2.5. Statistical Analysis
INTERACTION
A data analysis was conducted using Statistical Package for Social Sciences (SPSS) version 25.0 software. The significance level of this study was set at α = 0.05. We employed a combination of parametric and non-parametric tests to assess the mean differences between two groups, selecting the appropriate test based on the normality distribution of the data. For statistical inference, the non-parametric equivalents of independent The dimensions of the “quality of life” questionnaire (“Family Interaction”, “Parenting”, “Emotional Wellbeing”, “Physical/Material Wellbeing” and “Disability-Related Support”) and “parenting style” questionnaire (“Authoritative Parenting Style”, “Authoritarian Parenting Style” and “Permissive Parenting Style”) were calculated, with the control and exclusion of cases from the sample that did not meet the criteria for correct completion.Specifically, the analysis included the following steps: Comparisons of dimensions of “quality of life” and dimensions of “parenting style” scores were implemented, before and after the intervention, mainly for the intervention group, using paired Comparisons of “quality of life” and “parenting style” scores were implemented before the intervention between the intervention and the control group. We used an independent samples First, we determine the normality of the data distribution using the Shapiro–Wilk test [In our study, we consider the intervention phase as the factor, specifically whether measurements were taken pre-intervention or post-intervention. A significant factor indicates an effect on both the central tendency and variability of the dependent variable. The direction of this effect (positive or negative) can be inferred from mean value plots based on the two intervention stages.In cases where the observed results deviate from our expectations, either due to unexpected directions or a lack of statistical significance, we employ a cluster analysis methodology. This involves cluster analysis in two stages: First, we classify subjects as having either successful or unsuccessful interventions based on a new variable, “intervention”, indicating a success post-intervention and failure post-intervention. The second stage employs an algorithm to reclassify subjects into clusters based on their proximity to the cluster means. This enables the reclassification of subjects from “successful” to “unsuccessful” intervention clusters if their control variable values are closer to the “unsuccessful” cluster’s mean. In our study, the results of the analysis showed that there was an increase in all dimensions after the intervention, but they were not statistically significant at the 95% level of significance. That was our basic motivation: to examine cluster analysis for our sample (intervention group) in order to classify cases with a statistically significant improvement after the intervention. More precisely, in our analysis, we employed an embedded algorithm, offered through statistical software (SPSS 25.0), which classifies cases into clusters. The basic steps for the algorithm classification process were as follows: firstly, cases with common characteristics (in our case “dimensions’ results”) are selected, and secondly, cases in groups (here, we selected 2 groups) are classified using the Euclidean distance (there are many alternative metrics) between the examined cases and the previously created clusters as a metric. This algorithm resulted in the classification of the intervention group into 2 groups (70% of cases were successful, and 30% of cases were unsuccessful).