Continuous Predictors of Pretest-Posttest Change: Highlighting the Impact of the Regression Artifact
Author(s) -
Linda Farmus,
Chantal A. ArpinCribbie,
Robert A. Cribbie
Publication year - 2019
Publication title -
frontiers in applied mathematics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.31
H-Index - 13
ISSN - 2297-4687
DOI - 10.3389/fams.2018.00064
Subject(s) - regression analysis , covariate , regression toward the mean , regression , psychology , statistics , linear regression , baseline (sea) , econometrics , clinical psychology , mathematics , oceanography , geology
Researchers are often interested in exploring predictors of change, and commonly use a regression based model or a gain score analysis to compare degree of change across groups. Methodologists have cautioned against the use of the regression based model when there are non-random group differences at baseline because this model inappropriately corrects for baseline differences. Less research has addressed the issues that arise when exploring continuous predictors of change (e.g., a regression model with posttest as the outcome and pretest as a covariate). If continuous predictors of change correlate with pretest scores, the modeled relationship between predictors and change may be an artefact. This two-part study explored the statistical artefact that may arise when continuous predictors of change are included in pretest-posttest regression based models. Study 1 revealed that the problematic regression based model is currently being applied in psychology literature more often than non-problematic models, and that the conditions leading to the artefact were met in a significant amount of studies reviewed. Study 2 demonstrated that the artefact arises in conditions common within psychological research, and directly impacts Type I error rates. Recommendations are provided regarding which regression based models are appropriate when pretest scores are correlated with continuous predictors.
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