Statistical package for social sciences spss for windows version 20

Data were double entered into an excel sheet, cleaned, and then analyzed using IBM Statistical Package for Social Sciences (SPSS) for Windows version 20 (SPSS, Inc., Armonk, USA). Data were described using percentages, geometric means, and 95% confidence interval. The egg output data was 10 log-transformed to normalize skewed egg distribution. Geometric means of egg count (GM epg or eggs per 10 ml of urine) were computed for microscopically positive individuals, and intensity of infection was analyzed. The χ-square test was used to evaluate the association between infection status (Sm, Sh, and mixed infection) and disease covariates (sex, age, etc.). The independent-samples t-test was used to compare GM infection intensities with age and sex.

The statistical analyses were performed using the IBM Statistical Package for Social Sciences (SPSS) for Windows, version 20 (SPSS Inc., Chicago, IL, USA). The internal consistency of the scales was calculated using Cronbach’s α coefficient. The normality of the data distribution was assessed using the Kolmogorov–Smirnoff test. Given that all data are not normally distributed, non-parametric tests such as Mann–Whitney and Kruskal–Wallis H tests were used, followed by post hoc testing with Dunn’s pairwise tests to investigate differences in the study variables (adjusted using the Bonferroni correction). Spearman’s correlation was used to examine the relationship between the study variables.
Hierarchical moderated regression analyses were used, with GPA as the criterion variable and with the independent variables added to the model to determine the moderating effects of such independent variables by the change in the strength of the model. The moderator variable (RSE) was added to the second regression model. Then, the interaction term between self-esteem and PRCA was added to the third model. To avoid high multicollinearity with the interaction term, the variables were centered. Next, the interaction term between was added to the regression model. A p value ≤ 0.05 was considered statistically significant.

Data were analysed using IBM Statistical Package for Social Sciences (SPSS) for Windows, version 20.²² For bivariate analysis, we performed Pearson’s chi-square tests to examine significant sociodemographic differences by PPD status, whereas multiple logistic regression analysis was performed using the forced entry method²³ to build the final model. We computed adjusted odds ratios (AORs) and 95% CIs at an alpha level of 0.05. Selection of variables to be included in the final model was guided by the known clinical importance of the variables (based on literature), their contribution score in the model as well as an alpha level of < 0.25 in bivariate analyses.^{24 (link)} Model diagnostics were assessed using: (1) Hosmer and Lemeshow test, χ² (degree of freedom, df = 8) = 7.25, p = 0.51, which is > 0.05 indicating that the model fitted the data well, (2) the pseudo-R², (3) the p-value of the model χ² (both shown in Table 2) and (3) Cook’s deviance values (all < 1) and standardised residuals (all within ± 2.58). We assessed for multicollinearity between the covariates by fitting the same model using the linear regression procedure²³ to obtain tolerance (all < 1) and variance inflation factor (VIF) values (all < 10) indicating the absence of multicollinearity in the final model.