Which type of study is defined as having the independent variable presented or absent during a time period, independent of distribution assumptions?

• A. Nonparametric study
• B. Parametric study
• C. Quasi-experimental study
• D. Crossover study

Answer: (A)

Nonparametric approaches are distribution-free, meaning they do not rely on assumptions about the underlying shape of the data distribution. When the independent variable is presented or withheld during a time period and the analysis is conducted without assuming normality or other specific distributional forms, you’re applying a nonparametric approach. These methods focus on ranks or medians rather than means, which makes them robust to skewed data, outliers, or small sample sizes. In practice, a within-subject design that turns a treatment on in one period and off in another can be analyzed with nonparametric tests such as the Wilcoxon signed-rank test or the Friedman test, which do not require distribution assumptions.

Parametric studies, by contrast, rely on distribution assumptions like normality and homogeneity of variances, using means and variances in the analysis. Quasi-experimental refers to a design issue—lacking random assignment—not to distribution assumptions, and a crossover study describes a design where participants experience multiple conditions across periods, which can be analyzed with either parametric or nonparametric methods depending on whether distribution assumptions are met.