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Cited by 1,901Psychometric Methodologies and TestingStatistical Methods and ApplicationsAdvanced Statistical Methods and Models

Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: when to choose it and how to use it

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Lowry, Paul Benjamin, Gaskin, James·2014·IEEE Transactions on Professional Communication
DOI 10.1109/tpc.2014.2312452
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Abstract

Problem: Partial least squares (PLS), a form of structural equation modeling (SEM), can provide much value for causal inquiry in communication-related and behavioral research fields. Despite the wide availability of technical information on PLS, many behavioral and communication researchers often do not use PLS in situations in which it could provide unique theoretical insights. Moreover, complex models comprising formative (causal) and reflective (consequent) constructs are now common in behavioral research, but they are often misspecified in statistical models, resulting in erroneous tests. Key concepts: First-generation (1G) techniques, such as correlations, regressions, or difference of means tests (such as ANOVA or${\rm t}$-tests), offer limited modeling capabilities, particularly in terms of causal modeling. In contrast, second-generation techniques (such as covariance-based SEM or PLS) offer extensive, scalable, and flexible causal-modeling capabilities. Second-generation (2G) techniques do not invalidate the need for 1G techniques however. The key point of 2G techniques is that they are superior for the complex causal modeling that dominates recent communication and behavioral research. Key lessons: For exploratory work, or for studies that include formative constructs, PLS should be selected. For confirmatory work, either covariance-based SEM or PLS may be used. Despite claims that lower sampling requirements exist for PLS, inadequate sample sizes result in the same problems for either technique. Implications: SEM's strength is in modeling. In particular, SEM allows for complex models that include latent (unobserved) variables, formative variables, chains of effects (mediation), and multiple group comparisons of these more complex relationships.

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  • Harman's single-factor procedure showed multiple factors and no single component accounted for 50% or more of the covariance, suggesting absence of common method bias.

    “Last, we used Harman’s single-factor procedure to further test for the presence of CMB for all scale variables in the measurement model. As multiple factors emerged from the principal component factorial analysis of the variables, and as no single component accounted for 50% or more of the covariance among these variables, this further suggests the absence of CMB [37].”

    From Evolving Medical Students’ Digital Health Perceptions and Intentions: Insights From a Prepandemic and Postpandemic Survey Study (2025) · cites this paper as [37]

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Authors
Lowry, P. B., & Gaskin, J.
Affiliations
  • City University of Hong Kong (HK)
  • Brigham Young University (US)
ORCID
Venue
IEEE Transactions on Professional Communication
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2014-06
Volume
57
Issue
2
Pages
123-146
ISSN
  • 0361-1434 (ISSN-L)
  • 1558-1500
DOI
10.1109/tpc.2014.2312452
OpenAlex
W2065771650
Cited by
1,901
References
84
Citations by year
2026: 131 · 2025: 198 · 2024: 196 · 2023: 211 · 2022: 229 · 2021: 201 · 2020: 197 · 2019: 157 · 2018: 151 · 2017: 96 · 2016: 69 · 2015: 53 · 2014: 12
FWCI
96.56
Type
journal article
Language
en
Open access
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Transactions on Professional Communication (publishedVersion)
  • ScholarsArchive (Brigham Young University) (submittedVersion)
  • SSRN Electronic Journal (submittedVersion)
  • The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
Keywords
partial least squares structural equation modeling, formative constructs, causal models, behavioral research, communication research, model misspecification, sample size requirements, mediation, latent variables, covariance-based SEM

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