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The covariates that were nonsignificant at the bivariate level were removed first, followed by the significant variables.
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Genes below this level were removed.
All variables with a p-value <= 0.05 at the bivariate level were entered into the model and removed one by one.
All variables significant at the bivariate level were considered for a logistic regression model.
Predictors that were significant at the bivariate level were then entered into MPlus to test multivariate models.
Variables that were significant (p < 0.01) at the bivariate level were considered for inclusion in the multivariable analyses.
Factors that proved to be significant at the bivariate level were considered for a multivariate logistic regression analysis.
When controlling for covariates, factors that were significant at the bivariate level were adjusted for in the model.
Factors that proved to be significant at the bivariate level were considered for a multivariable logistic regression analysis.
Logistic regression for S-ECC including variables associated at the bivariate level was also performed.
Variables associated with CSE behaviour at the bivariate level are presented in Table 3.
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