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We used bivariate analysis to assess the unadjusted relative risk of poor hemodynamic control associated with several covariates.
We used bivariate analysis to assess the strength of the association between the selected demographic and reproductive characteristics and the women's self-reports of postpartum complaints.
Using simple binary logistic regression (LR) models to yield odds ratio (ORs) with a 95% confidence interval (CI), we conducted a bivariate analysis to assess the potential association between the burnout subtypes and socio-demographic and occupational variables of interest.
For the remaining 8 clinical signs, we performed a bivariate analysis to assess the association of each variable with the true dehydration status of the child based on our criterion standard.
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We used Spearman's rank correlation analysis to assess bivariate associations for identification of predictors of As metabolism and of potential confounding factors for further exploration in multivariate linear regression and analyses of covariance (ANCOVA).
We used bivariate (chi square, Wilcoxon rank-sum) analysis to assess differences in responding and non-responding physicians' number of years in practice and census region.
All variables with significant p-values (p < 0.05) in the bivariate analysis were included in the multivariate analysis to assess how independently each variable predicted the need for extra care.
In addition, bivariate analysis will be peformed with comparison of proportions using the chi-square test to evaluate secondary variables of the study and variance analysis to assess the relationship between quantitative variables for both independent and paired data.
To assess construct validity of the PRISM framework, we conducted bivariate analysis to investigate the hypothesized associations.
After univariate analysis, bivariate analysis was conducted to assess the association between the experience of having sex with FSWs ('never had sex', 'had sex but not in the last year' and 'had sex in the last year') with predictor variables.
The bivariate analysis was performed to assess the association between relevant variables, and it preceded the multivariate logistic regression analysis.
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