Spss statistics ver 22

Manufactured by IBM

Sourced in United States, Japan

SPSS Statistics ver. 22.0 is a software application for statistical analysis. It provides a comprehensive set of tools for data management, analysis, and presentation. The software is designed to handle a wide range of data types and supports a variety of statistical techniques, including regression analysis, hypothesis testing, and multivariate analysis.

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Lab products found in correlation

Prism ver 6, by GraphPad (4 mentions) Prism 6, by GraphPad (3 mentions) Prism 8, by GraphPad (2 mentions) R software version 3, by R Project for Statistical Computing (2 mentions) Sigmaplot 12, by Grafiti LLC (2 mentions) Ver 20, by MedCalc (2 mentions) Matlab 2018b, by MathWorks (1 mentions) Prism 7, by GraphPad (1 mentions) R ver 3, by R Project for Statistical Computing (1 mentions) Spectronaut pulsar x software, by Biognosys (1 mentions) Eos 50d, by Canon (1 mentions) R ver3.2.5, by R Project for Statistical Computing (1 mentions) Systat 13, by Grafiti LLC (1 mentions) Spss regression, by IBM (1 mentions) Rout test, by GraphPad (1 mentions) Sigmaplot 12.5 enzyme kinetics module, by Grafiti LLC (1 mentions) Prism ver 5.0b, by GraphPad (1 mentions) Stata 14, by StataCorp (1 mentions) Bx61 microscope, by Olympus (1 mentions) Neurolucida software, by MicroBrightField (1 mentions)

393 protocols using spss statistics ver 22

Metabolic Effects of Exercise Interventions

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Descriptive results were expressed as means with SD. Two-way GLMRM analysis (IBM SPSS statistics, ver. 22) was used to determine the effect, and interaction between the types of intervention (two control and three intervention groups) and the treatment (pre-post), on the variables measured from the blood. If a significant effect or interaction was detected, then post-hoc tests with Bonferroni adjustment were performed to determine where the differences existed. Statistical significance was set at the alpha level of 0.05.
The measurements of proteins expressions from skeletal muscle were only available at post-treatment. Therefore, one-way ANOVA (IBM SPSS statistics, ver. 22) was used to determine the effects of interventions (five groups) on the expressions of GLUT4, Akt, phosphorylated IR and GSK3 and the Akt activity from skeletal muscle at post-treatment. If a significant effect was detected, then post-hoc test with Bonferroni adjustment was used to determine where the difference existed. Statistical significance was set at the alpha level of 0.05.
This research obtained approval by the Animal Care and Ethics Committee of Southern Cross University (ARA-13/04 and ARA-14/09).

Wang Y., Wen L., Zhou S., Zhang Y., Wang X.H., He Y.Y., Davie A, & Broadbent S. (2018). Effects of four weeks intermittent hypoxia intervention on glucose homeostasis, insulin sensitivity, GLUT4 translocation, insulin receptor phosphorylation, and Akt activity in skeletal muscle of obese mice with type 2 diabetes. PLoS ONE, 13(9), e0203551.

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Biomarker Selection and Prediction Models for Activity

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General data analyses were performed using IBM SPSS Statistics ver. 22.0 (IBM Co., Armonk, NY, USA). Pearson correlation analysis was conducted to compare the relationship between each potential biomarker and CA. Correlation coefficients greater than 0.15 with P-value equal to or less than 0.5 was used as the biomarker selection criteria based on previous studies. The biomarker results were expressed in mean and standard deviation. MLR and principal component analyses were conducted to obtain the BA prediction models of MLR and PCA. Mathematical calculations for the KDM were conducted by MATLAB 2018b of MathWorks Inc. (Natick, MA, USA). Spiro–Wilk test was used to assess whether data was normally distributed prior to conducting the comparative analysis. Comparisons between active and less-active groups were done through t-test. The significance level (α) was set at 0.05.

, & Jee H. (2019). Selection of a set of biomarkers and comparisons of biological age estimation models for Korean men. Journal of Exercise Rehabilitation, 15(1), 31-36.

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Postoperative Fluid Balance and POPF Risk

Cited 2 times

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The statistical analysis of the study was performed using IBM SPSS Statistics ver. 22.0 (IBM Corp., Armonk, NY, USA). To compare the effect of postoperative fluid balance on surgical outcome, we divided it into 2 groups; high group and low group by median value. In each group, the Student t-test and Mann-Whitney U-test were used to compare continuous parameters, while categorical variables were tested with the chi-square test and Fisher exact test. An additional analysis was performed to identify factors associated with POPF. A logistic regression analysis was used with variables that show difference between POPF group and non-POPF group. High BMI and male sex variables known to be associated with POPF [1 (link)2 (link)] were also used in the univariate analysis. Significant parameters (P < 0.1) in univariate analysis were added to a multivariate analysis to determine risk factors for POPF. Results are reported as odds ratio (OR) and 95% confidence intervals (CIs) of statistical significance was accepted at a level of P < 0.05.

Jeon H.J., Kwon H.J., Hwang Y.J, & Kim S.G. (2022). Unfavorable effect of high postoperative fluid balance on outcome of pancreaticoduodenectomy. Annals of Surgical Treatment and Research, 102(3), 139-146.

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Comparative Visual Acuity and Contrast Sensitivity

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Decimal visual acuity was converted to logarithm of the minimal angle of resolution (logMAR) scale for statistical analyses. All CS data were transformed to logarithmic units and logCS values were compared in each group. All continuous data were expressed as mean ± standard deviation of the mean. Statistical analysis was performed with IBM SPSS Statistics ver. 22.0 (IBM Corp., Armonk, NY, USA). Statistical analyses of quantitative data, including descriptive statistics, were performed for all items. Categorical variables were compared using Fisher's exact test. The Kruskal-Wallis test was used to compare results among the three IOL groups. For post hoc analysis, Mann-Whitney U-test with Bonferroni adjustment was used to avoid experimental error. A p-value less than 0.05 was considered statistically significant.

