Data analysis was conducted using SPSS Software 26 using Model 14 with a 95% bootstrap interval for the coefficient in Hayes’ (2020 ) PROCESS (3.5.3). As there was no significant difference between the face-to-face and online groups regarding gaming disorder (t255 = 0.80, p = .43), the data were combined and analyzed. Assumptions of normality, homoscedasticity, linearity, and multicollinearity were also assessed. Accordingly, the data were not normally distributed and lacked homoscedasticity. Therefore, heteroscedastic standard errors and bootstrap confidence intervals are preferred for the significance tests (Hayes, 2020 ). In addition, because the amount of missing data for each variable did not exceed 5% and the missing values were MCAR (Chi-square = 10.3; p = .11), the missing data were imputed using the EM algorithm (Tabachnick & Fidell, 2012 ). Square root transformations were applied to deal with outliers in two variables (gaming disorder and daily game time), and one case of parental controlling mediation was excluded from the analysis. Daily game time was divided into two groups to reduce the effect of the relative variance. In addition, continuous variables that produced interaction terms were centered to avoid multicollinearity.
Spss software 26
Manufactured by IBM
Sourced in United States
SPSS software 26.0 is a statistical analysis tool used for data management, analysis, and presentation. It provides a wide range of statistical techniques, including regression analysis, hypothesis testing, and data visualization. The software is designed to help users gain insights from their data and make informed decisions.
Automatically generated - may contain errors
Lab products found in correlation
Prism 8, by GraphPad (3 mentions)
Originpro 2021, by OriginLab (2 mentions)
Spss v26, by IBM (1 mentions)
Design expert software version 7, by Stat-Ease (1 mentions)
Prism 9, by GraphPad (1 mentions)
Origin software, by Malvern Panalytical (1 mentions)
R software v3, by GraphPad (1 mentions)
Originpro 2017, by OriginLab (1 mentions)
Origin 2022, by OriginLab (1 mentions)
Prism 7, by GraphPad (1 mentions)
Masterview 1, by AB Sciex (1 mentions)
Markerview, by AB Sciex (1 mentions)
Peakview 2, by AB Sciex (1 mentions)
Analyst tf 1, by AB Sciex (1 mentions)
67 protocols using spss software 26
1
Analyzing Gaming Disorder and Mediators
2
Multivariate Analysis of Research Survey Data
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The descriptive univariate and bivariate analyses were carried out using the IBM SPSS v. 26© statistical software.22 The Kolmogorov–Smirnov test was initially performed, obtaining a p-value of < 0.005, so a non-normal data distribution was considered. Therefore, non-parametric tests were used. The Mann–Whitney U and Kruskal–Wallis tests were used for contrast tests. Kendall’s Tau-b test was also used to study the correlation between two quantitative variables.
Cronbach’s alpha calculation was performed for the reliability study. In addition, an exploratory factor analysis was carried out to study the dimensional structure of the scale. For this purpose, the maximum likelihood extraction method and varimax rotation were selected, eliminating items with loadings < 0.05.
Finally, for the study of the relationship between the study variables, and given their qualitative nature, a categorical regression analysis (CATREG) was carried out. This method quantifies the categorical data by assigning numerical values to the categories to obtain an optimal linear regression equation for the transformed variables.23 CATREG analysis includes characteristic aspects of classical regression analysis: coefficient of determination (R2), variance, regression analysis, and significance of model parameters.24 For the calculation, the optimal scaling option was selected in the SPSS© software 26.0.22
Cronbach’s alpha calculation was performed for the reliability study. In addition, an exploratory factor analysis was carried out to study the dimensional structure of the scale. For this purpose, the maximum likelihood extraction method and varimax rotation were selected, eliminating items with loadings < 0.05.
Finally, for the study of the relationship between the study variables, and given their qualitative nature, a categorical regression analysis (CATREG) was carried out. This method quantifies the categorical data by assigning numerical values to the categories to obtain an optimal linear regression equation for the transformed variables.23 CATREG analysis includes characteristic aspects of classical regression analysis: coefficient of determination (R2), variance, regression analysis, and significance of model parameters.24 For the calculation, the optimal scaling option was selected in the SPSS© software 26.0.22
3
Analyzing Antioxidant Properties of Plant Extracts
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All experimental data in the chart were presented as mean ± standard deviation, and three replicate experiments were carried out. Duncan's multiple range test and the least significant difference (LSD) test were used to evaluate the differences between groups. Statistical analysis was performed using SPSS software 26 (SPSS Inc., Chicago, IL, USA); P < 0.05 was considered statistically significant.
4
Quantitative Analysis of Protein Expression
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All data are represented as mean values ± SD, and each statistical analysis is detailed in the figure legend. Data distribution was assumed to be normal, but this was not formally tested. Statistical analyses of the data were performed with SPSS software 26 (IBM). n indicates the number of biological replicates in an experiment unless otherwise mentioned.
5
Risk Factors for Acute Mountain Sickness
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Continuous variables were presented as mean ± standard deviation. Differences in measurements between men and women with normal distribution were tested using an independent sample t-test, while the data that did not fit a normal distribution were analyzed by the Mann–Whitney U-test. Categorical data were presented as percentages (%) and were compared by the chi-square test, continuity correction, or Fisher's exact test, as appropriate. Binary and multivariate logistic regression was used to predict the risk factors of AMS, and receiver operating characteristic (ROC) curve was computed to evaluate the effectiveness of the prediction. Given the collinearity between variables and the number of subjects available, variables for inclusion were carefully chosen in our model. In addition, due to headache being the core symptom of AMS, we used a multiple linear regression model to evaluate the relationship between the risk factors of AMS and HA-related headache. Statistical significance was assumed at p < 0.05. Statistical analyses were performed by using SPSS software 26 (IBM, Armonk, NY, USA).
