Abstract:This tutorial explains in detail what factorial validity is and how to run its various aspects in PLS. The tutorial is written as a teaching aid for doctoral seminars that may cover PLS and for researchers interested in learning PLS. An annotated example with data is provided as an additional tool to assist the reader in reconstructing the detailed example.
“…For discriminant validity, the square root of the AVE for each construct should be greater than the inter-construct correlations, and items should load more strongly on their corresponding construct than on other constructs (i.e., loadings should be higher than cross-loadings) (Gefen and Straub, 2005). As shown in Tables I and II, these conditions were met for all constructs.…”
“…For discriminant validity, the square root of the AVE for each construct should be greater than the inter-construct correlations, and items should load more strongly on their corresponding construct than on other constructs (i.e., loadings should be higher than cross-loadings) (Gefen and Straub, 2005). As shown in Tables I and II, these conditions were met for all constructs.…”
“…In a confirmatory factor analysis (CFA) by PLS, convergent validity was shown when a measurement loaded highly if its coefficient was above 0.60 or loaded significantly if its t values are within the 0.05 level of their assigned construct (Gefen and Straub, 2005). Table III shows the factor loadings of the measurement items and t values.…”
Section: Reliability and Validity Of Measurement Itemsmentioning
“…We used PLS to analyze the data based on structural equation modeling techniques (Chin et al, 2003;Gefen and Straub, 2005). There were several reasons for this choice.…”
Section: Partial Least Squares Analysismentioning
confidence: 99%
“…PLS makes fewer demands on the underlying data distribution and sample size, and it is also capable of analyzing both reflective and formative indicators (Chin, 1998b). Because of these advantages, PLS analysis is now commonly used in conducting behavioral systems research and provides a robust way of analyzing survey data (Chin, 1998a;Chin et al, 2003;Gefen and Straub, 2005;Gefen et al, 2000).…”
Section: Partial Least Squares Analysismentioning
confidence: 99%
“…The latent variable correlations and factor loadings were derived in accordance with Gefen and Straub (2005) using SmartPLS and are provided in the Appendix. Reliabilities of individual items were examined by verifying loadings greater than 0.7.…”
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