Kim B.H., Hyon J.Y, & Kim M.K. (2019). Effects of Bifocal versus Trifocal Diffractive Intraocular Lens Implantation on Visual Quality after Cataract Surgery. Korean Journal of Ophthalmology : KJO, 33(4), 333-342.

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Sarcopenia and Obesity Risk Analysis

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All analyses were extracted by stratified cluster sampling, and the standard plots and weights were used so that the data used in this study would represent the Korean people using the data analysis method of complex sampling. To compare the general characteristics of the sarcopenia and non-sarcopenia groups, chi-squares and Student's t-tests were used to analyze the categorical variables and continuous variables, respectively. The results are presented as the mean±SD. The continuous variables of the four groups were analyzed by ANOVA and a least significance difference (LSD) post hoc comparison. In addition, the odds ratio of the sarcopenic obesity risk was analyzed using multiple logistic regression models. All statistical tests were performed using IBM SPSS Statistics (ver. 22.0; IBM Co., Armonk, NY, USA). Statistical significance was considered at P < 0.05.

Son J., Yu Q, & Seo J.S. (2019). Sarcopenic obesity can be negatively associated with active physical activity and adequate intake of some nutrients in Korean elderly: Findings from the Korea National Health and Nutrition Examination Survey (2008–2011). Nutrition Research and Practice, 13(1), 47-57.

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Physical Activity Patterns and Determinants

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The data collected was analyzed using the IBM SPSS Statistics ver. 22.0 (IBM Co., Armonk, NY, USA). The frequency and percentage of the subjects’ general characteristics, health status, health-related behaviors, and physical activity experiences were calculated. The relationship between general characteristics and health status, health-related behaviors and physical activities was analyzed by chi-square test. To determine the effect of the general characteristics, health status and health-related behaviors on the physical activity, composite sample logistic regression analysis was conducted to calculate the odds ratio (OR) and the 95% confidence interval (CI) was calculated. The significance level of all statistical analyses was set at P<0.05.

Cho M.S, & Kim J.Y. (2019). A study of factors affecting the physical activity of youth: analysis of data from the 13th youth health behavior online survey in 2017. Journal of Exercise Rehabilitation, 15(1), 8-14.

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Statistical Analysis of Intervention Impact

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Data analysis was performed using IBM SPSS Statistics ver. 22.0 (IBM Co., Armonk, NY, USA). The participants’ general characteristics were analyzed using descriptive statistics; the reliability of the assessment scales was assessed using Cronbach’s alpha coefficient, correlations between variables were explored by Pearson correlation coefficient, and the intervention effect was examined using a t-test. The correlation between factors was determined by the value of the correlation coefficient, and a p value < 0.05 was considered a statistically significant level.

, & Son H.K. (2021). The Effects of Simulation Problem-Based Learning on the Empathy, Attitudes toward Caring for the Elderly, and Team Efficacy of Undergraduate Health Profession Students. International Journal of Environmental Research and Public Health, 18(18), 9658.

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Analyzing Heart Rate Variability and Clinical Factors

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IBM SPSS Statistics ver. 22.0 (IBM Co., Armonk, NY, USA) was used for statistical analyses. All continuous variables were described as mean±standard deviation. Logarithmic transformations were performed before statistical analyses for variables that were not normally distributed. Independent sample t-tests were used to compare means of continuous variables. Chi-square tests were performed to determine the significance of categorical variables. Univariate and multivariate regression analyses were performed to examine the associations between HRV, as a continuous outcome, and clinical explanatory variables. Statistical significance was defined as P<0.05.

Kim H.Y., Jung H.W., Lee Y.A., Shin C.H, & Yang S.W. (2019). Cardiac autonomic neuropathy in nonobese young adults with type 1 diabetes. Annals of Pediatric Endocrinology & Metabolism, 24(3), 180-186.

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Statistical Analysis of Research Outcomes

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Statistical analysis was performed using IBM SPSS Statistics ver. 22.0 (IBM Corp., Armonk, NY, USA). Kolmogorov-Smirnov tests were used to analyze sample distributions. The independent t-test, the Mann-Whitney rank-sum test, the chi-square test, and the Fisher exact test were used. Logistic regression analyses were performed to assess the impact of patient and treatment factors on outcomes. A p-value <0.05 was considered statistically significant.

Kang H.G., Choi E.Y., Byeon S.H., Kim S.S., Koh H.J., Lee S.C, & Kim M. (2018). Anti-vascular Endothelial Growth Factor Treatment of Retinopathy of Prematurity: Efficacy, Safety, and Anatomical Outcomes. Korean Journal of Ophthalmology : KJO, 32(6), 451-458.

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Correlates of Renal Function Markers

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Data are presented as the mean±standard deviation. The relationship between CLvcm and parameters of renal function (Cys-C, SCr, GFRcys-c, CLcr) was assessed by correlation analysis and linear regression using Spearman's correlation coefficient. IBM SPSS Statistics ver. 22.0 (IBM Co., Armonk, NY, USA) was used. A P value of <0.05 was considered to be significant.

Shin J.E., Lee S.M., Eun H.S., Park M.S., Park K.I, & Namgung R. (2015). Usefulness of serum cystatin C to determine the dose of vancomycin in neonate. Korean Journal of Pediatrics, 58(11), 421-426.

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