Differences between groups (for example CMV- compared to CMV+) were assessed using Mann-Whitney U test, and comparisons within the same individuals (for example to compare time points in response upon influenza virus infection) with the Wilcoxon singed-rank test. Differences between groups in categorical variables were tested by chi-square test and corrected for multiple testing if applicable and indicated in figure legends. Correlations were tested with Spearman’s rank correlation coefficient. For all analyses p values < 0.05 were considered statistically significant. Data were analyzed using GraphPad Prism 8.3 and SPSS statistics 22 for Windows (SPSS Inc., Chicago, IL, USA).
Spss statistics 22 for windows
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Sourced in United States, United Kingdom
SPSS Statistics 22 for Windows is a statistical software package designed for data analysis, management, and presentation. It provides a comprehensive set of tools for handling a wide range of data types, performing various statistical analyses, and generating reports and visualizations. The core function of SPSS Statistics 22 is to enable users to analyze and interpret data effectively.
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Lab products found in correlation
Sas version 9, by SAS Institute (2 mentions)
Prism 6, by GraphPad (2 mentions)
Sas enterprise guide 6.1 for windows, by SAS Institute (2 mentions)
Sas 9, by IBM (1 mentions)
Prism 5, by GraphPad (1 mentions)
Cfx manager software, by Bio-Rad (1 mentions)
Spss 23, by IBM (1 mentions)
R version 3, by IBM (1 mentions)
Amos 24, by IBM (1 mentions)
132 protocols using spss statistics 22 for windows
1
Statistical Analysis of Biomedical Data
2
Gut Microbiome and Cardiorespiratory Fitness
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Data analyses were carried out using SPSS Statistics 22 for Windows (SPSS Inc., Chicago, IL, USA). All data was checked for normality using the Shapiro–Wilk W-test. If data were not normally distributed, their natural logarithms were used. Multiple comparisons were applied by using analysis of variance (ANOVA) with the Sidak post-hoc test.
Linear regression was used to assess the relationship between cardio-respiratory fitness and gut microbiota. The analyses were performed with and without adjusting for age, dietary intake and fat%. The p-values were adjusted by multiple comparisons in FDR (false discovery rate). Statistical significance was set at p < 0.05 with 2-tails.
In addition, multivariable least square regressions were performed in assessing the contribution of the variables (i.e., EreC, age, energy yield nutrients of fat, carbohydrates, protein and alcohol, as well as fat% of the whole body) and the outcome variables (i.e., VO2max, leptin, HDL and TG). To make each outcome comparable, we standardized each column by means of z-scoring so that each column has mean value 0 and standard deviation 1. The Pearson correlation coefficients between the regressed outcome and the observed outcome were calculated and regression weights for each variable were provided. The higher the absolute weight, the higher the contribution.
Linear regression was used to assess the relationship between cardio-respiratory fitness and gut microbiota. The analyses were performed with and without adjusting for age, dietary intake and fat%. The p-values were adjusted by multiple comparisons in FDR (false discovery rate). Statistical significance was set at p < 0.05 with 2-tails.
In addition, multivariable least square regressions were performed in assessing the contribution of the variables (i.e., EreC, age, energy yield nutrients of fat, carbohydrates, protein and alcohol, as well as fat% of the whole body) and the outcome variables (i.e., VO2max, leptin, HDL and TG). To make each outcome comparable, we standardized each column by means of z-scoring so that each column has mean value 0 and standard deviation 1. The Pearson correlation coefficients between the regressed outcome and the observed outcome were calculated and regression weights for each variable were provided. The higher the absolute weight, the higher the contribution.
3
Statistical Analysis of Surgical Outcomes
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Statistical analysis was performed using commercially available software (IBM SPSS statistics 22 for Windows; SPSS, Chicago, IL, USA). Due to the small sample size, we used nonparametric tests in this study; therefore, the quantitative data were reported as median [25th and 752 percentile]. The quantitative data before and after surgery were compared using Wilcoxon signed-rank test. The quantitative post-operative outcomes (ie, post-op AHI) between different preoperative statuses at velum were compared using Mann–Whitney U-test with two groups or Kruskal–Wallis test with three groups. The proportion of surgical success between different preoperative statuses at velum were compared using Fisher’s exact test. Significance was indicated at a two-sided P value <0.05.
4
Electronic Device Usage and Sleep
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IBM SPSS Statistics 22 for Windows (SPSS Inc, Chicago, Illinois, USA) was used for all analyses. χ2 Tests were used to examine gender differences in use of electronic devices and daytime screen use. Independent sample t tests and χ2 tests were used to examine the associations between sleep duration, electronic devices and daytime screen use. Logistic regression analyses using SOL of more than 60 min and sleep deficiency as outcome variables were conducted for all electronic devices and daytime screen (exposure variables). Multinomial logistic regression analyses were conducted with short sleep duration as the outcome variable (8–9 h as the reference category), and electronic devices and daytime screen as the exposure variables. To investigate whether ORs differed significantly between genders, we calculated the relative risk ratio.22 (link) As these analyses yielded no significant gender differences, the results of the logistic regressions are presented without gender stratification.
