Spss statistical package 22

Manufactured by IBM

Sourced in United States

SPSS Statistical Package 22.0 is an advanced software tool for statistical analysis. It provides a comprehensive set of features for data management, statistical modeling, and reporting. The software is designed to help users analyze and interpret complex data, making it a valuable tool for researchers, analysts, and decision-makers across various industries.

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Prism 5, by IBM (1 mentions) Prism 5, by GraphPad (1 mentions) Prism 7, by GraphPad (1 mentions)

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Statistical Package of Social Sciences (SPSS version 22.0). SPSS statistical software package version 22.0 SPSS 22.0 statistical package for Windows SPSS statistical package for Windows version 22.0 Statistical Package for Social Sciences for Windows (SPSS) version 22.0 Statistical Package for the Social Sciences (SPSS) for Windows, Version 22.0 SPSS statistical software package 22 SPSS 22.0 statistical package Statistical Package for the Social Sciences (SPSS) V.22.0 Statistical Package for the Social Science (SPSS) version 22

29 protocols using spss statistical package 22

Analysis of Survival Outcomes

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Frequencies and percentages were used to present categorical variables, while the median and range were applied to present continuous variables. Differences among baseline characteristics stratified by the tumor size or the primary tumor location were detected by the chi-square test (χ²) test and the Fisher's exact test. The Kaplan-Meier survival curves were applied to compare the OS, CSS and DFS between the two groups based on the tumor size, which were tested by the log-rank test. Multivariate proportional hazards regression models were utilized by adjusting for the simultaneous impact of potential confounders which were correlated with survival rates in univariate analyses (P<0.1). Additionally, hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated. All statistical analyses were performed by IBM SPSS statistical package 22.0 (IBM, Armonk, NY, USA) and statistically significance was defined as a two-sided P<0.05.

Li X., An B., Ma J., He B., Qi J., Wang W., Qin C, & Zhao Q. (2019). Prognostic Value of the Tumor Size in Resectable Colorectal Cancer with Different Primary Locations: A Retrospective Study with the Propensity Score Matching. Journal of Cancer, 10(2), 313-322.

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Statistical Analysis of Clinical Variables

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In all study subjects, continuous variables were compared parametrically using Student’s t-test or non-parametrically using the Mann-Whitney U-test. Categorical variables were compared using the χ²-test or Fisher’s exact test as appropriate.
Statistical results are presented as the mean ± s.d., and number of patients(%). Two-sided tests, P values < 0.05 were taken as significant. Statistical analyses were conducted using the IBM SPSS statistical package 22.0 (IBM, Armonk, NY, USA) with three plug-in (SPSS R-plug-in, R and psmatching).

Wang F.Y., Meng W., Li Y., Li T, & Qin C.Y. (2016). Comparison of overall survival in patients with unresectable hepatic metastases with or without transarterial chemoembolization: A Propensity Score Matching Study. Scientific Reports, 6, 35336.

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Comparison of Continuous and Categorical Variables

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In all study subjects, continuous variables were compared parametrically using Student t test or were compared nonparametrically using the Mann–Whitney U test. Categorical variables were compared using the χ² test or Fisher exact test as appropriate.
Statistical results are presented as the mean ± standard deviation and the number of patients (%). Two-sided test P values <.05 were defined as significant. Statistical analyses were conducted using the IBM SPSS statistical package 22.0 (IBM, Armonk, NY) with 3 plug-ins (SPSS R-plug-in, R and psmatching).

Li Y., Wang F., Chen X., Li B., Meng W, & Qin C. (2017). Short outcome comparison of elderly patients versus nonelderly patients treated with transjugular intrahepatic portosystemic stent shunt: A propensity score matched cohort study. Medicine, 96(29), e7551.

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Statistical Analysis of Fermentation Characteristics

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The data of fermentation characteristics, microbial population and chemical composition were subjected to analysis using the IBM SPSS statistical package 22.0 (SPSS Inc., Chicago, IL, USA), and Student–Newman–Keuls multiple range tests were used to evaluate differences among treatments and the significance was declared at p < 0.05.

Wang W., Tan Z., Gu L., Ma H., Wang Z., Wang L., Wu G., Qin G., Wang Y, & Pang H. (2022). Dynamics Changes of Microorganisms Community and Fermentation Quality in Soybean Meal Prepared with Lactic Acid Bacteria and Artemisia argyi through Fermentation and Aerobic Exposure Processes. Foods, 11(6), 795.

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Pharmacological Data Analysis Protocol

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In vitro pharmacology data were calculated from a number of independent experiments performed in replicates as indicated. Curve fitting was performed with Prism 5.0 (GraphPad Software) using the equation for a single-site sigmoidal, dose-response curve with a variable slope. EC₅₀ values are expressed as geometric means (95% confidence limits). Statistical analyses were performed with IBM SPSS Statistical Package 22.0 and Prism 5.0. Repeated measures, one-way or two-way analyses of variance were followed by post-hoc comparison of select treatment groups with Bonferroni’s or Dunnett’s tests correcting for multiple comparisons. Significance was accepted at p < .05.

