2012
DOI: 10.17705/1jais.00302
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Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations

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Cited by 2,203 publications
(1,589 citation statements)
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“…According to Kock and Lynn (2012) although vertical collinearity are met, lateral collinearity (predictor-criterion collineraity) may sometimes misled the findings. Thus, Lateral collinearity was assessed with collineraity satatistics VIF.…”
Section: Lateral Collinearity Assessmentmentioning
confidence: 99%
“…According to Kock and Lynn (2012) although vertical collinearity are met, lateral collinearity (predictor-criterion collineraity) may sometimes misled the findings. Thus, Lateral collinearity was assessed with collineraity satatistics VIF.…”
Section: Lateral Collinearity Assessmentmentioning
confidence: 99%
“…In the presence of multicollinearity, the estimate of one variable's impact on dependent variable Y while controlling for the others tends to be less precise than if predictors were uncorrelated [76]. Therefore, detecting any multicollinearity before estimating the parameters becomes necessary.…”
Section: Multicollinearity Detection and Ridge Regression Analysismentioning
confidence: 99%
“…Nevertheless, Kock (2015) demonstrates that even when discriminant validity is satisfactory, common methods bias (CMB) can still be an issue and recommends a full collinearity assessment. Kock and Lynn (2012) recommend an upper variance inflation factor (VIF) threshold of five for SEM models of this type. The highest VIF is 4.20 and we accordingly conclude that CMB is not an issue in our model.…”
mentioning
confidence: 99%