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We used analysis of variance for bivariate analysis to explore associations and potential confounders.
Table 1 presents the results of bivariate analysis to explore the differentials in ever having zero, one, or two and more induced abortions and women's experience in ever receiving VFP (any one, any two or all three dimensions), and other maternal and socio-economic characteristics.
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We carried out recommended analyses to tackle this problem, including bivariate and subgroup meta-analysis to take into account the threshold effect, 44 metaregression analysis to explore reasons for heterogeneity, 39 and component outcome analysis to examine the suitability of the composite morbidity outcome.
We also used bivariate analysis to examine associations with socioeconomic indicators (Table 3).
We used bivariate analysis to estimate odds ratios (ORs) with 95% confidence intervals (CIs).
After bivariate analysis to test the hypotheses using Pearson correlation, we performed multiple regression analysis.
We used univariate and bivariate analyses to describe the magnitude and pattern of abuse in the sample and bivariate analysis to identify risk factors for abuse.
We used bivariate analysis to compare adults hospitalized with influenza with and without pneumonia.
We performed bivariate analysis to examine associations between child's disease severity status and potential predictors.
We first performed bivariate analysis to determine parental and pregnancy characteristics associated with birth outcomes.
Univariate and bivariate analysis are performed to explore similarities and differences on selected items.
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