Data were managed and analyzed using Statistical Package for the Social Sciences version 25 (SPSS Inc.). Chi‐Square test was used to analyze demographic and anthropometric characteristics concerning different dietary patterns. Analysis of variance (anova ) was used to evaluate statistical differences in control and intervention groups at significance p < .05, confidence level is 95%.
Statistical package for the social sciences version 25
Manufactured by IBM
Sourced in United States, Japan
SPSS Statistics is a software package used for statistical analysis. Version 25.0 includes tools for data management, statistical modeling, and advanced analytics.
Automatically generated - may contain errors
Lab products found in correlation
Sas v9, by SAS Institute (2 mentions)
Spss statistics for windows version 25, by IBM (2 mentions)
Version 20, by MedCalc (2 mentions)
Jmp pro, by SAS Institute (2 mentions)
Stata 14, by StataCorp (1 mentions)
Matlab, by MathWorks (1 mentions)
Statistical package for the social science, by IBM (1 mentions)
Prism 7, by GraphPad (1 mentions)
Spss amos 22, by IBM (1 mentions)
R software version 3, by R Project for Statistical Computing (1 mentions)
Indesign cc, by Adobe (1 mentions)
Sas sas9, by SAS Institute (1 mentions)
Prism version 5, by GraphPad (1 mentions)
Prism version 8, by GraphPad (1 mentions)
276 protocols using statistical package for the social sciences version 25
1
Dietary Patterns Statistical Analysis
2
Evaluating Bone Reconstruction Accuracy
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Reconstruction accuracy of pelvis and femur geometric models were evaluated using two metrics. First, the distance error between (in)complete bone segmentation (i.e. a point cloud) and the closest nodes on reconstructed bone models was calculated using the root mean square error (RMSE, mm). The RMSE is an average error which has been applied in previous studies for comparing bone reconstruction accuracy [9 (link), 16 (link), 24 (link)]. Second, the overlapping volume similarity of complete bone segmentation and reconstructed geometric bone model was calculated using the Jaccard index (%) [25 (link)]. The Jaccard index was calculated in addition to the RMSE, because it accounts for absolute differences in overlapping volume of geometric bone models. The accuracy metrics of the different bone reconstruction methods were statistically compared (p<0.05) using a repeated measure analysis of variance followed by multiple pairwise comparisons with Bonferroni adjustment in the Statistical Package for the Social Sciences version 25 (SPSS Inc., Chicago, United States).
3
Evaluating Healthcare Knowledge Levels
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The survey was scored using the guidelines of Rhoda and Pickel-Voight (2015 (link)), tallying the number of correct responses in each section. Each participant’s level of knowledge was classified as follows: A score of ≥ 75% was ‘high’, 50% – 74% was ‘moderate’ and a score of ≤ 50% was ‘low’ (Rhoda & Pickel-Voight, 2015 (link)). Data were analysed using the Statistical Package for the Social Sciences version 25, using descriptive and inferential statistics in consultation with a statistician. The Cronbach’s alpha coefficient equalled 0.707, demonstrating the reliability of the questionnaire (Goforth, 2015 ). P-values of <0.05 were considered statistically significant. Variability of responses was noted across nurses and within levels.
4
Fetuin-B and IVF-ET Outcomes
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Data analysis was performed with the Statistical Package for the Social Sciences, version 25 (SPSS Inc., Chicago, USA) and R version 3.6.1 (http://www.r-project.org ). Data are expressed as mean ± SD, median (Q1, Q3), or proportion (%), as appropriate. Variables with skewness less than ±1 were considered as following a normal distribution, and covariates were natural logarithmic transformed if skewed. Comparison for numeric variables between groups was performed using Student's t-test or Mann–Whitney U test. Chi-square or Fisher's exact tests were used as appropriate for categorical variables. Correlations between the level of fetuin-B and other variables were quantified using Pearson and partial correlations. Logistic regression analysis was conducted to analyse the relationship between fetuin-B and the clinical outcomes of IVF-ET. P values < 0.05 (two-tailed) were considered statistically significant.
