Analysis of Normality Test and Homogeneity Test in Quantitative Research

Normality and homogeneity testing constitutes a fundamental prerequisite in quantitative data analysis, particularly in studies employing parametric statistical procedures. This study aims to examine the role, criteria, and implementation of normality and homogeneity tests as essential assumptions for valid statistical inference in quantitative research. The normality test is conducted to determine whether the distribution of research data follows a normal pattern, which is a critical requirement for applying parametric analyses such as linear regression, Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA), and Multivariate Analysis of Variance (MANOVA). In this study, the Shapiro–Wilk test was applied at a significance level of 5% (α = 0.05), where a significance value greater than 0.05 indicates normally distributed data, while a value less than 0.05 indicates non-normal distribution. In addition, data homogeneity was examined using the Levene test to assess the equality of variances across groups. At the same significance level (α = 0.05), a significance value equal to or greater than 0.05 indicates homogeneous variance, whereas a value below 0.05 suggests heterogeneity. Both tests were performed using SPSS software through the Analyze → Descriptive Statistics → Explore procedure to ensure analytical accuracy and replicability. The findings emphasize that fulfilling the assumptions of normality and homogeneity is essential to justify the use of parametric statistical tests and to enhance the reliability and scientific rigor of quantitative research outcomes. Consequently, a clear understanding and correct application of these tests enable researchers to make appropriate methodological decisions and draw valid conclusions from empirical data.