Open Collections

False positives in multiple regression : highlighting the consequences of measurement error in the independent variables

University of British Columbia

Type I error rates in multiple regression, and hence the chance for false positive research findings in the literature, can be drastically inflated when the analyses include independent variables measured with error. Although the bias caused by random measurement error in multiple regression is widely recognized, there has been little discussion of the impact on hypothesis tests outside of the statistical literature. The primary purpose of this thesis is to raise awareness of the problem among methodologists and researchers by demonstrating, in a non-technical manner, the nature and extent of the inflation in Type I error rates for educational and psychological research contexts. This thesis uses computer simulations to demonstrate that, for commonly encountered scenarios, the Type I error rate in a multiple regression model where the independent variables are correlated and measured with random error can approach 1.0, even if the nominal Type I error rate is 0.05. Because nearly all quantitative data in educational and psychological research contain some level of random measurement error, and because multiple regression is one of the most widely used data analytic techniques, this problem should be a serious concern for methodologists and applied researchers. The most important factors causing the problem are summarized, and the implications for research and pedagogy are discussed.

Text

2012-08-22

Attribution-NonCommercial-NoDerivs 3.0 Unported

http://hdl.handle.net/2429/43016

Measurement, Evaluation and Research Methodology

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/3.0/