A Multicollinearity and Measurement Error Statistical Blind Spot: Correcting for Excessive False Positives in Regression and PLS1
研究发现多重共线性与测量误差同时存在时，回归和PLS的路径估计与方差膨胀因子均有偏，导致假阳性过高；CB-SEM因考虑测量误差而避免此问题。通过推导和蒙特卡洛模拟验证，并提供了修正方法。
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.