratio data that researchers collapse into ordinal categories.The distribution of Chi-square is continuous, whereas the test applies to nominal data.As continuous distribution IntroductIonHealth researchers frequently collect nominal variables such as recovered (yes vs no) or diseased (yes vs no) in routine investigations.The t-test and Wilcoxon-Mann-Whitney test discussed in previous articles are ideal for continuous or ordinal outcome variables; however, the same does not apply to nominal data analysis. 1,2Karl Pearson proposed the Chi-squared test in 1900 to analyze nominal data. 3The test has become one of the most popular non-parametric tests due to ease of understanding and calculation.The results from the Chi-squared test are not valid when the sample size is smallthe extensions or alternatives are proposed by various researchers.This manuscript will extend the discussion to analyze, report, and interpret study findings from two independent groups discussed in the previous articles. 1,2The current article will discuss-(1) the problem statement, (2) the Chi-squared test, (3) the types of Chi-squared tests, (4) post hoc Chi-squared tests, (5) the strength of association, and (6) the interpretation and reporting of study findings.We will begin by framing a research question.All data analysis was conducted using R Commander (Rcmdr)-a graphical user interface for free, open-source, and command-driven R software.