TY - JOUR
T1 - The potentials of partial least squares structural equation modeling (PLS-SEM) in leisure research
AU - Kono, Shintaro
AU - Sato, Mikihiro
N1 - A version of this manuscript has been presented at the 2020 The Academy of Leisure Sciences' Research Institute. The authors would like to thank the two reviewers of the Journal of Leisure Research who provided constructive feedback to strengthen our manuscript.
PY - 2023
Y1 - 2023
N2 - Partial least squares structural equation modeling (PLS-SEM) is a multivariate statistical technique that helps examine complex relationships among a number of variables. Although its use has increased over decades, PLS-SEM remains underutilized in leisure research. The purpose of this methodological paper is to offer a primer on PLS-SEM for leisure researchers and to present a critical review of PLS-SEM’s strengths and limitations, while identifying potential applications of PLS-SEM across different sub-fields and theories in leisure research. Specifically, as to strengths, we discuss PLS-SEM’s sample size requirements, accommodation of formative and reflective measures, ability to model many variables and relationships, and statistical prediction capacity. In terms of its limitations, we review criticisms regarding PLS-SEM’s biased estimates as well as the lack of measurement error estimation and model fit assessment tools. Lastly, we provide recommendations for leisure researchers who wish to use PLS-SEM and journal editors and reviewers who assess PLS-SEM articles.
AB - Partial least squares structural equation modeling (PLS-SEM) is a multivariate statistical technique that helps examine complex relationships among a number of variables. Although its use has increased over decades, PLS-SEM remains underutilized in leisure research. The purpose of this methodological paper is to offer a primer on PLS-SEM for leisure researchers and to present a critical review of PLS-SEM’s strengths and limitations, while identifying potential applications of PLS-SEM across different sub-fields and theories in leisure research. Specifically, as to strengths, we discuss PLS-SEM’s sample size requirements, accommodation of formative and reflective measures, ability to model many variables and relationships, and statistical prediction capacity. In terms of its limitations, we review criticisms regarding PLS-SEM’s biased estimates as well as the lack of measurement error estimation and model fit assessment tools. Lastly, we provide recommendations for leisure researchers who wish to use PLS-SEM and journal editors and reviewers who assess PLS-SEM articles.
KW - Leisure research
KW - measurement
KW - multivariate statistics
KW - partial least squares
KW - structural equation modeling
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U2 - 10.1080/00222216.2022.2066492
DO - 10.1080/00222216.2022.2066492
M3 - Article
AN - SCOPUS:85132350074
SN - 0022-2216
VL - 54
SP - 309
EP - 329
JO - Journal of Leisure Research
JF - Journal of Leisure Research
IS - 3
ER -