Chin and Newsted (1999) present a Monte Carlo simulation study on PLS with small samples. A rule of thumb for robust PLS path modeling estimations suggests that the sample size be equal to the larger of the following (Barclay, Higgins, & Thompson, 1995): (1) ten times the number of indicators of the scale with the largest number of formative indicators, or (2) ten times the largest number of structural paths directed at a particular construct in the inner path model. Wold (1989) illustrates the low sample size requirement by analyzing a path model based on a data set consisting of 10 observations and 27 manifest variables. For example, ‘‘there can be more variables than observations and there may be a small amount of data that are missing completely at random’’ (Tenenhaus et al., 2005, p. In contrast, the sample size can be considerably smaller in PLS path modeling.
Moreover, hypothesis building and the assessment of CBSEM results through global goodness-of-fit parameter estimation) between concepts is of primary concern. This occurrence, of course, is not of concern in theory testing where structural relationships (i.e. Yet, due to the indeterminacy of factor score estimations, there is a loss of predictive accuracy.
PLS is primarily intended for causal-predictive analysis in situations of high complexity but low theoretical information.’’ The philosophical distinction between these approaches is whether to use CBSEM for theory testing and development, or PLS path modeling for predictive applications., in causal modeling situations where prior theory is strong and further testing and development is the goal, CBSEM is the most appropriate statistical methodology. 270) ‘‘ML is theory-oriented, and emphasizes the transition from exploratory to confirmatory analysis.
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