Statistical Modeling, Analytics, and Research Issues

  Enhancing Research Capabilities Among Professors in Philippine Universities To enhance the research capabilities of research professors, every university in the Philippines must foster a mindset focused on the following objectives: Deepening Expertise in Research Methodology:  Professors must possess deep knowledge and skills in research, covering both conceptual understanding and practical application in at least one research methodology—quantitative, qualitative, or mixed methods.  Enhancing Proficiency in the Application of Data Analysis Techniques: P rofes sors should demonstrate proficiency in applying data analysis techniques appropriately and commit to constantly upgrading their data analysis toolbox by continuously learning new data analysis techniques. To achieve depth in their expertise, professors should specialize in either qualitative data analysis, quantitative data analysis, or both methodologies, depending on their individual preferences. Many eyebrows ...

In our pursuit of academic and professional excellence, it's vital to remain vigilant against predatory and hijacked journals, as well as other deceptive publishing practices. Predatory and hijacked journals are both fraudulent, employing different methods of deception within the academic publishing landscape.  Predatory journals charge authors fees but lack proper peer review and editorial standards, compromising scholarly integrity. In contrast, hijacked journals imitate legitimate ones. For instance, the "Seybold Report" has an authentic website at https://seyboldreport.net/ and a counterfeit version at https://seyboldreport.org/ . The hijacked version replicates the legitimate journal's style, editorial process, and ISSN, misleading authors and undermining research integrity. The clarivate's blog  has more about hijacked journals. These disreputable entities often disguise themselves as credible scholarly platforms, exploiting the eagerness of researchers a...

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...

  Assessing the Validity of First-O rder 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. Download the PDF here :  Amora_2023_DAPJ_4_2_FormativeAssessment.pdf (scriptwarp.com) Enjoy reading!

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.  ...