Demographics and baseline characteristics were summarized using descriptive statistics. Patients were categorized into groups by their ATI status and the clinical activity. Differences between the groups were compared using the Student t test. Correlations among IFX-TLs/ATI levels and the clinical activity were analyzed with logistic regression analysis. The diagnostic power of IFX-TLs was investigated using area under the receiver-operator characteristic (ROC) curve analysis to obtain the area under the curve (AUC) and 95%CI. The cutoff value for the IFX-TLs that identified disease activity was determined by identifying the point closest to the 1.0 angle. Data were evaluated with SPSS® statistics version 23.0 for Windows (SPSS Inc., Chicago, IL) and P < 0.05 was considered statistically significant.
Spss statistics version 23.0 for windows
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SPSS Statistics version 23.0 for Windows is a statistical software package developed by IBM. It provides a comprehensive set of tools for data analysis, including data management, data exploration, modeling, and reporting. The software is designed to handle a wide range of data types and can be used for a variety of statistical analyses, such as regression, correlation, and hypothesis testing.
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Lab products found in correlation
46 protocols using spss statistics version 23.0 for windows
1
Predicting Disease Activity via IFX-TLs
2
Physical Activity Patterns in Adolescents
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Data management and analysis were performed using SPSS statistics version 23.0 for Windows (SPSS INC. Chicago IL USA). Data were expressed as means with standard deviation or medians with interquartile range, depending on their distribution. The distribution of the data was tested for normality by the Shapiro -Wilk test for small groups. Categorical variables were expressed as numbers with percentages. Comparisons of the age, sex distribution and time since surgery of patients who did and who did not participate in the study were done by the Mann-Whitney U or Chi-Square tests, where appropriate. For all analyses, patients were divided into two groups; 4-11 years old and 12-16þ years old, based on age matching questionnaires concerning physical activity and sedentary activity. For all analyses, the level for statistical significance was set at p < 0.05.
The results of the two different age groups are presented in percentages and compared with each other. The involvement in physical activity was also compared with the reference group in percentages.
The results of the two different age groups are presented in percentages and compared with each other. The involvement in physical activity was also compared with the reference group in percentages.
3
Children's Dietary Responses to Food Advertising
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Data were analysed separately by media condition. Linear mixed models with repeated measures were used to examine relationships between children's consumption responses (kJ) at the different eating occasions and dichotomised feeding practice scores. The three different primary outcomes were kJ consumed at the snack, lunch, and snack and lunch combined repeated across the two advertising conditions. The fixed-factor effects used in all models were advertising condition (food or non-food advertising) and feeding practice scores. The interaction between advertising condition and feeding practice scores was tested for significance in all models. Camp identifier was included as a random intercept in the models in order to adjust for the clustered nature of the data. Any influence of the impact of age, sex, weight status (BMI z-score), or hunger on snack and lunch intake were investigated by adding these variables as covariates to the models.
Descriptive statistics are reported as means (±SDs) for continuous variables or as percentages for categorical variables. Results from the linear mixed models analyses are presented as means (95% CIs) unless otherwise indicated. Reported p values are two-sided, and p<0.05 was considered significant in all tests. Analyses were completed using SPSS Statistics version 23.0 for Windows (SPSS Inc., Chicago, IL, USA).
Descriptive statistics are reported as means (±SDs) for continuous variables or as percentages for categorical variables. Results from the linear mixed models analyses are presented as means (95% CIs) unless otherwise indicated. Reported p values are two-sided, and p<0.05 was considered significant in all tests. Analyses were completed using SPSS Statistics version 23.0 for Windows (SPSS Inc., Chicago, IL, USA).
4
Biomarker Associations in Treatment Outcomes
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Statistical analysis was performed using IBM SPSS Statistics version 23.0 for Windows (Statistical Package for the Social Sciences, USA). The following descriptive statistics were reported: proportions with their 95% confidence intervals for dichotomous variables and medians with their interquartile ranges (IQR) for continuous variables. Comparisons of continuous variables between groups were made using Mann-Whitney U test for independent samples, as there were relatively few observations and no normal distribution. For correlation Spearman’s coefficient was used. To estimate the risks of non-conversion and death by biomarker levels, we used logistic regression analysis. We also estimated the association between treatment outcomes, culture conversion and biomarkers using general linear model for repeated measures. P ≤ 0.05 was considered statistically significant.
