Skip to main content

Correct Method of Calculating Minimum Sample Size in Quantitative Research: Statistical Power Analysis vs. Slovin's Formula

By

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.

explorethevisayas | This WordPress.com site is the bee's knees

 

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 is through Statistical Power Analysis. Therefore, it is essential to learn Statistical Power Analysis. Do not insist on using Slovin's Formula.

If you are using PLS-SEM for your study and want to know the concepts and guidelines on determining the minimum sample size, please visit this guide on PLS-SEM sample size requirements.

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

Box-Cox Transformation Using SPSS: A Practical Approach to Normalizing Skewed Data

By
  This is under construction. Abstract Skewed data distributions can violate key assumptions of parametric statistical techniques, potentially compromising the validity of research findings. One effective remedy is the Box-Cox transformation, a family of power transformations designed to normalize data and stabilize variance. This tutorial provides a clear, step-by-step guide for applying the Box-Cox transformation using SPSS, focusing on a user-friendly approach accessible to researchers with minimal programming background. The procedure involves ranking cases using fractional ranks, computing the mean and standard deviation of the original variable, and generating a normally distributed variable through SPSS's inverse normal function. Practical examples and detailed instructions are provided to facilitate implementation. This paper aims to support researchers in improving the statistical robustness of their analyses by addressing skewness through an accessible and replicable tran...
Post a Comment
Read more