All data were stored and analyzed using the SPSS statistical package 23.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were computed for continuous and categorical variables, including median and interquartile ranges (IQR) of ordinal variables, mean and standard deviations of continuous variables, and frequencies and relative frequencies of categorical factors. The Wilcoxon signed-rank test was used to test for differences in continuous and categorical variables within the groups. To test for between-group differences, the Mann–Whitney U test was used for continuous variables and Fisher’s exact test for categorical variables, as appropriate. All P-values were two-sided statistical tests, and P < 0.05 was considered significant. A Cox proportional hazards model was used for multivariate analysis to assess the independence of pain on prognostic factors for a good response at follow-up.
Spss statistical package 23
Manufactured by IBM
Sourced in United States
SPSS Statistical Package 23.0 is a comprehensive data analysis software developed by IBM. It provides a wide range of statistical techniques for data management, analysis, and visualization. The software is designed to help users gain insights from their data through advanced statistical methods, including regression analysis, factor analysis, and time series analysis.
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Lab products found in correlation
12 protocols using spss statistical package 23
1
Multivariate Analysis of Pain Response
2
Multivariable Analysis of CBCL Outcomes
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The SPSS statistical package 23.0 (IBM Corporation, Armonk, NY, USA) was used for our data analysis.
Mean (standard deviation [SD] ) and frequency (%) were reported for descriptive purposes. To perform multivariable analyses, two multiple multivariable regressions were performed for each outcome. First, we ruled out collinearity between our independent variables. We also tested the error terms of our regression model. We did not find any evidence against the assumptions of the multivariable regression model. All our models were performed in the pooled sample. These models controlled for age, gender, and marital status. Model 1 was performed without the interaction terms. Model 2 also included two interaction terms between ethnicity and family income. In each model, one CBCL domain was the outcome. Unstandardized regression coefficient (b), p-value, and sample size were reported for each model. We performed a sensitivity analysis by treating income as a categorical variable (quartiles). As the result did not change, we only reported the result of our main analysis, where family income was treated as a continuous measure. Data were not imputed because they were missing in less than 5%.
Mean (standard deviation [SD] ) and frequency (%) were reported for descriptive purposes. To perform multivariable analyses, two multiple multivariable regressions were performed for each outcome. First, we ruled out collinearity between our independent variables. We also tested the error terms of our regression model. We did not find any evidence against the assumptions of the multivariable regression model. All our models were performed in the pooled sample. These models controlled for age, gender, and marital status. Model 1 was performed without the interaction terms. Model 2 also included two interaction terms between ethnicity and family income. In each model, one CBCL domain was the outcome. Unstandardized regression coefficient (b), p-value, and sample size were reported for each model. We performed a sensitivity analysis by treating income as a categorical variable (quartiles). As the result did not change, we only reported the result of our main analysis, where family income was treated as a continuous measure. Data were not imputed because they were missing in less than 5%.
3
Factors Influencing Stress Resilience
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GLM repeated measures analysis was performed to examine the effects of independent variables (gender, age, specialization status, employment sector, leadership position, on-call burden, and time pressure) on the development of SRIS over the study period. The associations of Mauchly’s Test of Sphericity indicated that the assumption of sphericity had been violated, (p < .001), and therefore, a Greenhouse-Geisser correction was used. All analyses were conducted using the SPSS statistical package 23.0.
4
One-way ANOVA Statistical Analysis
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One-way analysis of variance ANOVA was conducted for statistical analysis of data using SPSS Statistical Package 23.0 (SPSS Inc., Chicago, USA). The Tukey post hoc test for pairwise comparisons was applied when required. A P-value of ≤ 0.05 was considered statistically significant.
5
Statistical Analysis of Continuous Variables
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All data were stored and analyzed using the SPSS statistical package 23.0 (SPSS Inc. Chicago, Illinois, USA). Descriptive statistics were computed for continuous variables, including mean and standard deviations. Because the Kolmogorov-Smirnov test did not reject the normal distribution hypothesis, testing for differences in continuous variables between study groups was accomplished with a 2-sample t test using the Bonferroni correction. For several parameters, evolution over time was described using specific linear or nonlinear regression analyses with best curve-fitting according to the coefficient of determination.
