Statistical Modeling, Analytics, and Research Issues

Convergent validity assessment in PLS-SEM: A loadings-driven approach The article below explains how to conduct a convergent validity assessment in the context of structural equation modeling via partial least squares (PLS-SEM) using WarpPLS software. Amora, J. T. (2021).  Convergent validity assessment in PLS-SEM: A loadings-driven approach .  Data Analysis Perspectives Journal , 2(3), 1-6. Abstract: Assessment of convergent validity of latent variables is one of the steps in conducting structural equation modeling via partial least squares (PLS-SEM). In this paper, we illustrate such an assessment using a loadings-driven approach. The analysis employs WarpPLS, a leading PLS-SEM software tool. Download the PDF here :  Amora_2021_DAPJ_2_3_ConvergentValidity.pdf  Enjoy reading!

On the Minimum Sample Size Requirement in PLS-SEM The minimum sample size required for conducting Partial Least Squares Structural Equation Modeling (PLS-SEM) is influenced by several factors. These factors include the complexity of the research model, the number of latent variables and indicators utilized, the magnitude of relationships between the latent variables, the desired level of statistical power, and the desired level of significance. Recently, Kock & Hadaya (2018) developed two formulas for determining the minimum sample size in PLS-SEM: the inverse square root method and the gamma exponential method. In these two formulas, the minimum sample size requirement in PLS-SEM depends on the minimum absolute significant path coefficient in the model, statistical power, and level of significant. In practice, researchers want to determine the minimum sample size before the data analysis and/or after the data analysis. A. Minimum sample size before data analysis According to Koc...

Regression Analysis versus Structural Equation Modeling (SEM):  What are the consequences if regression analysis is used instead of SEM when the variables are latent?   Regression analysis and Structural Equation Modeling (SEM) are both statistical techniques used to analyze relationships between variables. However, they differ in the types of variables they can analyze. Regression analysis is suitable for analyzing relationships between observed variables, while SEM is appropriate for analyzing relationships between both observed and latent variables. If regression analysis is used instead of SEM when the variables are latent, the consequences can be significant. Some of the possible consequences include: 1.   Misspecification of the model: Regression analysis assumes that all variables are observed, which means that latent variables are not accounted for. This can result in misspecification of the model, leading to biased and unreliable results. 2.  ...

 Regression analysis versus Path analysis: What are the consequences if regression analysis is used instead of path analysis? Regression analysis and path analysis are both statistical techniques used in research to understand the relationships between variables. However, they differ in their underlying assumptions and the types of questions they can answer. Regression analysis is a statistical technique used to examine the relationship between a dependent variable and one or more independent variables. It is generally used to make predictions about the value of the dependent variable based on the values of the independent variables. Regression analysis assumes that all variables are measured without error, and that there are no unmeasured variables that affect the relationship between the variables being examined. Path analysis, on the other hand, is a more complex statistical technique that allows researchers to examine the direct and indirect effects of variables on each other. ...