Continuous variables are presented as mean (±standard deviation), while categorical variables are presented as numbers (percentages). The Kolmogorov-Smirnov test (P > 0.05 for all variables) was used to test the normality assumption. Cronbach's alpha was used to assess questionnaires' reliability with values >0.7 indicating acceptable reliability. Multivariate linear regression analyses was used to control potential confounding variables. We estimated adjusted coefficients beta with 95% confidence intervals and Pvalues. Statistical significance was set at <0.05. IBM SPSS Statistics for Windows, Version 21.0 (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.) was used for data analysis.
Spss statistics for windows version 21
Manufactured by IBM
Sourced in United States, United Kingdom, Belgium, Japan
SPSS Statistics for Windows, Version 21.0 is a statistical software package designed for data analysis. It provides a comprehensive set of tools for data manipulation, analysis, and visualization. The software is designed to work on the Windows operating system.
Automatically generated - may contain errors
Lab products found in correlation
Spss 21, by IBM (22 mentions)
Spss statistics 21, by IBM (19 mentions)
Spss version 21, by IBM (10 mentions)
Prism 6, by GraphPad (6 mentions)
Prism 8, by GraphPad (6 mentions)
Statistical package for social science, by IBM (6 mentions)
Statistical package for social sciences software, by IBM (5 mentions)
Prism 5, by GraphPad (4 mentions)
Sas version 9, by SAS Institute (3 mentions)
Spss cluster analysis software, by IBM (3 mentions)
Prism 7, by GraphPad (2 mentions)
Spss v21, by IBM (2 mentions)
Masshunter quantitative analysis b 07, by Agilent Technologies (2 mentions)
Stata, by StataCorp (2 mentions)
Spss statistical software version 21, by IBM (2 mentions)
Statistical software version 15, by MedCalc (2 mentions)
Ms excel 2007, by IBM (2 mentions)
Sas 9, by SAS Institute (2 mentions)
Centricity pacs software, by GE Healthcare (1 mentions)
Stata mp 16, by StataCorp (1 mentions)
1 169 protocols using spss statistics for windows version 21
1
Multivariate Analysis of Continuous Variables
2
Prehospital Predictors of TBI Outcomes
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To describe general characteristics categorical variables are reported as percentage (%), while continuous variables are reported as median and range. Binary logistic regression analysis was used in univariate and multivariable models to predict mortality and a good neurological outcome. The evaluation was performed in the context of a prehospital environment using predictors that were of value in the prehospital treatment phase [17 (link)]. The following known conventional prognostic variables [5 (link), 6 (link)] for TBI were available in the prehospital setting: age, on-scene GCS, hypoxia and hypotension. As per the hypothesis that treatment provided by an on-scene anaesthetist would be beneficial to TBI outcomes, physician was added as a potential predictive factor with regard to the prognosis. The results are presented as odds ratios (OR) with 95% confidence intervals. Statistical significance was considered to be a p-value of ≤0.050. The data were analysed using SPSS Statistics for Windows® version 21.0.
3
Comparing Sample Parameter Differences
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Non-parametric Wilcoxon test was used to compare the differences between each fraction of parameters because not all samples were in compliance with normal distribution. Differences with a p value < 0.05 were considered statistically significant. All calculations were performed with SPSS Statistics for Windows, version 21.0 (SPSS Inc, Chicago, Ill, USA).
4
Serum IGFBP-2 and Non-Alcoholic Fatty Liver Disease
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Variables were compared between groups using Student’s t-test or Mann–Whitney U test for continuous variables, and χ2 test for categorical variables. Differences in NAFLD according to quartiles of serum IGFBP-2 were analyzed by χ2 test. Baseline and follow-up values were compared by Wilcoxon’s matched-pairs signed rank test. The relationship between baseline IGFBP-2 levels and the incidence of NAFLD at follow-up was analyzed by adjusted odds ratios (ORs) and 95% confidence intervals (CIs) obtained by multivariate binary logistic regression. Correlations between serum IGFBP-2 levels and anthropometric/biomedical variables were determined by partial correlation coefficients. The performance of the ELISA kits was validated by Cronbach’s Alpha test. All analyses were performed using SPSS Statistics for Windows, Version 21.0 (SPSS Inc., Chicago, IL, USA). The sample size power (post hoc) was calculated using G*Power (version 3.1, Heinrich-Heine-Universität Düsseldorf, Germany).20 (link) A two-sided value of P < 0.05 was considered significant.
