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Second, we considered bivariate statistics to identify which of the many measures would be most associated with persons who were transitioned.
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Descriptive statistics included bivariate statistics to assess the distribution of characteristics in the two cohorts.
We conducted bivariate analyses to identify any demographic or practice characteristics associated with comfort levels.
We used bivariate analyses to identify variables potentially predicting the presentation of MI.
Model-building involved consideration of variables, using a series of bivariate analyses to identify potential confounders.
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.
The bivariate analyses undertaken to identify confounding variables are presented in Table 2.
Bivariate analyses were conducted to identify significant associations among variables.
Descriptive statistics were used to identify frequencies.
Bivariate analysis was performed to identify associated factors to self assess the health status.
Bivariate analysis was used to identify the factors associated with labor force participation.
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