Cribbie, Robert AFarmus, Linda Sawa Dorota2019-11-222019-11-222019-072019-11-22http://hdl.handle.net/10315/36740Statistical 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.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Quantitative psychology andElectronic Thesis or Dissertation2019-11-22statistical suppression; Lord''s Paradox; regression artifacts