5
Determinants of eHealth Literacy in Adults
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Data were entered and analyzed using IBM SPSS Statistics version 23.0 for Windows (IBM Corp., Armonk, NY, USA). Descriptive statistics were used to define the participants' demographic characteristics and variables. Independent t-test, one-way ANOVA, and a post-hoc test (Scheffe test) were conducted to examine differences in eHealth literacy according to the sex and grade of participants. Mann-Whitney U-test was used when the assumptions of the t-test were not met. Pearson correlation coefficients were conducted to examine the association among frequency of hospital admission, number of diagnosed diseases, and eHealth literacy. Finally, multiple linear regression was used to examine all the variables that had a significant relationship with eHealth literacy via a univariate analysis and to determine the best determinants of eHealth literacy. The assumptions of the multiple linear regression analysis conducted via a residual analysis were generally met. A p-value of ≤0.05 indicated statistical significance.
6
Outcome Measures for Radicular Pain
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Preprocedural and postprocedural NRS pain scores, ODI, and duration of walking without radicular pain, and changes in NRS pain score (%), ODI (%), and duration of walking without radicular pain between baseline and the 3-month follow-up visit were analyzed with the Wilcoxon signed-rank test. Outcomes are shown as mean (interquartile range [IQR]), or frequency (%), as appropriate. We assessed the proportion of successful responders, defined as at least 50% decrease in the NRS pain score, accompanied by improvement in the ODI (%) and duration of walking, by the 3-month follow-up visit. Then, differences in outcomes were compared between responders and nonresponders, using the Mann–Whitney test for nonparametric data and Fisher exact test for parametric data.
Statistical analysis was performed using SPSS Statistics version 23.0 for Windows (IBM Corp., Armonk, NY). A P value <.05 was considered statistically significant.
Statistical analysis was performed using SPSS Statistics version 23.0 for Windows (IBM Corp., Armonk, NY). A P value <.05 was considered statistically significant.
7
Analyzing miRNA Levels and Recurrence
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The Shapiro–Wilk test was performed to test the normality of distribution. The Mann–Whitney test, Fisher’s test, and ANOVA, followed by Tukey’s post hoc test, Spearman’s correlation, and the area under curve (AUC) receiver operating characteristic curve analysis were performed with GraphPad Prism 9 (GraphPad Software Inc., San Diego, CA, USA). Multivariate logistic regression analysis was performed to investigate the association between miRNA levels, recurrence, and scar percentage; analysis was performed with IBM SPSS Statistics version 23.0 for Windows (IBM Corp., Armonk, NY, USA). The results of the logistic regression analysis are reported as an odds ratio (OR) and 95% confidence interval (CI). In all analyses, a two-tailed p < 0.05 was considered to be significant.
8
Assessing Dietary Habits and Cardiovascular Risk
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Statistical calculations were performed using IBM SPSS Statistics version 23.0 for Windows (IBM Corp, Armonk, NY, USA). All variables were checked for normality of distribution before the analysis. Differences between the sexes were analyzed by unpaired Student’s t-test. Descriptive data on nonparametric variables (ie, the dietary habits and the Wildman risk score) are presented as median and interquartile range (Q1–Q3). Descriptive data on quantitative variables are presented as mean and standard deviation (SD). Statistical analysis on qualitative variables was performed using Spearman’s correlation (rho) and Chi-2 test and Mann–Whitney U-test. Statistical analysis on quantitative variables was performed using Pearson’s correlation coefficient (r) and two-sample t-test. Level of significance was set at P<0.05 for all analyses.
9
Work Productivity and Work Engagement
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We conducted statistical analysis with complete cases. Univariate and multivariable regression analyses were conducted to examine the association of working hours and work engagement with work productivity. The first model estimated a crude coefficient with univariate regression analysis. Next, we estimated multiple regression model using work productivity as a dependent variable and working hours as an independent variable while controlling for demographic characteristics (age, gender, and educational level). The third model added work engagement to model 2.
Furthermore, in order to assess if work engagement moderate the influence of working hours on work productivity, we carried out stratified multivariable regression analysis separately for those with high‐work engagement and those with low‐work engagement (divided into high and low based on median). This analysis was adjusted for demographic characteristics (age, gender, and educational level). Data were analyzed using IBM SPSS Statistics version 23.0 for windows (IBM Japan, Tokyo, Japan).
Furthermore, in order to assess if work engagement moderate the influence of working hours on work productivity, we carried out stratified multivariable regression analysis separately for those with high‐work engagement and those with low‐work engagement (divided into high and low based on median). This analysis was adjusted for demographic characteristics (age, gender, and educational level). Data were analyzed using IBM SPSS Statistics version 23.0 for windows (IBM Japan, Tokyo, Japan).
10
Sociodemographic Factors and Sleep Disorders
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The prevalence was calculated by dividing the number of cases by the total population. We analyzed the data using the chi-square test for categorical data. Logistic regression analysis was used to compute the relative risk of sociodemographic factors associated with insomnia and EDS symptoms, All reported p values are two-tailed, and p < 0.05 was considered statistically significant. All statistical analyses were performed using IBM SPSS statistics version 23.0 for Windows (IBM Corp., Armonk, NY, USA).
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