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Spss statistics software v 23

Manufactured by IBM
Sourced in United States

SPSS Statistics software (v.23) is a comprehensive set of tools for data analysis, management, and presentation. It provides a wide range of statistical techniques, including descriptive statistics, bivariate analysis, predictive analytics, and more. The software is designed to help users quickly and easily analyze data, uncover insights, and make informed decisions.

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61 protocols using spss statistics software v 23

1

Statistical Analysis of Survival Data

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Data are presented as the mean and standard error of the mean (sem) or median values for survival analysis. Comparisons of means of variables between different groups were performed using the parametric Student’s t test (two-sided) when the population followed a Gaussian distribution, or the non-parametric Mann-Whitney test when they did not. Overall survival curves were performed using the Kaplan-Meier estimation, and the Mantel-Cox test was used for comparisons between groups. Univariable Cox proportional hazard ratio (HR) models were applied to investigate the influence of treatment in overall survival. A P ≤ 0.05 was considered significant. All statistical analyses were performed with GraphPad Prism software and SPSS v23 statistics software (North Castle, NY, USA).
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2

Statistical Analysis of Experimental Groups

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One-way ANOVA followed by a Tukey post hoc test and independent T-test was used to assess the statistically significant differences between the means (SPSS v23 statistics software, Armonk, NY, USA). The difference is significant if the p-value is < 0.05.
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3

Statistical Analysis of Immunomodulatory Compounds

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The results are expressed as the mean ± standard error of mean (SEM). Statistical analysis was conducted using the IBM SPSS v23 statistics software (Armonk, NY, USA). An independent t-test was conducted for comparing differences between two groups (Blank group vs. PMACI control group or vehicle group vs. DNFB control group). ANOVA with Tukey post-hoc test was conducted for comparing differences among the PMACI control group/DNFB control group vs. the physcion or DEX-treated group. A p value <0.05 was considered significant.
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4

Statistical Analysis of Biological Data

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Normality of data was assessed using the Kolmogorov–Smirnov test. Normally distributed data were expressed as the mean ± SD. Non-normally distributed data were represented using box-plots (Sigmaplot) or scatter-plots with the median and interquartile range (GraphPad). For normally distributed data, mean values were compared using ANOVA followed by Tukey’s test (more than two groups) or two-tailed t test (for two group comparisons). The data which were not normally distributed were compared by the Mann–Whitney U test (for two groups comparisons) or Kruskal–Wallis tests (more than two groups) followed by Dunn’s post-hoc test. The significance was set at 95 % of confidence. Linear correlations were analyzed using the Kendall tau-beta test. In all the cases, IBM SPSS (v23) Statistics software was used.
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5

Comparative Analysis of Qualitative and Quantitative Data

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Data are expressed as percentages for qualitative data and as means ± standard deviation (SD) or medians [interquartile range, IQR] for quantitative data. Qualitative variables were compared using Chi² test or Fisher's exact test, and quantitative variables using Mann-Whitney test. A result was considered significant if the p value was less than 0.05. SPSS Statistics v23® software was used for all analyses.
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6

Comparative Analysis of Experimental Outcomes

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The data are expressed as numbers (%) for qualitative data and as the means ± standard deviations (SDs) or medians [interquartile ranges] for quantitative data. Qualitative variables were compared using Chi² test or Fisher's exact test, and quantitative variables were compared using the Mann-Whitney test or an ANOVA test followed by a Tukey test for post hoc analysis. The normality of the distribution was verified using the Kolmogorov-Smirnov test, Shapiro-Wilk test and visual inspection. A result was considered significant if the p value less than 0.05. All the statistics were calculated using SPSS Statistics v23® software.
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7

Statistical Analysis of Diagnostic Tests

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Categorized data were assessed with chi-square test. A P value < 0.05 was considered significant. The specificity, sensitivity, positive predictive value, negative predictive value and accuracy rate were calculated. Statistical analysis was done using the SPSS Statistics software (v23).
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8

Survival and Adhesion of E. coli Nissle 1917 in GI Tract

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All tests were carried out in triplicate. ANOVA test with the significance level at p < 0.05 was used to evaluate the changes in kinetics parameters, post-acidification, numbers of EcN, survival of EcN under conditions simulating the GIT, and adhesion of EcN to Ca-co-2 cells. The data were shown as average ± standard deviation. The Duncan’s multiple range tests was used for determination of significant differences between values. SPSS Statistics software V.23 was used to evaluate all statistical tests in the current study. A principal components analysis (PCA), hierarchical cluster analysis (HCA) and contour plot of preference and preference map were performed using the XLSTAT program.
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9

Transmigration and Trans-endothelial Migration Assay

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For transmigration assay and trans-endothelial migration assay, statistical analyses and the determination of p-values were performed by using Student's t-test, with IBM SPSS Statistics software v. 23 (SPSS Inc., Chicago, IL, USA).
For the analysis of clinicopathological characteristics, SPSS for Windows version 21.0 (SPSS Inc.) was used. Mann-Whitney U-test and Fisher's exact test were used for the analysis of various clinicopathologic factors. p-values < 0.05 were considered statistically significant. Kaplan-Meier survival curves with log-rank statistics were applied to evaluate time to survival.
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10

Descriptive Statistical Analysis Protocol

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Analyses were performed using SPSS statistics software (v23) and encompassed mainly descriptive statistics. Normally distributed continuous data are presented as mean ± standard deviation (SD) and non-normally distributed continuous data with the median [inter quartile range (IQR)]. Discrete data are expressed by category frequencies and percentages.
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