Pre and post intervention - choice of statistical test #research #statistics #statisticaltests
Channel: SPM & Research with Dr Rock Britto
Date: 2023-08-25

The choice of a statistical test for pre and post intervention data depends on several factors, including the nature of the data, the research question, and the assumptions underlying the data distribution. Here are some common scenarios and the corresponding statistical tests that you might consider:

Paired Samples, Continuous Data (Normal Distribution):
If you have continuous data from the same individuals before and after an intervention and the data follows a normal distribution, you can use the paired t-test. This test assesses whether the mean difference between pre and post measurements is statistically significant.

Paired Samples, Non-Normal Distribution:
If your paired continuous data does not follow a normal distribution, you might consider using a non-parametric alternative to the paired t-test, such as the Wilcoxon signed-rank test.

Paired Samples, Ordinal Data:
If your data is ordinal (ranked categories) and paired, you could use the Wilcoxon signed-rank test, which is a non-parametric test suitable for non-normally distributed data.

Paired Samples, Categorical Data:
If your data is categorical and paired (e.g., before and after a binary outcome), you can use the McNemar test. This test is often used for 2x2 contingency tables.

Repeated Measures ANOVA:
If you have more than two time points and you're interested in assessing the overall effect of an intervention on a continuous outcome, you might use repeated measures ANOVA. This extends the paired t-test to more than two time points.

Mixed-Design ANOVA:
If you have both repeated measures and between-groups factors (e.g., different interventions applied to different groups), a mixed-design ANOVA might be appropriate.

Linear Regression:
If you want to assess the relationship between a continuous outcome and a continuous predictor, while accounting for pre and post measurements, you might consider a linear regression model with a term for the pre-intervention measurement.

Non-Parametric Methods:
If your data violates the assumptions of parametric tests, non-parametric tests like the Mann-Whitney U test or the Kruskal-Wallis test (for multiple groups) might be more appropriate.
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