Data are presented as means ± standard deviation (SD). Data related to the characterization of the products and the comparison of the results obtained were evaluated using the SPSS Statistics program, version 22 (Chicago, IL, USA). Data distribution was verified, and the Student’s T test was performed, as well as the Analysis of Variance (ANOVA), using Tukey’s test (p-values < 0.05). The experimental design with the application of the Response Surface methodology was developed with the Statistics program version 7 (StatSoft) (Tulsa, OK, USA).
Spss statistics program 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 a large variety of data formats and can perform both simple and complex statistical analyses.
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27 protocols using spss statistics program version 22
1
Optimization of Product Characterization
2
Statistical Analysis of Experimental Findings
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Statistical comparisons of the results were done using SPSS® Statistics program version 22 (SPSS Inc., USA). The normality of variance and homogeneity were assessed by Shapiro–Wilk and Levene tests, respectively. The data were analyzed by one-way analysis of variance (ANOVA) then differences among groups were assessed by Tukey-post hoc. All variables were expressed as means ± standard errors (SE). Statistically significant level was considered at P values < 0.05. Differences were esteemed significant when P < 0.05. All graphs were created using the GraphPad Prism software (Version 8, GraphPad Software Inc.).
3
Correlating Inpatient Psychiatric Care Provision
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The SPSS statistics program version 22 was used for the analyses. Scatterplots were used to explore and illustrate the associations between indicators. Spearman correlation analysis was used to investigate the association between acute and semi-acute inpatient treatment provision (independent variable) and the explanatory MHS service structure variables: diversity of outpatient and day care services, personnel FTE allocation per bed, and community orientation. The correlations between acute and semi-acute beds and other hospital beds and non-hospital beds were also analyzed.
The main outcome in the analyses was the provision of acute and semi-acute inpatient treatment, counted by used beds per 1000 (18+) on acute and semi-acute wards. The dependences between the explanatory variables and the provision of inpatient treatment were analyzed using linear regression analysis. First, univariate analyses were performed for each explanatory factor separately. Next, analyses were controlled for MHI. The significance level was set to p < 0.05. The analyses were carried out with Statistical Package for Social Sciences (SPSS) version 22 (IBM, Armonk, NY, USA).
The main outcome in the analyses was the provision of acute and semi-acute inpatient treatment, counted by used beds per 1000 (18+) on acute and semi-acute wards. The dependences between the explanatory variables and the provision of inpatient treatment were analyzed using linear regression analysis. First, univariate analyses were performed for each explanatory factor separately. Next, analyses were controlled for MHI. The significance level was set to p < 0.05. The analyses were carried out with Statistical Package for Social Sciences (SPSS) version 22 (IBM, Armonk, NY, USA).
4
Cell Culture Experiments Statistical Analysis
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All cell culture experiments were done with groups of n ≥ 3 and repeated at least three times. Statistical analyses were performed using the SPSS statistics program version 22 (SPSS Inc., Chicago, IL). The normality of the response variables was tested by the Kolmogorov-Smirnov test and histogram visualization. This analysis revealed that the response variables were not normally distributed. Statistical differences between the test groups were evaluated using the Kruskal-Wallis test, and comparison between specific groups was done with the Mann-Whitney U-test. The graphical presentation of the results was created with OriginPro 9.1 software (OriginLab, Northampton, MA). p < 0.05 was considered significant. The data are shown as means ± SEM.
5
One-way ANOVA Analysis of Variance
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Statistical differences in the data were found using the one-way analysis of variance (ANOVA) in SPSS® Statistics program version 22 (SPSS Inc., Chicago, IL, USA). The differences among groups were explored by Tukey’s post hoc tests. The normality of variance and homogeneity were evaluated by Shapiro–Wilk and Levene’s tests, respectively. All variables were conveyed as means ± standard error (SE). The minimal significant level was identified at p-values < 0.05. All graphs were made using GraphPad Prism software (Version 8, GraphPad Software Inc., San Diego, CA, USA).
6
Statistical Analysis of Experimental Data
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All experiments were done with groups of n ≥ 3 and repeated with at least 3 independent patient samples. Statistical analyses were performed using SPSS statistics program version 22 (SPSS Inc., Chicago, IL). The normality of the response variables was tested with Kolmogorov-Smirnov or Shapiro-Wilk test (for experiments with n < 50) and histogram visualization. If the response variables were normally distributed, statistical differences between the test groups were evaluated using one-way ANOVA, and comparison between groups was done with two-sample t-test. If the response variables were not normally distributed, statistical differences between the test groups were evaluated using Kruskal-Wallis test, and comparison between groups was done with Mann-Whitney U-test. The graphical presentation of the results was created with OriginPro 9.1 software (OriginLab, Northampton, MA). p < 0.05 was considered significant. Data are shown as means ± SEM.
7
Statistical Analysis of Experimental Data
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Statistical assessments of the experimental data were accomplished by SPSS® Statistics program version 22 (SPSS Inc., Chicago, IL, USA). The validation of statistical tests was assessed by normality of variance and homogeneity using Shapiro–Wilk and Levene tests, respectively. The data were examined by one-way analysis of variance (ANOVA), then variations among groups were evaluated by Tukey post hoc test. All experimental variables were expressed as means ± standard errors (SE). Statistically significant levels were anticipated at p-values 0 < 0.05. Differences were considered significant when p < 0.05. All graphs were designed via the GraphPad Prism software (Version 8, GraphPad Software Inc., San Diego, CA, USA).
8
Exploring Educational Challenges in Romania
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In order to identify the measures taken by the educational management system at the level of Romanian educational institutions, a quantitative approach was chosen, using the method of investigating the survey and the working tool-the online questionnaire. The conduct of the questionnaires in the online environment took into account the collection of several opinions / answers from several educational institutions in Romania, in order to have an overall picture of the existing challenges at their level. Data processing was performed using the SPSS Statistics program, version 22, running descriptive and frequency analysis, Cronbach alpha reliability test, Pearson correlation test.
9
Statistical Analysis of Experimental Data
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All data processing was performed using Microsoft Excel 2016® and OriginPro 8.5. Result significance was determined in the IBM SPSS Statistics program, version 22.0. Statistical analysis was performed using the Mann–Whitney test. p < 0.05 was considered statistically significant.
10
Evaluating Emergency Physicians' CT Interpretation
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Data were analyzed using the IBM SPSS Statistics program version 22.0 software package (SPSS Inc., Chicago, IL, USA). Based on the interpretation of the radiology instructor, the false-positive rate, false-negative rate, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio, and negative likelihood ratio values and kappa coefficient (κ) of the EPs’ interpretations of CT scans were calculated. Concordance was determined according to κ values. A κ value of >0.75 was regarded as perfect concordance, 0.40–0.75 as moderate concordance, and <0.40 as weak concordance.[3 (link)] Finally, the interpretation accuracy levels of EPs were compared. For the analysis of demographic data, frequency distribution and the Chi-squared test were used. A P < 0.05 was considered statistically significant.
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