Spss statistics 27 for macintosh

Respondents and non-respondents were compared to evaluate the representativeness of students who completed the follow-up. Chi-squared tests were used to detect differences in proportions for categorical variables (gender, amount of prior sleep education, baseline PSQI sleeper type, chronotype, shift work completed in the past month), Fisher’s exact test was used where there were cell sizes smaller than 5 (age groups), and independent-samples t-tests were used to test for differences in the means of continuous variables (mark in course, baseline sleep knowledge score, baseline PSQI total score).
For the students who completed the follow-up survey, descriptive statistics were used to illustrate student goal-setting, goal achievement, and sleep attitudes. Paired t-tests were used to compare differences in continuous variables (sleep knowledge score, sleep behaviour questionnaire scores, caffeine intake, hours awake before seeing sunlight, hours spent outdoors, PSQI total score). Wilcoxon signed-rank tests were used to compare differences in categorical variables (Stages of Change question responses, use of blue light filters, PSQI subcomponent scores) before and after the course.
All statistical analyses were carried out using IBM SPSS Statistics 27 for Macintosh (Armonk, NY, USA: IMB Corp).

All values are expressed as number (percentage) or median (first quartile; third quartile). IBM SPSS Statistics 27 for Macintosh (Armonk, NY, USA) was used for statistical analysis. Normality was assessed graphically using Q–Q plots. Group comparison for the univariable analysis was performed using Student’s t-test for continuous variables. For categorical variables, the Chi-squared or Fisher’s Exact test was applied when appropriate. A logistic regression analysis was done to identify independent predictors for postoperative stroke. We selected these clinical covariates and entered them in the model: age over 75, acute type A aortic dissection, dissected supra-aortic vessels, bovine arch, SACP time, cardiopulmonary bypass time, zone 2 distal anastomosis, trilateral cerebral perfusion, preoperative circle of Willis scan, axillary cannulation via Dacron graft, surgery before 2018 and acute preoperative neurological deficit and renal-failure history. The Hosmer–Lemeshow test was used to determine the model’s goodness of fit.

Data were analyzed using R statistical software version 4.0.3 (R Foundation for Statistical Computing), Prism (GraphPad Software Inc.) and IBM SPSS Statistics for Macintosh 27 (Armonk, NY, USA). Categorical variables were expressed as count (percentage) and continuous variables as mean ± standard deviation (SD) or median [25th–75th percentiles]. The Kolmogorov–Smirnov test was used, and histograms and normal-quantile plots were examined to verify the normality of distribution of continuous variables. Differences between groups were assessed using the Chi-square test or Fisher’s exact test for categorical variables and Student’s t-test, or Mann–Whitney U-test for continuous variables, as appropriate. The discriminative ability of each variable or combination to predict cerebral hypoperfusion was evaluated using receiver operating characteristic (ROC) curves with the corresponding area under the curve (AUROC), and sensitivity, specificity, positive (PPV) and negative predictive value (NPV) were computed. For each variable, the optimal predictive threshold was calculated using the Youden’s index. Differences between AUROCs were assessed using the DeLong analysis. Correlations between monitoring variables and CTP data were measured with Pearson’s correlation coefficient. All tests were two-tailed, and statistical significance was set at the 5% level.