Data : A UNODC Initiative for Family Adjustment and Child Resilience: Outcomes of Adapting and Evaluating the UNODC Strong Families Program in Iran - A Quasi-Experimental Study
Description
All data were entered in Microsoft Excel and analyzed using SPSS (version 27; IBM, Armonk, NY, USA) and Python (version 3.11) with the libraries pandas (v1.5.3), matplotlib (v3.7.1), and pingouin (v0.5.3). SPSS was primarily used for inferential statistical analyses, while Python was utilized for data visualization and supplementary data processing. Plausibility checks were performed to ensure data accuracy, and data completeness was verified prior to analysis. Missing values, primarily from time point 3 (follow-up), were imputed using the mean of the respective variable. Although mean imputation may lead to an underestimation of variance, it was deemed acceptable due to the limited proportion of missing data and the fact that the variability remained relatively high after imputation. The normality of data distribution on the multi-item Likert-type scales was assessed both visually (histogram, normality Q-Q plot, and box plot) and by use of the Kolmogorov–Smirnov test. Continuous variables were presented as mean and standard deviation (SD), and compared using independent samples t-tests. Categorical variables were presented as frequencies and analyzed using the chi-square test. Baseline differences between the control and intervention groups at Pre-test (T1) and Post-test (T2) were assessed using independent samples t-tests to determine if any significant differences existed prior to the intervention. Paired-samples t-tests were also conducted to evaluate within-group changes from Pre-test to Post-test. To compare scores at different time points (T1, T2, and T3), we first examined potential group-by-time interaction effects using a two-way mixed ANOVA with within-subjects and between-subjects factors. Then, we conducted separate repeated measures ANOVAs for the intervention and control groups to assess the effects on each outcome variable. Additionally, we tested key assumptions for ANOVA, including independent variables, outliers and normality, homogeneity of variances, covariances, and sphericity. In case Mauchly’s Test of Sphericity indicated that the assumption of sphericity had been violated, a Greenhouse Geisser correction was used. As part of the assumption checks, homogeneity of variances was tested using Levene’s test Effect sizes (partial η²) were reported for all ANOVA results to provide information on the magnitude of effects. Bonferroni-adjusted post-hoc tests were performed to identify significant pairwise differences between time points while controlling for the risk of Type I error. . Statistical significance level was set at p-value lower than 0.05