Beyond Traditional Quantitative Research Methods: Applying G*Power and WebPower for Sample Size Determination and Statistical Power Analysis in Tax Morale Studies in Malaysia
DOI:
https://doi.org/10.53840/e-jpi.v13i3.445Abstract
Determining an adequate sample size and evaluating statistical assumptions are fundamental steps in ensuring the validity and reliability of quantitative research instruments. Despite their importance, many social science studies continue to rely on conventional rules of thumb rather than evidence-based statistical approaches for research methods. This paper demonstrates how G*Power and the WebPower online platform were used as complementary methodological tools for sample size determination, statistical power analysis, and multivariate normality assessment, based on a tax morale study involving Malaysian taxpayers as the unit of analysis. Using G*Power, a priori power analysis was performed based on five predictors specified in the research model. The analysis recommended a minimum sample size of 92 respondents to achieve the desired statistical power, providing empirical justification for the study’s sampling strategy. The actual data collected comprised 321 respondents, substantially exceeding the minimum sample size requirement and thereby enhancing the robustness of the statistical analysis. Although Partial Least Squares Structural Equation Modelling (PLS-SEM) can produce reliable estimates with relatively small sample sizes, larger samples are expected to improve estimation precision and stability. To assess the distributional characteristics of the data before model estimation, multivariate normality was examined using the WebPower online platform based on Mardia's multivariate skewness and kurtosis statistics. The results indicate significant multivariate skewness (β = 9.46, p < 0.01) and multivariate kurtosis (β = 107.44, p < 0.01), suggesting that the data did not satisfy the assumption of multivariate normality. These findings support the use of variance-based Structural Equation Modeling through SmartPLS, which is well-suited for analysing non-normally distributed data. This paper highlights the value of integrating G*Power and WebPower into quantitative research design as contemporary methodological tools for evidence-based sample size determination, statistical power analysis, and assumption testing. The proposed approach enhances methodological rigour and provides practical guidance for researchers conducting quantitative studies in taxation and the broader social sciences.
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