Hypothesis Testing with Confidence Intervals and P Values in PLS-SEM

E-collaboration researchers usually employ P values for hypothesis testing, a common practice in a variety of other fields. This is also customary in many methodological contexts, such as analyses of path models with or without latent variables, as well as simpler tests that can be seen as special cases of these (e.g., comparisons of means). The author discusses here how a researcher can use another major approach for hypothesis testing, the one building on confidence intervals, in analyses of path models with latent variables employing partial least squares structural equation modeling (PLS-SEM). The author contrasts this approach with the one employing P values through the analysis of a simulated dataset, created based on a model grounded on past theory and empirical research. The model refers to social networking site use at work and its impact on job performance. The results of the analyses suggest that tests employing confidence intervals and P values are likely to lead to very similar outcomes in terms of acceptance or rejection of hypotheses.