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Regression assumptions in clinical psychology research practice—a systematic review of common misconceptions
Author(s) -
Anja F. Ernst,
Casper J. Albers
Publication year - 2017
Publication title -
peerj
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.927
H-Index - 70
ISSN - 2167-8359
DOI - 10.7717/peerj.3323
Subject(s) - meta regression , linear regression , regression analysis , regression toward the mean , normality , transparency (behavior) , econometrics , regression diagnostic , regression , psychology , statistics , computer science , actuarial science , meta analysis , medicine , polynomial regression , mathematics , social psychology , economics , computer security
Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking.

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