For bivariate analyses, we used χ tests for categorical variables.
To determine statistical significance in the bivariate analyses, we used the chi-square test for categorical variables, the Student t-test for continuous variables that were normally distributed, and the Kruskal-Wallis (KW) test for continuous variables that were skewed.
Multicolinearity for all covariates significant after bivariate analyses was tested before using them in the multivariate logistic regression analyses.
Univariate and bivariate analyses were tested with the exact Fisher test instead of a standard Chi square because of the low numbers in some categories.
All variables used in the bivariate analyses were tested in the process of obtaining the most parsimonious set of DSH risk factors.
We first performed bivariate analyses to test for associations between diabetes status and baseline characteristics.
We firstly did bivariate analyses to test for associations between baseline characteristics and race by using χ tests for categorical variables and t tests for continuous variables.
First, bivariate analyses tested the associations between potential explanatory variables and each outcome of interest by using appropriate test statistic.
In two-tailed, χ bivariate analyses to test for significant association between potential covariates and the outcomes, we found the following covariates to be significantly associated with at least one of the outcomes (P < 0.10): age, race/ethnicity, employment, geographic region, residence location, education, household income, primary language, health insurance type, and health status.
(2) Bivariate analyses to test the relationship between each independent variable and each outcome, using either the Cochrane-Mantel-Haenszel test (to calculate the odds ratios, relative risks), T-test, or ANOVA, depending on the nature of the variables.
