Univariate frequencies were generated for the dependent and independent variables, on the total sample (N=383) and also separately by gender (Men/Women). Next, unadjusted incidence rate ratios were calculated. Variables marginally significant at the bivariate level (p<.10) [59 ], in at least one group, were entered into a multivariate Poisson regression, to regress UVL on correlates separately by gender. Poisson regression is appropriate for binary outcomes for non-rare events. Robust standard errors accounted for heteroskedasticity (inconstant variation) [60 ]. The same model was run on both groups. Regression analyses were only run with African-American participants, due to lack of statistical variation in race, and also theoretical significance. Educational attainment and physical functioning were retained as control variables in the final model, despite non-significance. Finally, post-hoc analyses were conducted to test for potential interactions between control variables and social support variables, and substance use, mental illness, and/or familial conflict. Analyses were conducted on complete cases only, due to acceptable missingness (< 10 percent) [61 ]. All analyses were conducted in STATA Version 11.2 SE [62 ].
Stata version 11.2 se
Manufactured by StataCorp
Sourced in United States
Stata version 11.2/SE is a statistical software package that provides a comprehensive set of tools for data analysis, data management, and visualization. It is designed to assist researchers, analysts, and professionals in various fields with their data-driven projects. The software offers a wide range of statistical methods and capabilities, allowing users to perform advanced analyses, modeling, and inference.
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Lab products found in correlation
3 protocols using stata version 11.2 se
1
Factors Influencing Viral Load in African-Americans
2
Multilevel Analysis of Anemia Determinants
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Descriptive statistics were presented as percentages where appropriate. Univariate analysis using simple logistic regression and a Chi-square test was used to evaluate associations between outcome (any level of anemia and moderate to severe anemia) and independent variables. In the 2016 NDHS, individuals are nested within households and households are nested within communities, which indicate that individuals and households are not independent of each other [19 (link)]. Data of this nature is usually analyzed using a multilevel random intercept model to account for variation in different levels. Therefore, multivariable analysis was performed using a multilevel binary logistic regression model with random intercept at household and community levels. Stata version 11.2/SE (Stata Corp, College Station, Texas, USA) was used for all statistical analysis.
3
Household Food Insecurity Determinants
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Descriptive statistics were presented as mean and percentages for HFI, individual, household and community level characteristics. Chi-square test and binary logistic regression were used to evaluate the association between outcome and independent variables (covariates). Logistic regression was investigated for multicollinearity. The regression coefficient standard error (SE) was <0.10 for the independent variables, namely HFI, and the results indicate an absence of multicollinearity. SE>2.0 indicates numerical problems (15) . Stata version 11.2/SE (Stata Corp, College Station, Texas, USA) was used for all statistical analysis. All analyses were statistically significant at 5% level.
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