5
Evaluating Shear Wave Velocity Measurements
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Microsoft Office Excel 2007 (Microsoft, Redmond, WA, USA) was used to collect data. For statistical data analysis, SPSS software (SPSS Statistics 22 for Windows; SPSS Inc., Chicago, IL, USA) was used. For each participant, at each level of applied transducer force, the median of five valid measurements was used as a representative SWV. The mean SWV for each applied transducer force was then compared using repeated measures ANOVA, followed by pairwise comparisons for each exerted force using Bonferroni correction for multiple comparisons. All tests were two sided and statistical significance was assumed when p < 0.05.
For each participant, the interquartile range (IQR, the difference between the 75th percentile and the 25th percentile) and success rate (SR, number of valid measurements divided by total number of measurements) for each level of applied transducer force was recorded. The mean IQR and SR were then calculated across all participants for each magnitude of applied transducer force. Where means were presented, the standard deviation and overall range of the data were included.
For each participant, the interquartile range (IQR, the difference between the 75th percentile and the 25th percentile) and success rate (SR, number of valid measurements divided by total number of measurements) for each level of applied transducer force was recorded. The mean IQR and SR were then calculated across all participants for each magnitude of applied transducer force. Where means were presented, the standard deviation and overall range of the data were included.
6
Statistical Analysis of Experimental Groups
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Differences between the groups were assessed by first using a One-Way ANOVA (or a Kruskal-Wallis, when data was non-parametric). If the One-Way ANOVA was significant, a (post hoc) Mann-Whitney U tests was performed to compare the groups. For all analyses, p-values < 0.05 were considered statistically significant. Data were analyzed using GraphPad Prism 8.3 and SPSS statistics 22 for Windows (SPSS Inc., Chicago, IL, USA).
7
Statistical Analysis of Positive Activity Intervention
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The equivalence of the baseline data between the intervention and control groups was determined using the independent t-test, Mann–Whitney U test, and chi-squared test. The statistical significance of any inter-group differences in class participation and program adherence rates was examined using the chi-squared test, while an independent t-test was used for the mean class attendance rates. The Mann–Whitney U test was used to examine changes in participants' GDS scores as a result of the intervention. An analysis of covariance was used to examine between-group differences in the groups' pre- and post-intervention changes using group and time as explanatory variables and baseline GDS score, gender, and age as covariates. McNemar's test was used to compare the rates of the participants who responded positively (answers 1 and 2) to the questions in each positive activity before and after the group interventions. A z-test was used to analyze inter-group differences in changes in participants' desire to pursue the positive activities based on the assumption that the between-group difference of this variable was normally distributed. SPSS Statistics 22 for Windows was used for all analysis, and the significance level was set at 0.05.
8
Cross-over Study on Endothelial Function
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Based on a previous study [17 (link)], 32 people are required for a cross over study to have 80% power, p < 0.05 to detect at least a 2.0% FMD absolute change. Analyses was performed using SPSS Statistics® 22 for Windows (SPSS Inc, Chicago, IL, USA). An ANOVA with repeated measures was run to analyze outcomes (with and without covariates including, age, diet order and BP). Associations between variables were evaluated with a Pearson’s correlation. Results are expressed as mean ± SDs unless otherwise stated.
9
Nonparametric Statistical Analysis Protocol
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Differences between the groups (for example, vaccinated versus unvaccinated) were assessed using Mann–Whitney U-tests. Paired data (differences between timepoints or differences between epitopes) were compared using the Wilcoxon rank test (nonparametric).
Correlations were tested with Spearman’s rank correlation coefficient. For all analyses, p-values < 0.05 were considered statistically significant. Data were analyzed using GraphPad Prism 8.3 and SPSS statistics 22 for Windows (SPSS Inc., Chicago, IL, USA).
Correlations were tested with Spearman’s rank correlation coefficient. For all analyses, p-values < 0.05 were considered statistically significant. Data were analyzed using GraphPad Prism 8.3 and SPSS statistics 22 for Windows (SPSS Inc., Chicago, IL, USA).
10
Epidemiology of Hypertension and Obesity
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To answer our research questions, we used Student t-tests, Chi-square tests, and logistic regressions. Results were expressed as mean ± standard deviation for continuous variables or as percentages for categorical variables. Bivariate comparisons were performed using Student t-tests for continuous variables, and Chi-square tests for categorical variables. Multivariate analyses were performed using binary logistic regression and results were expressed as odds ratios with 95% confidence intervals (CIs). In these binary logistic regression models, dichotomous outcome variables were: HTN, awareness and treatment; as well as general and central obesity. The software used for the statistical analysis was SPSS Statistics 22 for Windows.
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