Bivariate analysis will be used to examine relationships between each potential predictor and the outcome(s) of interest.
If the purpose is genuinely to study prevalences in population subgroups, then simple uni or bivariate analysis will suffice.
A bivariate analysis will be performed to evaluate possible individual risk factors for preterm birth, calculating the odds ratios with their respective CI of 95% [ 23, 24].
A bivariate analysis will be performed to establish if there are any differences in the diagnostic error variables based on the heuristics identified and the independent variables.
The bivariate analysis will be contrasted using the Student's t-test or Chi-square test or a non-parametric test if a normal distribution cannot be assumed.
All variables with P < 0.2 from bivariate analysis were selected for multivariate logistic regression.
All independent variables that had a p value below 0.20 in the bivariate analysis were selected for multivariate analysis.
Independent variables with p-values less than 0.3 in the bivariate analysis were selected for multivariate linear regression.
Table 2 shows the results of the bivariate analysis of selected variables and the results of T. gondii seropositivity.
The independent variables with a p-value of less than 0.2 in bivariate analysis were selected for adjusted analysis in each level of the hierarchical model.
