Spss ver 24.0 for windows

The sample characteristics were presented as the mean and standard deviation (SD) for continuous variables, and frequencies and percentages were calculated for categorical variables. Both statistical (Kolmogorov–Smirnov test) and graphical methods (normal probability plots) were used to examine the fit to a normal distribution for each continuous variable. Due to their skewed distribution, all dependent variables were log-transformed before to be included in the models. To aid interpretation, the data were back-transformed from the log scale for presentation in the results (Table 1).
Analysis of covariance (ANCOVA) was used to compare means to explore group differences (i.e., NWL vs. NWO), adjusted for age and sex. Finally, the odds ratios (ORs) and 95% confidence interval (CI) for cardiometabolic risk factors were calculated in each group using a multiple logistic regression analysis after adjusting for age and sex (Model 1). The analysis was further adjusted for physical activity, alcohol intake, tobacco status, and healthy diet (Model 2). All p-values presented are two-tailed, and p-values of <0.05 were considered to indicate statistical significance. Statistical analyses were performed with SPSS ver. 24.0 for Windows (SPSS, Inc., Chicago, IL, USA).

In order to confirm significant differences among the experimental groups, CFU assay results were analyzed using the Kruskal–Wallis test, and significant differences among the experimental groups were confirmed using the Mann–Whitney U test and the Bonferroni correction. Results for Ra, VH, FS, and FM of each specimen were subjected to one-way analysis of variance, followed by Tukey's significant difference multiple comparison test. The significance level was set at 0.05. All analyses were performed using SPSS ver. 24.0 for Windows (IBM, NY, USA).

The collected data were analyzed using IBM SPSS ver. 24.0 for Windows (IBM Corp., Armonk, NY, USA). The characteristics of the participants and the schools with which they were affiliated were analyzed with descriptive statistics such as error, percentage, mean, and standard deviation. The characteristics related to the operation of the women’s health nursing clinical practicum were analyzed using frequency and percentage. The most difficult points, solutions, the most positive points in clinical practicums during the spring 2020 semester, and expectations of the Society were categorized. Opinions on establishing an educational platform for women’s health nursing practice and sharing their data were analyzed using frequency and percentage.

Continuous values are presented as means with standard deviations and were compared using the Student t-test; the chi-square or Fisher exact test (as appropriate) was employed to compare categorical variables. Cox’s proportional hazard regression models were used to calculate hazard ratios (HRs) with 95% confidence intervals (CIs). Binary logistic regression was used to identify factors that significantly influenced weight change. All variables with P-values < 0.10 in univariate analysis were entered into multivariate analysis to identify independent risk factors; Along with parameters with p-values < 0.10, variables identified as factors influencing weight change in previous studies or that the authors of this study empirically judged to affect weight change were also entered into the final model.
All analyses were performed using SPSS ver. 24.0 for Windows (IBM Corp., Armonk, NY, USA). P < 0.05 was considered statistically significant.