# Comparative Analysis of MoCA and DigiMoCA Test Results: A Pilot Study
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were carried out using SPSS Statistics 24 software (International Business Machines, S.A., Madrid, Spain). Descriptive statistics were obtained for each of the sociodemographic variables included in the study, and also for the MoCA, CDT, and DigiMoCA scores. An analysis of variance (ANOVA) was performed on the three tests' scores to estimate the effect size through the \(\eta^{2}\) statistic. Descriptive statistics provided a clear and concise overview of the data, allowing for initial observations about the central tendencies and variability within each group. The ANOVA extended this analysis by testing for statistically significant differences between the means of the different groups and provided data to estimate the effect size. A Kolmogorov normality analysis [24] was conducted to assess the suitability of the distributions of the variables under study. Non-parametric Kruskal-Wallis [25] and Mann-Whitney U [26] tests were performed to compare groups in the absence of normality and homogeneity of variances assumptions. The Kruskal-Wallis statistic was applied in scenarios involving three or more groups, while the Mann-Whitney U test was used for comparisons between two groups. The Kolmogorov-Smirnov normality analysis, Kruskal-Wallis, and Mann-Whitney U tests were deemed appropriate for this study due to the specific characteristics of the data and the research question addressed. The Kolmogorov-Smirnov test helped us to assess whether the distribution of the variables deviated from a normal distribution, ensuring the correct application of statistical methods. Given that our data did not meet the assumptions of normality and homogeneity of variances, non-parametric tests were necessary. The Kruskal-Wallis test was suitable for comparing more than two groups, as it evaluates whether there are statistically significant differences in the median scores across multiple independent groups without assuming a normal distribution. Similarly, the Mann-Whitney U test was considered appropriate for comparing groups and determining if their distributions differed significantly. These non-parametric tests are robust against violations of normality and provided reliable results for comparing group differences. Principal Component Analysis (PCA [27]) was employed with the variables collected through the DigiMoCA. This method was utilised to reduce the dimensionality of the data and uncover underlying patterns among the variables of the digital test, facilitating subsequent correlation analyses. The variance explained by each component was examined, and a Scree plot was generated to visualise the optimal number of components to retain. A rotated component matrix was computed to identify relationships among variables and summarise the information into a more manageable set of principal components. Finally, Spearman correlation analyses [28] were conducted between the original variables and the factors extracted from the PCA, providing a detailed insight into the relationships and associations observed in the dataset. Since PCA reduces the dimensionality of the dataset and identifies underlying factors, it was considered key to understand how these factors correlate with the original variables. Additionally, the Spearman correlation, being a non-parametric method, is robust to outliers and non-normal distributions, making it well suited for diverse data characteristics. This analysis provided valuable insights into the associations between the extracted factors and the original variables, revealing patterns and relationships that inform the interpretation of the PCA results and enhance the overall understanding of the dataset. The confidence interval defined for the calculated statistics was 95% in all cases.