Assessing Inner Model

Bootstrapping in SmartPLS

The number of bootstrap samples must be larger than the number of valid observations in the original data set but should be higher; generally, 5,000 bootstrap samples are recommended.

Critical t-values

If t-value is greater than or equal to 1.645, then the relationship is significant at alpha < 0.05.

Bootstrap Confidence Interval

It provides additional information on the stability of a coefficient estimate.

It shows the range into which the true population parameter will fall assuming a certain level of confidence (e.g., 95%).

If a confidence interval for an estimated coefficient does not include zero, then the null hypothesis is rejected, and we assume a significant effect.

The range of the confidence interval provides the researcher with an indication of how stable the estimate is. If the confidence interval of a coefficient is wider, then its stability is lower.

Coefficients of determination (R²)

PLS-SEM aims at maximizing R²of endogenous variable in a path model.

Effect Size (f²)

In addition to evaluating the R² values of all endogenous constructs, the change in the R² value when a specified exogenous construct is omitted from the model can be used to evaluate whether the omitted construct has a substantive impact on the endogenous constructs.

Rule of Thumb

Predictive Relevance

Using PLS for prediction purposes requires a measure of predictive capability.

Suggested approach – Blindfolding.

Wold (1982, p. 30), “The cross-validation test of Stone (1974) and Geisser (1975) fits soft modeling like hand in glove”

Blindfolding in SmartPLS

Guideline:

Make sure the number of observations used in the model estimation divided by the omission distance D is not integer (round number).
Chose D values between 5 and 12.
If Q²> 0 indicate that exogenous variable have predictive relevance for endogenous construct under consideration.
The Q² values of 0.02, 0.15, and 0.35 signify small, medium, and large predictive relevance of certain latent variable, thus explaining the endogenous latent variable under evaluation.

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