Continuous variables were tabulated as medians with ranges, or as means with standard deviations, depending on data's distribution. The distribution was assessed using the Shapiro–Wilk test with a P-value higher than 0.05 considered as normally distributed. Two groups' comparisons were tested using Student's T-test or Mann–Whitney U. Nominal data were analyzed using the chi-square (χ2) test. RFS was calculated as the difference between the date of recurrence or the last follow-up and the beginning of surveillance of first-line treatment. RFS curves were estimated by the Kaplan–Meier method, whereas comparisons among groups were analyzed with log-rank or Breslow tests. Statistically significant and borderline variables (P ≤ 0.05) were included in the multivariate analyses. All data were analyzed using the SPSS software package version 23 (SPSS, Inc., Chicago, IL, US).
Spss software package version 23
Manufactured by IBM
Sourced in United States
SPSS software package version 23.0 is a comprehensive analytical tool designed for statistical analysis. It provides a wide range of statistical procedures and data management capabilities to help users analyze and interpret data. The software is capable of handling large datasets and offers advanced analytical techniques for researchers, analysts, and decision-makers.
Automatically generated - may contain errors
Lab products found in correlation
31 protocols using spss software package version 23
1
Statistical Analysis of Recurrence-Free Survival
2
Sural Nerve Conduction Factors
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Previous studies have reported that sural SNAP significantly depends on subject age, and SNCV depends on subject age and height.13 , 14 , 15 , 16 The SNAP values decrease by 1 μV for every 10 years, whereas SNCV values decrease by 1.3 m/s for every 10 years and 2.0 m/s for every 10 cm of height.13 Thus, one‐way ancova was used to eliminate the confounding effects of age and height. Because homogeneity of variances was violated (P = 0.002, by Levene's test), the measured value of SNAP was transformed into the square root of the values (SNAPsqrt). The independent variable was the CTCAE grade, and the dependent variables were SNAPsqrt and SNCV. The covariate was subject age for SNAPsqrt, and subject age and height for SNCV. As a post hoc analysis, adjusted means of SNAPsqrt and SNCV were compared between each CTCAE grade using t‐tests with a Bonferroni correction. Correlations between the measured values of SNAP or SNCV and the CTCAE grade were also examined using Spearman's rank‐order correlation coefficient. All calculations were carried out using the spss software package, version 23 (SPSS, Chicago, IL, USA) and two‐sided P‐values <0.05 were considered to indicate statistical significance.
3
Statistical Analysis of Genetic Variants
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Statistical analysis was performed using SPSS software package, version 23 (SPSS, Inc.; Chicago, Illinois, United States). The mean difference between study group was assessed by using the Student's
t-test. Qualitative data were presented as number of (%), the comparisons of genotypes and alleles frequencies between patients and controls were assessed using the chi-squared (x
2) test and the Fisher's exact tests, and levels of risk for genotypes and alleles were expressed as odds ratio (OR) with a 95% confidence interval (95% CI). Deviation from Hardy–Weinberg equilibrium was performed by applying the equation (p
2 (link)
+ 2pq + q
2 (link)
) to compare the observed frequencies with the expected frequencies of the different genotype distribution in patients and controls by using Pearson's x
2test of independence in SPSS. Statistical significance was considered at
p<0.05.
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Statistical analysis was performed using SPSS software package, version 23 (SPSS, Inc.; Chicago, Illinois, United States). The mean difference between study group was assessed by using the Student's
t-test. Qualitative data were presented as number of (%), the comparisons of genotypes and alleles frequencies between patients and controls were assessed using the chi-squared (x
2) test and the Fisher's exact tests, and levels of risk for genotypes and alleles were expressed as odds ratio (OR) with a 95% confidence interval (95% CI). Deviation from Hardy–Weinberg equilibrium was performed by applying the equation (p
2 (link)
+ 2pq + q
2 (link)
) to compare the observed frequencies with the expected frequencies of the different genotype distribution in patients and controls by using Pearson's x
2test of independence in SPSS. Statistical significance was considered at
p<0.05.
