Statistical Modeling, Analytics, and Research Issues

  Conducting Exploratory Factor Analysis: A Concise and Practical Method Johnny T. Amora 1,2 1 De La Salle-College of Saint Benilde 2 Philippine Association of Researchers and Statistical Software Users(PARSSU)   To uncover the underlying factor structure of the scale, the collected data were subjected to exploratory factor analysis. To test the factorability of the scales, the following were examined: inter-item correlations, Kaiser-Meyer-Olkin(KMO) measure of sampling adequacy, Bartlett’s Test of Sphericity, and communalities.  The inter-item correlation coefficients were examined to ensure that most of them are greater than 0.3 (SPSS, 2000).  Subsequently, the KMO for both multiple and individual variables/items were examined.  The values of KMO vary between 0 and 1, where values closer to 1 are better.  This study used the KMO criterion of greater than 0.5 (Field, 2000).   To ensure that the correlation matrix is not an identity matrix...

Suppose a lot of lanzones have been placed in a large tray. If you want to know if they are sweet or not, do you need to count all the lanzones (population)? Not really, right? You just need to taste a sample. The total count of the lanzones (population) is not needed.   It's the same in quantitative research. If you want to test whether there is a relationship between X and Y, or if there is a significant difference between the means of groups A and B, or if there is a significant effect of X on Y, you don't need the population size. As long as the research uses statistical tests like T-test, ANOVA, Chi-square test, Pearson correlation, Regression, SEM, among others, the population size is not a requirement. It is incorrect to use Slovin's formula to calculate the sample size when the research employs these statistical models. Refer to the article titled " On the Misuse of Slovin's Formula" The correct way to calculate the minimum sample size for your study i...

  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...