5
Analyzing Stress Adaptation and Inflammatory Markers
6
Analyzing renal function and risk factors
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The above data were input into the Statistical Package for the Social Sciences version 25 (SPSS Inc., Chicago, US). All p values obtained were based on two-tailed tests, with significance levels set at 0.05. In the descriptive analysis, continuous variables were described as means and standard deviation (SD), and dichotomous variables were described as numbers and corresponding percentages. Single-sample t-test and chi-squared test were used to compare differences in continuous and dichotomous variables, respectively, between the groups with different characteristics. The Pearson correlation coefficient was applied to estimate the relationships between eGFR and variables, including age, smoking status, and drinking status. Univariate and multivariate linear regression were applied to analyze the effects of different variables on eGFR.
7
Evaluating Suicide Intervention Service
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Analyses will be conducted using the Statistical Package for the Social Sciences version 25 [85 ] and SAS v9.4 [86 ]. Primary analyses will be conducted according to the intention-to-treat approach. Presentations to an emergency department with a repeat suicide-related event and for all-cause death will be compared between the two non-randomised groups (intervention vs control) using a Kaplan Meier plot where the initial presentation to an emergency department is designated as time zero, and using censoring. A log-rank test will compare the Kaplan Meier curves of the two groups.
A negative binomial regression model will be applied to the data to estimate the effect of the intervention on re-presentation rates, accounting for varying lengths of follow-up period and adjusting for service and person-level characteristics (e.g. facility, age, sex, indigenous status) and censoring.
To investigate the impact of the service on suicide risk factors (secondary outcome measures), a comparison of pre- and post-intervention outcome measures will be conducted for participants of the intervention using paired t-tests, or a Wilcoxon signed-ranks test (if the sample is non-normally distributed), adjusting for multiple comparisons (Bonferroni correction).
A negative binomial regression model will be applied to the data to estimate the effect of the intervention on re-presentation rates, accounting for varying lengths of follow-up period and adjusting for service and person-level characteristics (e.g. facility, age, sex, indigenous status) and censoring.
To investigate the impact of the service on suicide risk factors (secondary outcome measures), a comparison of pre- and post-intervention outcome measures will be conducted for participants of the intervention using paired t-tests, or a Wilcoxon signed-ranks test (if the sample is non-normally distributed), adjusting for multiple comparisons (Bonferroni correction).
8
Hierarchical Regression Analysis of Psychological Factors
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Data were analyzed using the Statistical Package for the Social Sciences, version 25 (SPSS, Chicago, IL). Statistical tests were two-tailed with α set at 0.05. Descriptive statistics were calculated to provide information about participants’ characteristics and the prevalence of the main variables of the study. In order to test our main hypotheses, we used a hierarchical regression model to test both main and interaction effects.
9
Comparing EEG Power Dynamics in EC and EO
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The Statistical Package for the Social Sciences version 25 software was used for statistical analysis. The distribution characteristics of the variables were examined using the Shapiro–Wilk test. Normally distributed independent data were evaluated using the t-test, and nonparametric independent samples were evaluated using the Mann–Whitney U-test. Power values of EEG bands in EC and EO conditions were compared using the paired-samples t-test and Wilcoxon test for parametric and nonparametric evaluations, respectively. Frequency band power changes due to the transition from EC to EO conditions were evaluated using the following formula:
(for each frequency band). The percentage change of the RS of power in each frequency band was compared between the SH and control groups.
(for each frequency band). The percentage change of the RS of power in each frequency band was compared between the SH and control groups.
10
Comparative Analysis of African and European Team Performance
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Data were reported as means and standard deviations. An independent-sample t-test was used to compare differences in performance indicators between African and European teams. Additionally, effect size (ES) was used to examine the magnitude of mean differences between the two groups of teams. ES values were classified as 0.01 (small effect), 0.06 (moderate effect) and 0.14 (large effect) (Cohen, 1988 ). The probability level was set at p ≤ 0.05 and all statistical analyses were performed using the Statistical Package for the Social Sciences version25.
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