Descriptive quantitative analyses were conducted using STATA BE (Version 17.0). We examined the itemized distribution of each behavioral economic measure by total and by gender (e.g., male and female). Non-binary and non-gender conforming categories were omitted given that all participants identified as cisgender. Qualitative data were analyzed using Dedoose (Version 9.0.62). We used dual deductive and inductive content analyses. First, we deductively coded responses according to previously identified BE topics in the interview guide. Second, after re-reading participants’ transcripts, we inductively coded responses within each BE topic based on emerging patterns in participants’ explanations. Exemplary quotations were extracted to support findings and labeled with the participant’s gender and age.
Stata be
Manufactured by StataCorp
Sourced in United States
Stata/BE is a software product developed by StataCorp. It provides a comprehensive data analysis and statistical computing environment. Stata/BE is designed to handle a wide range of data types and offers a variety of statistical tools and procedures for data management, analysis, and visualization.
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Lab products found in correlation
5 protocols using stata be
1
Behavioral Economics Measure Analysis
2
Statistical Analysis of Learning Outcomes
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The statistical analysis of the students’ answers to the before-VP and after-VP questionnaires was performed using Stata/BE (version 17; StataCorp LLC). Median scores with IQRs were calculated, and differences in scores before and after the learning activity were analyzed using the Wilcoxon signed rank test for paired measurements. P values less than .05 were considered statistically significant.
3
Statistical Analysis of Treatment Response
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Stata BE (StataCorp, College Station, TX, USA) was used for all statistical analyses. Baseline characteristics are reported as mean±sd or median (interquartile range) for nonparametric data. In regards to group response to treatment, a paired t-test and Wilcoxon signed-rank test were used to determine significant changes in parametric and nonparametric parameters, respectively. Pearson's correlation coefficient was calculated to evaluate the association between bivariate, continuous, normally distributed data. Statistical significance was defined as p<0.05 for a two-tailed test.
4
Observational Data Analysis Protocol
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All assessment data, including demographics, were summarized using descriptive techniques. Summary statistics (mean, standard deviation, median, minimum, and maximum values) are presented for continuous variables. Statistical analyses were based on patients with observed data. Counts and percentages are presented for categorical and binary variables. All analyses were conducted using MS Excel v10 and Stata BE (version 17, StataCorp, College Station, TX).
5
Evaluating a Digital Diabetes Intervention
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All analyses used Stata/BE (version 17.0; StataCorp) and were performed on data from participants who attended the 12-month follow-up; other participants were considered dropouts. Baseline characteristics of all participants allocated to the intervention and control groups were analyzed descriptively. The statistical significance of differences in baseline characteristics of the participants who attended the 12-month follow-up was assessed with the Student t test and the Kruskal-Wallis test. The statistical significance of between-group differences in outcomes at 6 and 12 months was assessed with either a 1-way ANOVA or the chi-square test. Statistical significance was set at 2-tailed P<.05. In addition, we performed a per protocol analysis by using only data from participants who had been using the eHealth tool for 365 days or more. Finally, we performed a regression analysis that included an interaction term to determine whether participants with T2D responded differently to the intervention.
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