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231 protocols using spss 24.0 for windows

1

Cognitive Decline and Intervention Outcomes

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Means and standard deviations were calculated for all data, including the percent change for all outcome variables in both groups. All data were checked for normality using Shapiro–Wilk Test. T-test was used to check baseline differences between intervention and control group. Only for analytical purposes the intervention group was treated as having two arms (patients with and without dementia. The intervention group was divided in two: group 1 consisted of older adults with dementia and group 2 consisted of older adults without cognitive disease. Considering the sample size was greater than 30 (Glass et al., 1972; Lumley et al., 2002), a repeated measure analysis of covariance (ANCOVA) was performed for each dependent variable. Gender, age, BMI and baseline value were used as covariates for each variable. Differences between groups over time were determined by significant group X time interactions. Levene’s Test was used to assess homogeneity of variance (p > 0.050) and sphericity was assessed using Mauchly’s test (p > 0.050). If sphericity was violated the Greenhouse–Geisser correction was applied. The Statistical Package for Social Sciences (SPSS 24.0 for Windows) was used for all statistical analyses. The statistical significance level was set at p < 0.05.
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2

Statistical Methods for Analyzing Patient Data

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The Kolmogorov-Smirnov test, Shapiro-Wilk test, coefficient of variation, skewness, and kurtosis methods were utilized to control the normal distribution of patient data. Mean and standard deviation values were expressed for continuous variables, while categorical variables were expressed as numbers (n) and percentage (%). Pearson’s χ2 test was used in the analysis of patients with microscopic colitis, who responded and did not respond to treatment. All tests were bilateral, and p < 0.05 was considered statistically significant. Statistical analyses were performed using the SPSS24.0 for Windows (SPSS Inc., Chicago, IL, USA) software package.
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3

Analysis of Platelet Parameters in Survivors

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Data were analyzed using SPSS 24.0 for Windows (SPSS Inc., Chicago, IL). We used the Shapiro-Wilk test and stratified distribution plots to verify the normality of distribution of continuous variables. Continuous variables are presented as median [25–75 percentile] and number (%) as appropriate. Differences between groups (survivors vs. no survivors, and unfavorable vs. favourable outcome) were assessed using Student’s T test, Mann-Whitney test, χ2 test, or Fisher’s exact test, as appropriate. Time-courses of platelet parameters were analyzed using a two-way ANOVA for repeated measurement with Bonferroni post-hoc correction. A two-tailed test was performed and a p value < 0.05 was considered to be significant.
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4

Comparative Analysis of Experimental Methods

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All determinations were replicated three times and expressed as mean values with standard deviations. One-way ANOVA followed by Tukey’s HSD test using SPSS 24.0 for Windows (SPSS Inc., Chicago, IL, USA) was used to determine statistical differences at p < 0.05.
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5

Repeated Measures Analysis of Variance

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The data were analyzed using SPSS software (SPSS 24.0 for Windows, SPSS Inc., Chicago, USA). Repeated measures analysis of variance was used to evaluate the procedures. (Box’s M, Mauchly’s and Wilk's Lambda tests were provided. Geisser–Greenhouse test was also given along with the associated test power.) Bonferroni post hoc test was performed on each pair of the groups.
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6

Medication Chart Preparation Efficiency

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We used Microsoft Office Excel V.15.27 and SPSS 24.0 for Windows for data management and analysis. Continuous variables were expressed with means and SD, medians and minimum and maximum values. Data were not normally distributed, and we applied non-parametric statistical tests. We used the Wilcoxon signed-rank (WSR) test to compare time per medication chart in between the two phases, only including medication charts completed by the same participant in both phases. The Mann-Whitney U (M-W) test was applied to compare mean time and mean number of medication charts prepared between groups, including all medication charts from both phases. We applied Fischer′s exact (FE) test to explore differences in error rates per medication chart between the two phases, including all medication charts from both phases. A value of p<0.05 was considered significant.
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7

Volatile Compounds in Ancient Cypress Leaves

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Chemometric analyses such as hierarchical cluster analysis (HCA), principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS–DA) were performed to systematically analyze the influence of tree age on the volatile component contents in ancient Platycladus orientalis leaves. Heatmap analysis and principal component analysis were generated using the Origin software for statistical and computing (Origin Pro 2020b, Origin Lab, USA). Orthogonal partial least squares discriminant analysis was performed using R (http://www.r-project.org/ (accessed on 15 November 2022)). Variance analysis was performed using SPSS 24.0 for Windows (SPSS Inc., Chicago, IL, USA).
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8

Quantitative Analysis of Psychological Factors

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Statistics were calculated by using statistical software SPSS 24.0 for Windows. Demographic, anthropomorphic, and psychological data were analyzed using column statistics. The Shapiro–Wilk W test determined the normal distribution of the demographic and clinical variables. Partial correlations analyzed relationships between outcome factors and other factor correlations. Differences between scores or measures were computed by unpaired t-tests or Mann–Whitney tests in dichotomous subgroups of data. To evaluate the correlation coefficient, the Cohen model of the correlation force developed for psychological testing (1988) was used, with a very weak correlation (0.00–0.09), weak correlation (0.09–0.29), a moderate relationship (0.30–0.49), and a strong relationship (0.50–1.00). For reduction of the multicollinearity, the regression analyses were made. Differences were reflected as significant when p-values were less than 0.05.
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9

Statistical Analysis of Experimental Data

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All data were analyzed on the statistical software program SPSS 24.0 for Windows (SPSS, Chicago, IL). Differences between the groups were evaluated by Student’s t test, non-parametric Mann-Whitney U test, one-way ANOVA test, Kruskal-Wallis H test, and Spearman’s correlation test. P value less than 0.05 was considered statistically significant (*P < 0.05, ** P < 0.01, *** P < 0.001; ns, not significant).
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10

Concordance of Ki67 Grading in Pancreatic Neuroendocrine Tumors

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All patient cases were included in statistical analysis comparing concordance rates of (1) Ki67 grading between EUS-FNA cytology and surgical pathology specimens and (2) Ki67 grading and mitotic rate grading of surgical pathology specimens.
All patients, except one patient with synchronous metastasis during diagnosis, were included in the Kaplan–Meier estimator for disease recurrence. Fisher’s exact test was used for univariate analysis to determine the risk factors associated with disease-related death and disease recurrence. One patient had a NF-PNET resection with disease recurrence at seven years after surgical resection. This patient’s second tumor data was excluded from receiver operating characteristic (ROC) curve analysis and Fisher’s exact test. P-values less than 0.05 were regarded as statistically significant. Given our sample size, odds ratios were calculated with a two-by-two table instead of logistic regression.
All data were analyzed with SPSS 24.0 for Windows.
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