6
Evaluating Right Ventricular Dysfunction
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We analyzed the data using the SPSS statistical package, 23.0. We verified data normality using the Shapiro–Wilk test. We characterized patients with and without RV dysfunction using contingency tables with absolute (n) and relative (%) frequency values for categorical variables and descriptive statistics (mean and standard deviation) for continuous variables. We compared patients with and without RV dysfunction using Student’s t, Mann–Whitney’s, and Pearson’s Chi-square tests, adopting a significance level of 5%. We performed a Pearson correlation to verify the relationship between myocardial deformation parameters and classic parameters of right ventricular dysfunction. We conducted a binary logistic regression analysis to evaluate which of the myocardial deformation parameters best correlates with the diagnosis of RV dysfunction according to the classic parameters.
7
Growth and Survival Assessment of Fish
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Weight gain rate (WGR, %)=100×(weight gain, g)/(initial weight, g); Survival rate (%)=100×(final number of fish)/(initial number of fish); Feed conversion ratio (FCR)=(dry dietary intake, g)/(weight gain, g). Specific growth rate (SGR, % per day)=100×[Ln (final weight, g)−Ln (initial weight, g)]/duration (days); Condition factor (CF)=100×(body weight, g)/(body length, cm)3; survival rate (SR, %)=100×(final fish number/initial fish number); Daily feed intake (DFI)=(feed consumed, g)/[(initial weight+final weight)/2, g]×(days, d).
All data in tables and figures are expressed as mean±standard deviation (SD). After normality and homogeneity checking, one-way variance analysis (ANOVA) and Duncan’s multiple range tests were used to examine the data. p values <0.05 were considered significantly different. The SPSS statistical package 23.0 (SPSS Inc., Chicago, IL, USA) was used for statistical analysis. The sigma plot software version 14.0 is used to draw column graphs and curves.
All data in tables and figures are expressed as mean±standard deviation (SD). After normality and homogeneity checking, one-way variance analysis (ANOVA) and Duncan’s multiple range tests were used to examine the data. p values <0.05 were considered significantly different. The SPSS statistical package 23.0 (SPSS Inc., Chicago, IL, USA) was used for statistical analysis. The sigma plot software version 14.0 is used to draw column graphs and curves.
8
Quantitative Analysis of Experimental Treatments
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All experiments were performed in triplicate and repeated at least twice to ensure the reliability of data. Statistical analysis was conducted using the statistical product and service solutions (SPSS) statistical package 23.0 (SPSS Inc. Chicago, USA). The effects of the treatments were determined by analysis of variance (ANOVA), and significant differences (P < 0.05) were separated using the Waller–Duncan multiple range test.
9
Performance Evaluation of Fish Growth
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Weight gain rate (WGR, %) = 100 × (weight gain, g) / (initial weight, g); Survival rate (%) = 100 × ( nal amount of sh) / (initial amount of sh); Condition factor (CF) = 100 × [(body weight, g) / (total length, cm) 3 ]; Feed conversion ratio (FCR) = (dry dietary intake, g) / (weight gain, g).
After normality and homogeneity checking, One-way variance analysis (ANOVA) and Duncan multimultiple-range analyses were used to examine the data. P values < 0.05 were considered signi cantly different. The SPSS statistical package 23.0 was used for statistical analysis (SPSS Inc., Chicago, IL, USA). The GraphPad Prism software version 9.0 is used to draw column graphs.
After normality and homogeneity checking, One-way variance analysis (ANOVA) and Duncan multimultiple-range analyses were used to examine the data. P values < 0.05 were considered signi cantly different. The SPSS statistical package 23.0 was used for statistical analysis (SPSS Inc., Chicago, IL, USA). The GraphPad Prism software version 9.0 is used to draw column graphs.
10
Evaluation of Delirium Symptoms
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All statistical calculations were carried out using the IBM SPSS 23 statistical package (Armonk, NY, USA) and an Excel 2016 spreadsheet. The statistical analysis included the baseline measurement adjusted to the variables, mean, standard deviation, N (%). The following rules were adopted: p < 0.05—statistically significant relationship (marked *); p < 0.01—highly significant relationship (marked **); and p < 0.001—extremely significant relationship (marked as ***).
In order to check if the symptoms of delirium improved, an ANOVA (F) test was used. The level of significance was p < 0.05. As a result of the analysis, statistically significant differences in the number of patients with delirium at particular time points were detected (F = 13.229, p < 0.00002).
In order to check if the symptoms of delirium improved, an ANOVA (F) test was used. The level of significance was p < 0.05. As a result of the analysis, statistically significant differences in the number of patients with delirium at particular time points were detected (F = 13.229, p < 0.00002).
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