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Chi-Square Analysis - Parametric vs Non-parametric

Chi-square tests are nonparametric tests that examine nominal categories as opposed to numerical values. Think of a situation where you may want to transform numerical scores into categories. Give a specific example of a situation where the categories are more informative than the actual values.
Use an example of a hypothesis from a parametric test that you used earlier in this course, for instance an analysis of variance (ANOVA) or a t-test. Explain the changes that would be needed so that you could analyze the hypothesis using a chi-square test. For instance, rather than looking at test scores as a range from 0 to 100, you could change the variable to low, medium, or high. What advantages and disadvantages do you see in using this approach? Which is the better option for this hypothesis—parametric approach or nonparametric approach?


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When looking at Chi-square tests, it's always imperative to think of them as the analysis of "categorical" variables (in essence, groupings of values, such as Gender, Colour etc.). In Political/Social science, this follows the most logical approach to ...

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This response deals with the definition and application of chi-square analysis in the context of political/social science.