Combining composites and factors in PLS-SEM models: A multi-algorithm technique

The article below presents a multi-algorithm technique for combining latent variables estimated as composites or factors into a single model, in the context of structural equation modeling via partial least squares (PLS-SEM).

Kock, N. (2024). Combining composites and factors in PLS-SEM models: A multi-algorithm technique. Data Analysis Perspectives Journal, 5(4), 1-8.

Link to full-text file for this and other DAPJ articles:

https://scriptwarp.com/dapj/#Published_Articles

Abstract:

A multi-algorithm technique is presented for combining latent variables estimated as composites or factors into a single model, in the context of structural equation modeling via partial least squares. The multi-algorithm technique consists of three key steps: selecting composite-based or factor-based outer model analysis algorithms to be used for latent variable estimation; adding the latent variables estimated with the chosen composite-based or factor-based algorithms as new standardized variables; and creating and estimating a final model with the new variables added as single indicators of latent variables.

Best regards to all!

Minimum sample size estimation in SEM: Contrasting results for models using composites and factors

The article below discusses how one can conduct a minimum sample size estimation, contrasting results for models using composites and factors, in the context of structural equation modeling via partial least squares (PLS-SEM).

Ezeugwa, B., Talukder, M. F., Amin, M. R., Hossain, S. I., & Arslan, F. (2022). Minimum sample size estimation in SEM: Contrasting results for models using composites and factors. Data Analysis Perspectives Journal, 3(4), 1-7.

Link to full-text file for this and other DAPJ articles:

https://scriptwarp.com/dapj/#Published_Articles

Abstract:

Estimating the minimum required sample size is an essential issue for studies that use structural equation modeling employing partial least squares (PLS-SEM). Several PLS-SEM-based studies ignore this critical step or use simple techniques, which lead to inaccurate sample size estimations. This paper illustrates two effective heuristic methods to estimate the minimum required sample size using WarpPLS, a leading PLS-SEM software tool.

Best regards to all!

Reliability assessment in SEM models with composites and factors: A modern perspective

The article below puts forth a modern perspective on how one can conduct reliability assessments in models with composites and factors, in the context of structural equation modeling via partial least squares (PLS-SEM).

Canatay, A., Emegwa, T., Lybolt, L. M. & Loch, K. D. (2022). Reliability assessment in SEM models with composites and factors: A modern perspective. Data Analysis Perspectives Journal, 3(1), 1-6.

Link to full-text file for this and other DAPJ articles:

https://scriptwarp.com/dapj/#Published_Articles

Abstract:

This paper’s focus is on reliability tests for both composite-based and factor-based analysis algorithms in structural equation modeling through partial least squares (PLS-SEM). We illustrate this analysis employing a widely used PLS-SEM software tool, WarpPLS. The results show the magnitude of differences between the two approaches, suggesting that the estimates of coefficients obtained using the factor-based approach are more conservative than those obtained using the corresponding composite-based approach.

Best regards to all!

Using indicator correlation fit indices in PLS-SEM: Selecting the algorithm with the best fit

The article below explains how one can use indicator correlation fit indices for selecting the analysis algorithm with the best fit in the context of structural equation modeling via partial least squares (PLS-SEM).

Kock, N. (2020). Using indicator correlation fit indices in PLS-SEM: Selecting the algorithm with the best fit. Data Analysis Perspectives Journal, 1(4), 1-4.

A link to a PDF file is available ().

Abstract:

Upon completion of a PLS-SEM analysis, one can obtain the model-implied indicator correlation matrix and compare it with the actual indicator correlation matrix. The latter is obtained directly from the data being analyzed. Indicator correlation fit indices are quantifications of the differences among these two matrices. Our focus in this paper is on the use of indicator correlation fit indices in PLS-SEM for selecting the analysis algorithm with the best fit.

PLS-SEM with factor estimation (PLSF-SEM): An applied discussion in the field of marketing

The article below (mentioned as forthcoming in a recent post; now published!) explains how one can conduct a factor-based PLS structural equation modeling (PLSF-SEM) analysis, with an illustration in the field of marketing, as well as the advantages of using PLSF-SEM in terms of avoidance of type I and II errors.

Kock, N. (2019). Factor-based structural equation modeling with WarpPLS. Australasian Marketing Journal, 27(1), 57-63.

A link to a PDF file is available ().

Abstract:

Structural equation modeling (SEM) is extensively used in marketing research. For various years now, there has been a somewhat heated debate among proponents and detractors of the use of the partial least squares (PLS) method for SEM. The classic PLS design, originally proposed by Herman Wold, has a number of advantages over covariance-based SEM; e.g., minimal model identification demands. However, that design does not base its model parameter recovery approach on the estimation of factors, but on composites, which are exact linear combinations of indicators. This leads to adverse consequences, primarily in the form of unacceptable levels of type I and II errors. Recently a new factor-based method for SEM has been developed, called PLSF, which we discuss in this paper. This method has the advantages of classic PLS, but without the problems inherent in the use of composites. For readers interested in trying it, the PLSF method is implemented in the SEM software WarpPLS.

Factor-based SEM

There has been a long and in some instances fairly antagonistic debate among proponents and detractors of the use of Wold’s original partial least squares (PLS) algorithms in the context of structural equation modeling (SEM). This debate has been fueled by one key issue: Wold’s original PLS algorithms do not deal with actual factors, as covariance-based SEM algorithms do; but with composites, which are exact linear combinations of indicators. The factor-based SEM algorithms in WarpPLS have been developed specifically to address this perceived limitation of Wold’s original PLS algorithms.

Related YouTube videos:

Conduct a Factor-Based PLS-SEM Analysis with WarpPLS
http://youtu.be/PvXuD5COezU

Use Consistent PLS Factor-Based Algorithms in WarpPLS
http://youtu.be/I5x4SuQHdME