The Chi-Square test is a statistical method used to determine if there is a significant association between categorical variables or if a categorical variable follows a hypothesized distribution. There are two main types of Chi-Square tests: the Chi-Square Test of Independence and the Chi-Square Goodness of Fit Test. The Chi-Square Test of Independence assesses whether there is a significant relationship between two categorical variables, while the Goodness of Fit Test evaluates if a single categorical variable matches an expected distribution (Scribbr, n.d.; Statology, n.d.). When reporting Chi-Square test results in APA format, it is essential to specify the type of test conducted, the degrees of freedom, the sample size, the chi-square statistic value rounded to two decimal places, and the p-value rounded to three decimal places without a leading zero (SocSciStatistics, n.d.; Statology, n.d.). For example, a Chi-Square Test of Independence might be reported as follows: “A chi-square test of independence was performed to assess the relationship between gender and sports preference. The relationship between these variables was significant, $$ \chi^2(2, N = 50) = 7.34, p = .025 $$” (Statology, n.d.).
Citations:
[1] https://www.socscistatistics.com/tutorials/chisquare/default.aspx
[2] https://www.statology.org/how-to-report-chi-square-results/
[3] https://ezspss.com/report-chi-square-goodness-of-fit-from-spss-in-apa-style/
[4] https://ezspss.com/how-to-report-chi-square-results-from-spss-in-apa-format/
[5] https://www.scribbr.com/statistics/chi-square-tests/
[6] https://www.youtube.com/watch?v=VjvsrgIJWLE
[7] https://www.scribbr.com/apa-style/numbers-and-statistics/
[8] https://www.youtube.com/watch?v=qjV9-a6uJV0