# Research questions lead to choice of statistical design

## Differences between-subjects and within-subjects designs

**YEARS**to wrap my head around some fundamental aspects of statistical reasoning, much less the

**lexicon**. I would hypothesize that

**90% of the statistics reported in the empirical literature as a whole**fall between two different categories of statistics,

**between-subjects and within-subjects**. Here is a basic breakdown of the differences in these types of statistical tests:

1.

__- When you are__

**Between-subjects****comparing independent groups**on a categorical, ordinal, or continuous outcome variable, you are conducting between-subjects analyses. The "between-" denotes the differences between mutually exclusive groups or levels of a categorical predictor variable.

**Chi-square, Mann-Whitney U, independent-samples t-tests, odds ratio, Kruskal-Wallis, and one-way ANOVA**are all considered between-subjects analyses because of the comparison of independent groups.

2.

__- When you are c__

**Within-subjects****omparing THE SAME GROUP on a categorical, ordinal, or continuous outcome ACROSS TIME OR WITHIN THE SAME OBJECT OF MEASUREMENT MULTIPLE TIMES**, then you are conducting within-subjects analyses. The "within-" relates to the differences within the same object of measurement across multiple observations, time, or literally, "within-subjects."

**Chi-square Goodness-of-fit, Wilcoxon, repeated-measures t-tests, relative risk, Friedman's ANOVA, and repeated-measures ANOVA**are within-subjects analyses because the same group or cohort of individuals is measured at several different time-points or observations.