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The p-values based on the deviance statistic are respectively 0.08,0.00,0.00.
The p-value based on the deviance gave the values 0.40,0.22,0.01 when n=1,5,10 respectively.
The p-values based on the deviance statistic are respectively 0.08,0.00,0.00 so, as might be expected, the test does not do as well when n=1.
The p-value based on the deviance gave the values 1.0, 0.22, 0.22 when n=1,5,10 respectively, and so this test would also not reject the logistic regression model in these simulations.
Statistical comparisons among models were based on the deviance [ 9].
Appropriate models reported below were selected based on the deviance goodness-of-fit test.
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Model selection can be done based on the deviance-based information criterion (and several other criteria), which can even be used to compare hierarchical models.
A chi-square test based on the residual deviance and degrees of freedom indicated overdispersion, and the final model was subsequently fit as a negative binomial process (Hinkelman 2012).
A mixed-methods study, based on the positive deviance approach[ 14], was conducted in 2011/2012.
A mixed-methods study, based on the positive deviance approach, was conducted in Ethiopia in 2011/12.
For each outcome, fixed and random effects models were evaluated, and the better fitting model was selected based on the lower Deviance Information Criterion value [ 31].
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