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Statistical package for the social sciences spss statistics 22

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SPSS Statistics 22 is a comprehensive software package designed for statistical analysis. It provides a wide range of analytical tools for data management, data manipulation, and statistical modeling. The core function of SPSS Statistics 22 is to assist users in analyzing and interpreting complex data sets, with a focus on social sciences and related fields.

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Statistical package for the social sciences version 21, by IBM (1 mentions)

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SPSS Statistics Package 22.0 SPSS statistical package 22.0 SPSS Statistics 22 SPSS Statistic 22 SPSS package 22 SPSS Statistics package SPSS statistical 22.0 software SPSS statistical package SPSS for Statistics Statistics 22.0

2 protocols using statistical package for the social sciences spss statistics 22

1

Validation of the ESQ-NS Questionnaire

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Initially, descriptive statistics were analysed. Second, an exploratory factor analysis (EFA) [32 ] was conducted to find possible factor structures of questionnaire items after checking response bias of items by ceiling and floor effects. For internal consistency reliability, Cronbach’s alpha [33 ] was calculated for each factor. For criterion-related validity, a simple liner regression analysis was performed using the total and individual scores of the domains of JMST and JMSQ as independent variables, and the total and individual scores of the domains of ESQ-NS as dependent variables. Test-retest reliability was analysed using Pearson’s correlation analysis [34 ]. The average of the total and individual scores for the domains of ESQ-NS were compared by grade using a one-way analysis of variance. IBM Statistical Package for the Social Sciences (SPSS) Statistics 22 was used to calculate descriptive statistics and to conduct the EFA.
Muramatsu T., Nakamura M., Okada E., Katayama H, & Ojima T. (2019). The development and validation of the Ethical Sensitivity Questionnaire for Nursing Students. BMC Medical Education, 19, 215.
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2

Multivariate Analysis of Metabolite Profiles

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Multiple linear and logistic regression analyses were applied using the Statistical Package for the Social Sciences version 21 (IBM SPSS Inc). Categorical variables were analyzed using Chi-squared and Fisher exact tests as appropriate. Continuous variables were compared using 2-sided t-tests (for parametric variables) and the Mann-Whitney U test (non-parametric variables). A false discovery rate (FDR) was used to adjust for multiple metabolite testing. Metabolites with FDR <0.05 were considered significant.
Multivariate beta diversity analysis between groups, including age, gender and diagnostic category, was performed using R studio and the Statistical Package for the Social Sciences (SPSS Statistics 22, IBM Corp., Armonk, New York). All presented p values were corrected for multiple comparisons using the Benjamini-Hochberg false discovery rate (FDR) method.
Wijeyesekera A., Wagner J., De Goffau M., Thurston S., Rodrigues Sabino A., Zaher S., White D., Ridout J., Peters M.J., Ramnarayan P., Branco R.G., Torok M.E., Valla F., Meyer R., Klein N., Frost G., Parkhill J., Holmes E, & Pathan N. (2019). Multi-Compartment Profiling of Bacterial and Host Metabolites Identifies Intestinal Dysbiosis and Its Functional Consequences in the Critically Ill Child. Critical Care Medicine, 47(9), e727-e734.
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