5
Comparing Radiation Dose Metrics
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DIR-based cumulative DVH parameters and simple DVH parameter addition were compared using the paired t-test. All statistical analyses were performed using IBM SPSS Statistics for Windows, Version 21.0 (SPSS Inc., Armonk, NY, USA). p < 0.05 was considered statistically significant.
6
Effect of Whole-Body Vibration on Athletic Performance
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A software package (GPower 3.0) was used to calculate sample size based on power calculations. The statistical power, for all performance parameters, ranged from 0.85 to 0.94. All statistical analyses were performed using SPSS Statistics for Windows, version 21.0 (SPSS Inc., Chicago, Ill., USA). The normality of data was examined using the Shapiro-Wilk test. Two-way analysis of variance (ANOVA) [3 groups (Lf-WBV, Hf-WBV, and CG) x 2-time points (pre and post training)] with repeated measures on the «time» factor was used to analyse the data. A one-way ANOVA was used to compare the changes of each variable from pre- to post-training between the three groups (Lf-WBV, Hf-WBV, and CG). When a significant group × time interaction or group main effect was found, Tukey pairwise comparisons were applied to locate the significantly different means within and between groups. Cohen’s effect sizes (ES) were calculated using the equation: d=difference between means/pooled SD. The level of significance for all statistical analyses was set at p < 0.05.
7
Epidata-Based Data Entry and Analysis
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The database was created using Epidata 3.1 software (Epidata Association, Odense, Denmark). Data were entered twice by different people, and the Epidata software could be used to perform automatic logic checks to conduct the data review. All data were analyzed using SPSS Statistics for Windows, version 21.0 (SPSS Inc., Chicago, IL, USA). In the description of each indicator, continuous variables were presented as mean ± standard deviation and categorical variables as ratio or composition ratio. Continuous variables were compared between groups using t-tests and categorical variables using χ2 tests. A two sided P-value < 0.05 was considered to be statistically significant.
8
Statistical Analysis of Experimental Data
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Statistical analysis was executed on SPSS Statistics for Windows, version 21.0 (SPSS Inc., Chicago, IL, USA). The data are mentioned as means ± SE from four independent experiments with three replications each time. The p values (< 0.05) were used for statistically significance between different experimental groups using one-way ANOVA.
9
Comparative Analysis of Regional Variations
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Variables are reported as means and standard deviations, median and interquartile, or percentages, as appropriate. Means were compared using one-way ANOVA complemented by the Tukey test or Kruskall-Wallis ANOVA complemented by Dunn test. Differences between categorical variables were assessed by the chi-square test. We used the Southeast region as reference.
The SPSS Statistics for Windows version 21.0 was used to analyze the data. P-values <0.05 were considered significant.
The SPSS Statistics for Windows version 21.0 was used to analyze the data. P-values <0.05 were considered significant.
10
Predicting Mortality Risk with sTIPS
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Data were calculated as frequencies and percentages for categorical variables, and as mean ±SD for continuous variables (normal distribution) or median and interquartile ranges (abnormal distribution). Patient characteristics were compared based on sTIPS values. Parametric patient characteristics were compared using a one-way ANOVA, whereas nonparametric characteristics were compared using the Kruskal-Wallis H test. Categorical data were compared using the chi-square (χ2) test. Kaplan-Meier curves were constructed and stratified according to sTIPS. Cox proportional hazards models were used to investigate the relationship between sTIPS and time-to-mortality during hospitalization. To construct the Cox model, univariate Cox regression for each predicting variable was performed, with all-cause mortality as the outcome variable. Variables that were found to be significant (P < 0.05) on univariate Cox models were then entered into a multivariable Cox model. From the multivariable model, we identified variables that were significant (P < 0.05) predictors of mortality. Data analysis was performed using SPSS Statistics for Windows, Version 21.0 (Chicago, IL, USA: SPSS Inc.).
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