Ardestani P.M., Evans A.K., Yi B., Nguyen T., Coutellier L, & Shamloo M. (2017). Modulation of neuroinflammation and pathology in the 5XFAD mouse model of Alzheimer’s Disease using a biased and selective beta-1 adrenergic receptor partial agonist. Neuropharmacology, 116, 371-386.

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Statistical Analysis of Categorical Data

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Data were analyzed using IBM SPSS statistical package 22.0 (IBM Co., Chicago, USA). Categorical variables are presented as the mean ± standard deviation (SD). One-way analysis of variance (ANOVA) with the Student–Newman–Keuls (SNK) comparison test was employed to detect differences among different groups. A significance level of 0.05 was chosen.

Li S., Yang H., Duan Q., Bao H., Li A., Li W., Chen J, & He Y. (2022). A comparative study of the effects of platelet-rich fibrin, concentrated growth factor and platelet-poor plasma on the healing of tooth extraction sockets in rabbits. BMC Oral Health, 22, 87.

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Differential Expression of miRNAs in Diseases

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Statistical software IBM SPSS Statistical Package 22.0 (IBM, Armonk, New York, US) was used to perform all the statistical analysis. Data were represented as mean ± standard deviation (SD) or SD from standard error of the mean as appropriate. Kolmogorov-Smirnov test was used to check the normality of the data. The expression levels of miRNAs and mRNA levels of their target genes between control group and three disease groups were compared using Kruskal-Wallis test and within groups by Mann-Whitney test as appropriate. To check the diagnostic potential of all differentially expressed miRNAs, receiver operating characteristic (ROC) curve analysis was performed on all miRNAs to investigate the diagnostic accuracy of these miRNAs in three diseases and also for each individual disease.

Kumari P., Sharma I., Saha S.C., Srinivasan R, & Minhas P. (2021). Diagnostic potential of differentially regulated microRNAs among endometriosis, endometrioid ovarian cancer, and endometrial cancer. Journal of cancer research and therapeutics, 17(4).

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Epigenetic Regulation of Disease

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All the statistical analysis was performed by statistical software IBM SPSS Statistical Package 22.0 (IBM, Armonk, New York, US). Data were represented as mean ± standard deviation. The normality of the data was determined by the Kolmogorov-Smirnov test. Differences in the promoter methylation frequencies of all studied groups were analyzed by the Chi-square (χ 2 ) test. The mRNA levels of the studied genes between the control group and three disease groups as well as between methylated and unmethylated groups were compared using the Kruskal-Wallis's test and within groups by Mann-Whitney test as appropriate. A difference of P < 0.05 was considered as statistically significant.

Kumari P., Sharma I., Saha S.C., Srinivasan R, & Sharma A. (2022). Promoter methylation status of key genes and its implications in the pathogenesis of endometriosis, endometrioid carcinoma of ovary and endometrioid endometrial cancer. Journal of cancer research and therapeutics, 18(Supplement).

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Statistical Analysis and Visualization

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For statistical analysis and visualization of results SPSS statistical package 22 (SPSS Inc. Chicago, Illinois, USA), and Sigma Plot Version 10 (Jandel Scientific Inc.) were used. Normal distribution of data was evaluated by the Kolmogorov-Smirnov test and comparison between groups was done using Student’s t-test or Mann-Whitney U test, if appropriate. For computation of correlations, Spearman’s rho test was used. All p-values were two-sided and a p-value below 0.05 was considered significant. Data are given as mean ± standard deviation (sd) or median and range, where appropriate.

Trefz P., Obermeier J., Lehbrink R., Schubert J.K., Miekisch W, & Fischer D.C. (2019). Exhaled volatile substances in children suffering from type 1 diabetes mellitus: results from a cross-sectional study. Scientific Reports, 9, 15707.

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Nonparametric Statistical Analysis of Data

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For statistical testing and visualization of results SPSS statistical package 22 (SPSS Inc. Chicago, Illinois, USA), Sigma Stat statistical package Version 3.5 and Sigma Plot Version 10 (Jandel Scientific Inc.) were used. Correlations between variables were assessed by bivariate linear regression analysis (Spearman Rank Order coefficient). Kruskal-Wallis analysis of variance on ranks followed by Dunn’s test for nonparametric values were performed to evaluate differences between groups. All p-values are two sided and a p-value below 0.05 was considered significant. Data are given as median and range unless mentioned otherwise.

Obermeier J., Trefz P., Happ J., Schubert J.K., Staude H., Fischer D.C, & Miekisch W. (2017). Exhaled volatile substances mirror clinical conditions in pediatric chronic kidney disease. PLoS ONE, 12(6), e0178745.

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