Partial Least Squares Structural Equation Modeling: An Emerging Tool in Research
What is partial least squares structural equation modeling?
Partial least squares structural equation modeling (PLS-SEM) has gained attention in research and practice across various disciplines such as management, marketing, information systems, medicine, engineering, psychology, political and environmental sciences. PLS-SEM enables researchers to model and estimate complex cause-effects relationship models with both latent (graphically represented as circles) and observed variables (graphically represented as rectangles).
The latent variables embody unobserved (i.e., not directly measurable) phenomena such as perceptions, attitudes, and intentions.
The observed variables (e.g., responses on a questionnaire or secondary data) are used to represent the latent variables in a statistical model.
PLS-SEM estimates the relationships between the latent variables (i.e., their strengths) and determines how well the model explains the target constructs of interest.
The main reasons for the popularity of PLS-SEM are its capability to estimate very complex models and its relaxed data requirements. The most popular applications are the estimation of technology acceptance models (TAM) and American Customer Satisfaction Index (ACSI) models. Each of these models has been published in thousands of different of studies.
PLS-SEM also increased the analytical demands connected with the method. Hence, recent research presented numerous methodological extensions to provide researchers and practitioners with a broad portfolio of technical options to meet their analytical goals. These extensions include, for example, the importance-performance map, mediation, moderation, multi group, latent class segmentation, and predictive analyses
Learn more about partial least squares structural equation modeling
To get to know the PLS-SEM method, the third edition of A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) by Joe Hair, Thomas Hult, Christian Ringle, and Marko Sarstedt, and the second edition of Advanced Issues in Partial Least Squares Structural Equation Modeling by Hair, Sarstedt, Ringle, and Siegfried Gudergan, are practical guides that provide researchers with a shortcut to fully understand and competently use the rapidly emerging multivariate PLS-SEM technique.
While the primer offers an introduction to fundamental topics such as establishing, estimating and evaluating PLS path models and some additional topics such as mediation and moderation, the book on advanced issues fully focuses on complementary analyses such as testing nonlinear relationships, latent class segmentation, multigroup analyses, measurement invariance assessment, and higher-order models. Featuring the latest research, examples analyzed with the SmartPLS 4 software, and expanded discussions throughout, these two books are designed to be easily understood by those want to exploit the analytical opportunities of PLS-SEM in research and practice. There is also an associated website for both books. Use the code COMMUNIT24 for a 25% discount when you order books from Sage, good until December 31, 2024.
Open access journal articles about partial least squares structural equation modeling
Becker, J.-M./ Cheah, J.H./ Gholamzade, R./ Ringle, C.M./ Sarstedt, M.: PLS-SEM’s Most Wanted Guidance, International Journal of Contemporary Hospitality Management, Volume 35 (2023), Issue 1, pp. 321-346.
Cook, D.R./ Forzani, L. On the Role of Partial Least Squares in Path Analysis for the Social Sciences. Journal of Business Research, Volume 167 (2023), p. 114132.
Guenther, P./ Guenther, M./ Ringle, C. M./ Zaefarian, G./ Cartwright, S.: Improving PLS-SEM Use for Business Marketing Research. Industrial Marketing Management, Vol. 111 (2023), pp. 127-142.
Morgeson, F.V./ Hult, G.T.M./ Sharma, U./ Fornell, C.: The American Customer Satisfaction Index (ACSI): A Sample Dataset and Description. Data in Brief, Volume 48 (2023), p. 109123.
Ringle, C.M./ Sarstedt, M./ Sinkovics, N./ Sinkovics, R.R.: A Perspective on Using Partial Least Squares Structural Equation Modelling in Data Articles, Data in Brief, Vol. 48 (2023), p. 109074.
Sarstedt, M./ Ringle, C.M./ Iuklanov, D.: Antecedents and Consequences of Corporate Reputation: A Dataset, Data in Brief, Vol. 48 (2023), p. 109079.
More Sage Research Methods Community posts about quantitative and computational methods
This blog post is the eighth, and final, post in a follow-on to our 2021 “The future of computational social science is Black” series, about a Summer Institute in Computational Social Science organized by Howard University and Mathematica. It continues to bring the power of computational social science to the issues of systemic racism and inequality in America. This marks the third iteration of the successful SICSS model being hosted by a Historically Black College or University.