2012
DOI: 10.2307/41703490
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Does PLS Have Advantages for Small Sample Size or Non-Normal Data?

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Cited by 517 publications
(392 citation statements)
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“…1 This contrast with regression, where equal weights are normally given to all indicators, and each dependent composite construct and all its predictors are analysed separately using ordinary least squares [34,35]. 2 We performed a multiple regression analysis using both equal weights and factor weights given to all indicators and found results that were consistent with PLS.…”
Section: Model Analysis and Resultsmentioning
confidence: 99%
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“…1 This contrast with regression, where equal weights are normally given to all indicators, and each dependent composite construct and all its predictors are analysed separately using ordinary least squares [34,35]. 2 We performed a multiple regression analysis using both equal weights and factor weights given to all indicators and found results that were consistent with PLS.…”
Section: Model Analysis and Resultsmentioning
confidence: 99%
“…Second, with our complex research model, PLS may have an advantage over regression since it can analyse the whole model as a unit, rather than dividing it into pieces [35]. 1 This contrast with regression, where equal weights are normally given to all indicators, and each dependent composite construct and all its predictors are analysed separately using ordinary least squares [34,35].…”
Section: Model Analysis and Resultsmentioning
confidence: 99%
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“…1 The possibility of testing complex relationships with small samples and parameter estimates can be estimated with the violation of normality assumption (Goodhue et al 2012). 2 For exploratory theory development purpose, PLS-SEM is preferred instead of Covariance based-Structural Equation Modeling (CB-SEM), because CB-SEM is used for conforming the model (Gefen et al 2011).…”
Section: Discussionmentioning
confidence: 99%