Contextualizing Statistical Suppression Within Pretest-Posttest Designs

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dc.contributor.advisorCribbie, Robert A
dc.contributor.authorFarmus, Linda Sawa Dorota
dc.date.accessioned2019-11-22T18:49:40Z
dc.date.available2019-11-22T18:49:40Z
dc.date.copyright2019-07
dc.date.issued2019-11-22
dc.date.updated2019-11-22T18:49:39Z
dc.degree.disciplinePsychology (Functional Area: Quantitative Methods)
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractStatistical 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.
dc.identifier.urihttp://hdl.handle.net/10315/36740
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectQuantitative psychology and
dc.subject.keywordsstatistical suppression; Lord''s Paradox; regression artifacts
dc.titleContextualizing Statistical Suppression Within Pretest-Posttest Designs
dc.typeElectronic Thesis or Dissertation

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