Spss 22.0 for mac

Values are given as mean (standard deviation [SD]). Students’ T- tests and one-way Anova tests were applied to compare mean CRP serum levels and clinico-pathological findings. P-values of <0.05 were considered statistically significant. Survival probabilities were calculated by the product limit method of Kaplan and Meier. Differences between groups were tested using the log-rank test. The results were analysed for the endpoint of OS. Survival times of patients, that were still alive at the last follow up visit, were censored with the last follow-up date. Univariate and multivariable Cox regression models for OS were performed, comprising tumor stage (FIGO I vs. II—IV), histological grade (G1-G2 vs. G3), patients' age (< 48.6 vs. ≥ 48.6 years), tumor size (<5 vs. 5–10 vs. >10 cm) and median CRP serum levels (< 3.46 vs. ≥3.46 mg/dL). Statistical analysis was performed by use of the commercially available statistical software SPSS 22.0 for MAC (SPSS 22.0, IBM Inc., Armonk, NY).

This study employed the SPSS 22.0 for Mac (IBM Corp., Armonk, NY, USA) for data analysis. Descriptive statistics were used to reveal the participants’ demographic characteristics and descriptive results of the solitude capacity scale, personality trait scale, and physical and mental health scale. Then, Pearson’s correlation analysis was conducted to explore the correlations between the solitude capacity scale, the personality trait scale, and the physical and mental health scale. Finally, multiple regression analysis was used to verify the correlations between the solitude capacity scale, the personality trait scale, and the physical and mental health scale. In the multiple regression models, the overall score for the solitude capacity scale and the scores for the respective subscales were used as the dependent variables, and the scores for the personality trait scale and the physical and mental health scale were used as the independent variables. All demographic variables were controlled. In addition, multicollinearity diagnostics were conducted for each multiple regression model; the variance inflation factors for independent variables in all regression models were less than 10, meaning that multicollinearity could be disregarded [37 (link)].

In this study, data analysis was conducted using SPSS 22.0 for Mac (IBM Corp., Armonk, NY, USA). First, descriptive statistics were presented for the demographic data collected from the questionnaires. Subsequently, the correlations between RIIS, IIIS, SABAS, BSMAS, and IGDS9-SF were analyzed using Pearson correlation coefficient analysis. Finally, multiple linear regression analysis was used to verify the correlations among RIIS, IIIS, SABAS, BSMAS, and IGDS9-SF. The total score and subscale scores for both RIIS and IIIS were constructed into the multiple regression model as a dependent variable, and the total scores for BSMAS, SABAS, and IGDS9-SF were constructed, respectively, as an independent variable; all demographic variables were simultaneously adjusted in the regression models. Significance was indicated by a p-value of 0.0024 (0.05/21) for multiple linear regression analysis (0.05 for Pearson correlation coefficient analysis), derived after the adoption of Bonferroni Adjustments.