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

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

Ned Kock (Division of International Business and Technology Studies, Texas A&M International University, Laredo, TX, USA)
Copyright: © 2016 |Pages: 6
DOI: 10.4018/IJeC.2016070101


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.
Article Preview

Illustrative Model And Data

Figure 1 shows an illustrative model that is used in our discussion about using confidence intervals and P values in hypothesis testing. This model contains five latent variables: internal social networking tool use (SN), job satisfaction (JS), organizational commitment (OC), job innovativeness (JI), and job performance (JP).

Figure 1.

Illustrative model representation in WarpPLS. Notes: SN = internal social networking tool use; JS = job satisfaction; OC = organizational commitment; JI = job innovativeness; JP = job performance; notation under latent variable acronym describes measurement approach and number of indicators, e.g., (R)5i = reflective measurement with 5 indicators.


Internal social networking tool use (SN) measures the degree to which an employee uses a social networking tool (e.g., Facebook) made available internally in the employee’s organization to facilitate employee-employee socialization. This is the only exogenous (or independent) latent variable in the model. Job performance (JP), the main endogenous (or dependent) latent variable in the model measures the employee’s overall performance at work.

Three latent variables mediated the indirect relationship between SN and JP. Job satisfaction (JS) measures the degree to which the employee is satisfied with the organization. Organizational commitment (OC) measures the degree to which the employee is personally committed to the organization. Job innovativeness (JI) measures the degree to which the employee engages in innovative or creative behavior at work.

The figure has been created with WarpPLS, and thus employs the software’s notation for summarized latent variable description: the alphanumeric combination under each latent variable’s label (e.g., “JP”) in the model describes the measurement approach used for that latent variable and the number of indicators. For example, “(R)5i” means reflective measurement with 5 indicators.

Complete Article List

Search this Journal:
Open Access Articles
Volume 16: 4 Issues (2020): 1 Released, 3 Forthcoming
Volume 15: 4 Issues (2019)
Volume 14: 4 Issues (2018)
Volume 13: 4 Issues (2017)
Volume 12: 4 Issues (2016)
Volume 11: 4 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing