Contextualizing Statistical Suppression Within Pretest-Posttest Designs

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Date

2019-11-22

Authors

Farmus, Linda Sawa Dorota

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Abstract

Statistical suppression occurs when adjusting for a variable enhances or substantially modifies the association between a predictor and an outcome. Although many methodologists have discussed this phenomenon, very little work has examined suppression in longitudinal regression models such as the pretest-posttest design. This research addressed this gap with two separate studies. Study One was a literature review that reviewed 80 articles (i.e., those meeting the inclusion criteria) from a variety fields within psychology. Study Two was an analysis of a large longitudinal clinical dataset via 925 statistical models. Both studies revealed consistent results: in approximately 20% of instances suppression effects were observed and were attributable to the inclusion of a pretest measure. Results underscore that controlling for pretest measures when assessing change may be of value, as this may help to clarify associations between predictors and posttest outcomes.

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Quantitative psychology and

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