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We also used bivariate analysis to examine associations with socioeconomic indicators (Table 3).
We collected demographic, clinical and biological data and performed bivariate analysis to examine risk factors associated with mortality.
We performed bivariate analysis to examine associations between child's disease severity status and potential predictors.
We used bivariate analysis to examine the relationship of the four primary exposure measures (ΣPCB4, dioxin-like PCBs, DDE, and MeHg) with the three primary outcome measures (Verbal Memory Index, Visual Memory Index, and Learning Index).
We performed bivariate analysis to examine associations between bypassing a nearer facility and potential predictors, and multivariate analyses to determine which predictors remained associated with bypass when adjusted for other factors.
We conducted bivariate analysis to examine whether there were differences in socio-demographics based on language (English vs. Spanish) and the results indicate statistically significant differences in race, ethnicity, marital status and education, and having ever gone online to search for pre-natal care information (p <0.05).
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In this study, we used bivariate spatial correlation analysis to examine the spatial relationship of SVI over time.
We conducted descriptive, bivariate, and multivariate analysis to examine the associations of self-immolation with demographic and familial risk factors, adverse life events, mental disorders, as well as potential protective factors.
We conducted bivariate logistic regression analysis to examine the relationship between condom use at last sexual intercourse and other variables.
Pearson correlations were conducted as an exploratory analysis to examine bivariate associations between the variables.
SAS (v. 9.1) was used to perform Wilcoxon rank sum analysis for bivariate analyses involving continuous variables and χ analysis to examine categorical variables.
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