6
High-Altitude Blood Pressure Changes and Acute Mountain Sickness
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Continuous variables were presented as mean ± standard deviation. Differences in measurements between males and females with normal distribution were tested using independent-sample T test, while the data that did not fit a normal distribution were analyzed by the Mann–Whitney U test. Changes in BP and other indexes from LA to HA were compared using a 2 × 2 mixed-model analysis of ANOVA. Categorical data were presented as percentage (%) and were compared by the chi-square test, continuity correction, or Fisher exact test, as appropriate. Spearman correlation coefficients were used to determine the correlation between the different BP index variations after acute HA exposure and AMS, as well as different AMS symptom severity. Statistical significance was assumed at P < 0.05. Statistical analyses were performed by SPSS software 26 (IBM, Armonk, NY, United States). Statistical power calculations were performed using the PASS software, version 11 (NCSS, LLC, Kaysville, UT, United States). Results suggested that 46 subjects would provide more than 75% power to detect morning systolic blood pressure (MSBP) and MSBPS differences between subgroups using a two-sided alpha of 0.05.
7
Cardiovascular Responses to Altitude
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Continuous variables were presented as mean ± standard deviation. Differences in measurements at SL and HA were tested using a paired t test if they showed a normal distribution, and data that did not fit a normal distribution were analyzed with a Wilcoxon rank sum test. Differences in measurements between the non‐DP group and DP group were tested with an independent‐samples T‐test and Mann–Whitney U‐test. Categorical data were presented as numbers and were compared using the chi‐square test, continuity correction, or Fisher's exact test as appropriate. A Pearson coefficient was used to determine the correlation between BP and LV mechanical index. P < .05 was considered statistically significant. Statistical analyses were performed using the SPSS software 26 (IBM).
8
Parkinson's Disease Biomarker Analysis
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SPSS software 26 (IBM Corp., Armonk, NY, USA) and MATLAB 2018 were used for the statistical analysis. The Kolmogorov–Smirnov test was used to determine whether the variables conformed to a normal distribution. For variables that fitted a normal distribution, the Student t-test was used. The Wilcoxon rank-sum test was adopted for variables that did not conform to a normal distribution. The significance level was established at 0.05 (two-tailed) and amended based on the number of comparisons [24 (link)]. The false discovery rate was introduced in the statistical analysis of each band to prevent type I errors [24 (link)]. The Bonferroni multiple-comparison test was used for correction. The Spearman correlation analysis was used to correlate the PSD of the extracted bands and the three eigenvalues with the UPDRS scale. The significance level was p < 0.05. Due to the exploratory nature of the study, no correction for multiple comparisons was used.
9
Living Donor Liver Transplantation Protocol
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Results were expressed as mean ± standard deviation or median (range). Paired sample Student’s t test was used for comparative analysis of results before and after transplantation, and P < 0.05 was considered a significant difference. Statistical analyses were performed using SPSS software 26.0 (SPSS, Inc., Chicago, IL, USA).
This study was reviewed and approved by the Ethical Committee of Hospital (No. 2020-P2-094-01) and conducted according to the ethical guidelines of the Declaration of Helsinki and the Declaration of Istanbul. We declare that all cases of LDLT were approved by the Ethical Committee of the Hospital, and all living donors were voluntary and altruistic.
This study was reviewed and approved by the Ethical Committee of Hospital (No. 2020-P2-094-01) and conducted according to the ethical guidelines of the Declaration of Helsinki and the Declaration of Istanbul. We declare that all cases of LDLT were approved by the Ethical Committee of the Hospital, and all living donors were voluntary and altruistic.
10
Xuesaitong vs. Aspirin Randomized Trial
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We use EDC system to collect the data. The system administrator establishes the eCRF and conducts logic verification and range checks for data values. Two investigators fulfill double-data entry.
The analysis will be performed in a modified intention-treat population, which included all randomized participants who received at least one dose of Xuesaitong or aspirin. Continuous variables are presented as the mean ± SD and categorical variables as number and percentage. Comparisons between treatment groups were assessed using Student's t-test or Wilcoxon rank-sum test for continuous variables. Comparisons were assessed using the Fisher exact test for categorical variables. Data for patients who were lost to follow-up were censored at the time of the last contact. A multivariable Cox proportional-hazards regression analysis that included center and treatment as independent variables was conducted, and the results are expressed as hazard ratio (HR) with 95% confidence interval (CI). The incidence of adverse events was compared using the chi-squared test. P < 0.05 (two-sided) was considered to be statistically significant. All analyses were conducted using SPSS software 26.0.
The analysis will be performed in a modified intention-treat population, which included all randomized participants who received at least one dose of Xuesaitong or aspirin. Continuous variables are presented as the mean ± SD and categorical variables as number and percentage. Comparisons between treatment groups were assessed using Student's t-test or Wilcoxon rank-sum test for continuous variables. Comparisons were assessed using the Fisher exact test for categorical variables. Data for patients who were lost to follow-up were censored at the time of the last contact. A multivariable Cox proportional-hazards regression analysis that included center and treatment as independent variables was conducted, and the results are expressed as hazard ratio (HR) with 95% confidence interval (CI). The incidence of adverse events was compared using the chi-squared test. P < 0.05 (two-sided) was considered to be statistically significant. All analyses were conducted using SPSS software 26.0.
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