Between-subjects statistics

Between-subjects statistics are used to compare independent groups on an outcome

Between-subjects statistics are used to compare independent groups on an outcome variable. The groups are independent because participants can only be in one group. This is also known as the statistical assumption of Independence of Observations (IOO), where each participant is only counted once. Between-subjects statistics require some sort of independent group variable (or categorical variable) and an outcome measured at either a categorical, ordinal, or continuous level. The choice of between-subjects statistics depends upon two specific questions.

1. How many independent groups are being compared on an outcome in the between-subjects analysis?

2. What is the scale of measurement of the outcome in the between-subjects analysis?

When asking research questions and answering them with between-subjects statistics, researchers are asking how many independent groups (or levels of a categorical variable) are being compared on the outcome?

How many independent groups are being compared on an outcome in the between-subjects analysis?

Chi-square Goodness-of-fit, one-sample median test, one-sample t-test
One Group
Chi-square to yield unadjusted odds ratios with 95% confidence intervals, Mann-Whitney U test, independent samples or unpaired t-test
Two Groups
Unadjusted odds ratios with 95% confidence intervals with reference category, Kruskal-Wallis test, one-way ANOVA
Three or more Groups
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There are nine primary between-subjects statistics: Chi-square Goodness-of-fit, one-sample median test, one-sample t-test, chi-square, Mann-Whitney U, independent samples t-test, unadjusted odds ratio, Kruskal-Wallis, and one-way ANOVA.