Three categorical-response logit ordinal models were used [33 ,34 ,35 ], one for each dimension of burnout, in which the explanatory variables were psychological and related to personality. For each model and for each dimension of the syndrome, we determined which variables favoured transitions among levels of burnout. A principal-effects model containing the effects of factors without interaction was found to best fit the data obtained. The goodness of fit was confirmed by the likelihood-ratio test and Pearson’s chi-square test. The statistical significance of the parameters in each model was assessed by the Wald test, and the prognostic ratios of each level were obtained in relation to the other levels, according to possible changes in the explanatory variables considered. All analyses were performed using the SPSS 19.0 statistical program (SPSS Inc., Chicago, IL, USA).
Spss 19.0 statistical program
Manufactured by IBM
Sourced in United States
SPSS 19.0 is a statistical software program developed by IBM. It provides data management, analysis, and visualization capabilities to users. The program is designed to handle a variety of data types and supports common statistical techniques, such as regression analysis, hypothesis testing, and multivariate analysis.
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Lab products found in correlation
5 protocols using spss 19.0 statistical program
1
Modeling Burnout Dimensions Using Personality Factors
2
Statistical Analysis of Experimental Data
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The SPSS 19.0 statistical program was used for data analysis. Statistical comparisons were performed by Student’s t test or ANOVA, followed by the LSD or Dunnett’s T3 post hoc test. p < 0.05 was considered significant. Values were presented as the mean ± SEM.
3
Urban Garden Activity's Impact on Wellbeing
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This study was conducted with experimental and control groups to determine the effect of urban garden activity programs on participants’ perception of the restorative environment, resilience, community consciousness, and stress. For data analysis, 90 questionnaires were analyzed using the SPSS 19.0 statistical program. Frequency analysis and multiple response analysis were conducted for the demographic characteristics and the degree of demand for the number of study subjects. Repeated measures analysis of variance (RM ANOVA) was conducted to verify the means difference between the experimental group and the control group’s PRS scores, CD-RISC scores, SCI scores, and BEPSI scores before and after the urban garden activity program. For the analysis of the difference in cortisol levels as a physiological indicator of stress, a corresponding paired t-test and RM ANOVA were used. All statistical tests used a p-value of <0.05 as the significance level.
4
Statistical Analysis of Genomic Data
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SPSS 19.0 statistical program (SPSS Inc., Chicago, IL, USA) was used for statistical analysis. Data are expressed as mean ± standard deviation. T-test method was used to assess differences between two groups using.
Spearman was used to determine group-wise correlations the GSVA and GSEA analysis. p < 0.05 was considered statistically significant.
Spearman was used to determine group-wise correlations the GSVA and GSEA analysis. p < 0.05 was considered statistically significant.
5
Evaluating Game Performance Intervention
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The SPSS 19.0 statistical program was used to analyze and process the data. Data normality was examined through the Shapiro-Wilk test and the homogeneity of variance through Levene's test, as a prior stage to the application of the GPET: game performance evaluation tool.
intervention program. The assessment of both statistics made it possible to use parametric statistics, determining that the groups were equivalent in all study variables.
To do so, for the decision making and execution variables, an inferential analysis was performed through a two-factor multi variate analysis of variance with repeated measures in one factor in order to determine the effect on the interaction between the two measurement moments (preintervention and postintervention) and between the two groups (experimental and control). The partial eta-squared statistic was used ( 2p ) as the effect size.
intervention program. The assessment of both statistics made it possible to use parametric statistics, determining that the groups were equivalent in all study variables.
To do so, for the decision making and execution variables, an inferential analysis was performed through a two-factor multi variate analysis of variance with repeated measures in one factor in order to determine the effect on the interaction between the two measurement moments (preintervention and postintervention) and between the two groups (experimental and control). The partial eta-squared statistic was used ( 2p ) as the effect size.
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