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Spss statistics program for windows version 22

Manufactured by IBM
Sourced in United States

SPSS Statistics is a software package used for interactive, or batched, statistical analysis. It is capable of handling large amounts of data and can perform a variety of statistical procedures.

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Lab products found in correlation

5 protocols using spss statistics program for windows version 22

1

Appropriateness of Pulmonary Artery Revascularization

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Descriptive analyses (mean, standard deviation, median, interquartile range) were conducted to describe the study population. The characteristics of patients whose indication for PAR was appropriate according to the most restrictive guidelines (HAS and ACR) were compared against the characteristics of patients whose indication for PAR was inappropriate This comparison was performed by means of univariate and multivariate analyses using odds ratios (OR) and associated 95% confidence intervals (CI). The analyses were performed using the IBM SPSS statistics program for Windows, version 22 (IBM Corp., Armonk, New York, USA).
The study was approved by the research ethics committee of Geneva, Switzerland and registered on ClinicalTrials.gov (identifier NCT02980081).
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2

Statistical Analyses of Experimental Results

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Statistical evaluations were performed using the IBM SPSS-Statistics program for Windows, version 22 (International Business Machines Corp., Armonk, United States of America). Graphs were plotted with GraphPad Prism 5 software (Graphpad Software Inc., San Diego, CA). Results are given as means ± SEM. The significance level for all analyses were set to p < 0.05 (), p < 0.01 (∗∗), and p < 0.001 (∗∗∗). All experiments were performed three times.
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3

Evaluating Intestinal Cell Responses

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Statistical evaluations were carried out by means of the IBM SPSS-Statistics program for Windows, version 22 (International Business Machines Corp., Armonk, United States of America). Graphs were plotted with SPSS and Microsoft Excel 2010. Results are given as means ± SEM. N refers to the number of experiments.
Statistical significance of differences was assessed by variance analysis. The fixed factor was “treatment of the cells” (incubation with medium (control), E. faecium, ETEC, EPEC, ETEC or EPEC in coincubation with E. faecium). An overall analysis of the data for each cell line and each parameter (TER, mRNA-, and protein expression) was performed. A P value of <0.05 was assumed to indicate statistically significant differences. If a statistical significant difference occurred in the overall analysis each time point (4 h, 6 h, 8 h) was analyzed separately. In the case of a significant difference between groups (treatment of the cells), the Fisher least significant difference post hoc test was performed.
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4

Exploring School Environment Factors in Bullying Victimization

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All analyses were performed using the IBM SPSS Statistics program for Windows, version 22 (IBM Corp., Armonk, NY, USA). First, participant demographics and differences in the school environment for the participants who had not experienced bullying were analyzed. Continuous variables were analyzed using independent sample t-tests, and categorical variables were compared using the chi-square test. Next, four separate hierarchical logistic regression analyses were conducted to examine the association between aspects in the school environment and the following types of bullying victimization: social exclusion, insults or teasing, extortion, and sexual harassment. Student characteristics were input into the models to assess their initial effects on bullying victimization. After adjusting for individual factors, the variables concerning aspects of the school environment were entered and analyzed.
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5

Examining School Environment Factors and Bullying Victimization

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All analyses were performed using the IBM SPSS Statistics program for Windows, version 22 (IBM Corp., Armonk, NY, USA). First, participant demographics and differences in the school environment between the participants who had not experienced bullying were analyzed. Continuous variables were analyzed using independent sample t-tests, and categorical variables were compared using the chi-square test. Next, four separate hierarchical logistic regression analyses were conducted to examine the association between aspects in the school environment and the following types of bullying victimization: social exclusion, insults or teasing, extortion, and sexual harassment. Student characteristics were input into the models to assess their initial effects on bullying victimization. After adjusting for individual factors, the variables concerning aspects of the school environment were entered and analyzed.
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