A Multicollinearity and Measurement Error Statistical Blind Spot: Correcting for Excessive False Positives in Regression and PLS

Digital transformation of healthcare during the COVID-19 pandemic: Patients’ teleconsultation acceptance and trusting beliefs

“…The PLS approach, based on variance, was selected for this research because this method is recommended for prediction and identification purposes as indicated by Hair et al (2022) . According to Goodhue et al (2017) , structural equation models using PLS-SEM are often used in IS research. Indeed, in recent years, the PLS method has been widely used in ranked journals.…”

Predicting mental health treatment access in high-symptom adolescents: Machine learning approaches

“…Random forests and elastic nets are also considerably more robust to the inclusion of highly correlated predictors (e.g., multiple indices of adolescent SES; co-occurring depressive symptoms; Breiman, 2001;Zou & Hastie, 2005) than more commonly-used linear regressions and ANOVAs (Goodhue et al, 2017). Indeed, including highly-correlated predictors in typical regressions and ANOVAs increases risk of false-positives (Kalnins, 2018).…”

Polynomial Regression and Measurement Error

“…Second, in the case of multiple independent variables that are correlated, measurement error in one independent variable leads to bias in all regression coefficients but determining the magnitude or even the direction of bias is difficult (e.g., Wooldridge, 2013, p. 322). This is highlighted in a recent study by Goodhue et al (2017), who demonstrated a scenario where measurement error leads to false positive findings.…”

“…While both the methodological literature (e.g., Edwards & Parry, 1993) and applied IS research (e.g., Venkatesh & Goyal, 2010) have noted that polynomial regression assumes that the independent variables are measured without error, and that violating this assumption will lead to biased estimates, the consequences of violating this assumption have not been carefully examined in IS research. This is somewhat surprising given that the biasing effects of measurement error on regression estimates are wellknown (Goodhue, Lewis, & Thompson, 2017) and a number of procedures to address the issue in a linear model context exist (Dijkstra & Henseler, 2015;Rönkkö, McIntosh, & Aguirre-Urreta, 2016). What appears less well understood is that this effect is much greater in the case of polynomial regression models and that measurement error can influence not only the magnitude (and possibly sign) but also the shape of the relationship.…”