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!

Statistical power and minimum sample size requirements

The WarpPLS menu option “Explore statistical power and minimum sample size requirements”, available starting in version 6.0, allows you to obtain estimates of the minimum required sample sizes for empirical studies based on the following model elements: the minimum absolute significant path coefficient in the model (e.g., 0.21), the significance level used for hypothesis testing (e.g., 0.05), and the power level required (e.g., 0.80). Two methods are used to estimate minimum required sample sizes, the inverse square root and gamma-exponential methods. These methods simulate Monte Carlo experiments, and thus produce estimates that are in line with the estimates that would be produced through the Monte Carlo method.

Related YouTube video:

Explore Statistical Power and Minimum Sample Size in WarpPLS
http://youtu.be/mGT6-NKUe3E

Article in the Information Systems Journal discussing the methods:
http://onlinelibrary.wiley.com/doi/10.1111/isj.12131/full