A MULTICOLLINEARITY AND MEASUREMENT ERROR STATISTICAL BLIND SPOT: CORRECTING FOR EXCESSIVE FALSE POSITIVES IN REGRESSION AND PLS

• 成果类型: Article
• 署名作者: Goodhue, Dale L.; Lewis, William; Thompson, Ron
• 署名单位: University System of Georgia; University of Georgia; Wake Forest University
• 刊物名称: MIS QUARTERLY
• ISSN/ISSBN: 0276-7783
• 发表日期: 2017
• 页码: 667-+
• 关键词: STRUCTURAL EQUATION MODELS; common beliefs; squares; collinearity; RETHINKING; variables; POWER

摘要

Multiple regression has a previously unrecognized statistical blind spot because when multicollnearity and measurement error are present, both path estimates and variance inflation factors are biased. This can result in overestimated t-statistics, and excessive false positives. PLS has the same weakness, but CB-SEM's estimation process accounts for measurement error, avoiding the problem. Bringing together partial insights from a range of disciplines to provide a more comprehensive treatment of the problem, we derive equations showing false positives will increase with greater multicollinearity, lower reliability, greater effect size in the dominant correlated construct, and, surprisingly, with higher sample size. Using Monte Carlo simulations, we show that false positives increase as predicted. We also provide a correction for the problem. A literature search found that of IS research papers using regression or PLS for path analysis, 33% were operating in this danger zone. Our findings are important not only for IS, but for all fields using regression or PLS in path analysis.