Skip to main content

Assessing the Validity of Formative Latent Variables in PLS-SEM

By

 Assessing the Validity of First-Order and Higher-Order Formative Latent Variables in PLS-SEM


The article below explains how to conduct a validity assessment of formative latent variables in the context of structural equation modeling via partial least squares (PLS-SEM) using WarpPLS software.

Amora, J. T. (2023). On the validity assessment of formative measurement models in PLS-SEM. Data Analysis Perspectives Journal, 4(2), 1-7.


Abstract:

Structural equation modeling via partial least squares (PLS-SEM) is the preferred approach when a research model includes formative measurement models. In this paper, the validity assessment of first-order and higher-order measurement models is illustrated using real data employing the WarpPLS, a prominent software tool for PLS-SEM.

Comments

Post a Comment

Popular posts from this blog

On the Minimum Sample Size Requirement in PLS-SEM

By
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...
Post a Comment
Read more

Testing the Validity of Reflective and Formative Latent Variables in PLS-SEM Using WarpPLS

By
Testing the Validity of Reflective and Formative Latent Variables in PLS-SEM Using WarpPLS PLS-SEM is typically analyzed and interpreted in three sequential stages. The process begins with the analysis of the measurement model , which focuses on assessing the validity and reliability of the model. This stage is followed by the examination of model fit and quality indices . The final stage involves analyzing the structural model , which examines the relationships among latent variables used to address research hypotheses, including direct effects, indirect effects, and moderating effects. For guidance on the validity assessment of reflective latent variables using WarpPLS, refer to Amora (2021) . For the validity of formative latent variables, including both first-order and higher-order latent variables, consult Amora (2023) .   References: Amora, J. T. (2021). Convergent validity assessment in PLS-SEM: A loadings-driven approach. Data Analysis Perspectives Journal, 2(3), 1-6. h...
Post a Comment
Read more

Convergent validity assessment in PLS-SEM: A loadings-driven approach

By
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!
Post a Comment
Read more