"The statistical analysis for this study was conducted using IBM SPSS Statistics (Version 25.0; IBM Corp., Armonk, NY). This software package was selected due to its widespread acceptance in social science research, robust data management capabilities, and comprehensive suite of parametric and non-parametric statistical tests."
SPSS 25.0 includes advanced features:
All data cleaning, transformation, and statistical analyses were performed using IBM SPSS Statistics for Windows, Version 25.0 (IBM Corp., 2017). Descriptive statistics (means, standard deviations, frequencies) were computed for demographic and primary outcome variables. Prior to inferential testing, assumptions of normality (Shapiro-Wilk test, ( p > .05 )) and homogeneity of variance (Levene’s test) were assessed. A paired-samples t-test was conducted to compare pre- and post-intervention scores, utilizing SPSS v25.0’s Bayesian option to provide evidence for the null hypothesis where appropriate. For all frequentist tests, the alpha level was set at ( \alpha = .05 ). Missing data were handled using the multiple imputation procedure (fully conditional specification) available in SPSS v25.0, generating 20 imputed datasets for analysis. spss version 25.0
If you want to create a non-standard visualization or a unique data manipulation, SPSS can be frustrating. It excels at standard analyses but fights you when you try to think outside the box. Python integration exists, but it is clunky compared to native Python environments. "The statistical analysis for this study was conducted