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The data form a 2x4 frequency matrix and the ratio of each cell represents the result. Each participant's rating is a birth order category (1, 2, 3, 4). The Mann-Whitney test can assess the significance of differences between groups.
The chi-square test of independence determines whether the proportions are significantly different for each birth order group. Effect sizes are measured in Cramer's V. Alternatively, the data form two sets of ratings, one for each personality group. Each participant's rating is a birth order category (1, 2, 3, 4). The Mann-Whitney test can assess the significance of differences between groups.
- Chi- Square Test:
The chi-square test (also called chi-square or chi-square test) is a statistical hypothesis test used in contingency table analysis when the sample size is large. Simply put, the main purpose of this test is to test whether two categorical variables (the two dimensions of the contingency table) independently affect the test statistic (the values in the table) . The test is valid when the test statistic is a chi-square distribution based on the null hypothesis, specifically Pearson's chi-square test and variants thereof. Use Pearson's chi-square test to determine whether there is a statistically significant difference between the expected and observed frequencies for one or more categories in the contingency table. For contingency tables with small sample sizes, Fisher's exact test is used instead.
- Mann - Whitney U Test:
In statistics, the Mann-Whitney U test (also known as the Mann-Whitney-Wilcoxon (MWW/MWU), Wilcoxon rank sum test, or Wilcoxon-Mann-Whitney test) is a test of the null hypothesis used at random. It is a non-parametric test. Values X and Y selected from two populations where the probability of X being greater than Y is equal to the probability of Y being greater than X.
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