4
Analyzing Physiological Changes in Weightlifting
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To assess normality of data distribution and equality of variances, the Shapiro-Wilk and Levene tests were used, respectively. Analysis of variance (ANOVA) with repeated measure [within-subject effects (time), between-subject effects (group), interaction between the two types of effects (group × time)] and Bonferroni post-hoc test were used to determine intra-group and inter-group changes in body weight and Maximal carrying load (MCL) variables. In relation to other research variables, one-way ANOVA and Tukey's post hoc tests were used to compare differences between groups (in the post test). SPSS software package (version 23) was used for data analysis and the statistically signi cant difference was set at p < 0.05.
5
Analyzing Physiological Changes in Weightlifting
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To assess normality of data distribution and equality of variances, the Shapiro-Wilk and Levene tests were used, respectively. Analysis of variance (ANOVA) with repeated measure [within-subject effects (time), between-subject effects (group), interaction between the two types of effects (group × time)] and Bonferroni post-hoc test were used to determine intra-group and inter-group changes in body weight and Maximal carrying load (MCL) variables. In relation to other research variables, one-way ANOVA and Tukey's post hoc tests were used to compare differences between groups (in the post test). SPSS software package (version 23) was used for data analysis and the statistically signi cant difference was set at p < 0.05.
6
Comparing Surgical Outcomes: OSS vs. SSV
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The paired two-tailed Student's t-test was used to compare the OSS and SSV before and after surgery. A p value of <0.05 was considered significant. The SPSS software package, version 23 (SPSS Inc, an IBM Company, Chicago, Illinois) was used to analyse data.
7
Genetic Association Analysis with SPSS
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SPSS software package, version 23.0 (IBM, Armonk, NY, USA) was applied for calculating all statistical data. Chi‐square test (χ2), odds ratio (OR), and their 95% confidence intervals (CI) and Hardy–Weinberg Equilibrium (HWE) were calculated. The genotype and allelic frequencies were reported as the percentage. For all analyses, a statistically significant value was considered at p < 0.05. Bonferroni correction was performed to correct the p‐values, and a p‐value of <0.0033 (for three SNPs and five genetic models for each SNP, p = 0.05/5×3=) after the Bonferroni correction was considered statistically significant.29
8
Statistical Analysis of Research Data
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Statistical analysis was performed in the Statistica software package version 14.0 (TIBCO Inc., Palo Alto, CA, USA) and SPSS software package version 23.0. (IBM, Armonk, NY, USA). After testing the normality of data distribution via the Kolmogorov–Smirnov test, the appropriate parametric and nonparametric tests were used. For continuous and ordinal data, differences between two groups were tested by t-test or Mann–Whitney test for independent variables, while the Wilcoxon test was used for dependent variables. For categorical data testing, the Chi-square test was used. Continuous data were presented as mean ± SD and median (min–max), ordinal data as median (min–max) and categorical data as numbers. The Spearman rank correlation test evaluated the presence of associations between examined variables, and binary univariate and multiple logistic regression analyses were performed to determine the strength and independence of associations. In all analyses, the level of statistical significance was set at 0.05.
9
Impact of Person-Centered Critical Care Nursing on Kinesiophobia
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Data were fed to the computer and analyzed using IBM SPSS software package version 23.0. A one-way ANOVA test was used to compare more than two categories. Student t-test was used to compare two categories for normally distributed quantitative variables. Pearson coefficient was used to correlate between normally distributed quantitative variables. Linear regression was assessed to detect factors that affect HADS and Kinesiophobia. Path analysis was assessed using AMOS 23. 0 software to detect the Direct and Indirect Effect of Person-Centered Critical Care Nursing on Kinesiophobia mediating by (HADS). s ignificance of the obtained results was judged at the 5% level.
10
Quantitative Data Analysis Protocol
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The computer was fedded with the data, and IBM SPSS software package version 23.0 was used for analysis. A one-way ANOVA test was employed for comparisons between more than two categories. Two categories were compared using the student t-test for regularly distributed quantitative data. The Pearson coefficient was employed to correlate customarily distributed quantitative data. At the 5% level, the results’ significance was assessed. The internal consistency of the scale was calculated through Cronbach’s α coefficient.
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