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Logit random effects models were used to test for differences in proportions (allowing for clustering within villages).
Logit random effects models were used to test for differences in proportions including a random effect for village.
Participants who had not heard of HIV/AIDS (n = 24) were included under "don't know/not sure" bParticipants who had not heard of HIV/AIDS (n = 24) were not asked about HIV prevention practice cTest for difference compares yes vs. no/don't know/not sure by logit random effects model 574 (76 %) men and 1156 (84%%) women reported ever testing for HIV (P < 0.001 for difference).
The estimates using random- and fixed-effects logit and random-effects probit models were not substantially different from the results of the conditional logit model, and provided poorer fits than\the conditional logit model (Table 6).
Our aim is to extend the standard binary logit model to random-effects model to permit spatial clustering and heterogeneity.
Common modelling techniques used include random-effects logit or probit model and conditional logit models [ 53, 54], while there is an increasing use of more sophisticated tools such as mixed logit models [ 59].
Finally, to ensure that the most appropriate model was used, we repeated the statistical analysis for the main effects model using a random-effects logit model (Model 6), a fixed-effects logit model (Model 7), and a random-effects probit model (Model 8).
Log likelihood ratio test of the base case model was compared to the sum of the log likelihood ratio test for the unrestricted model (i.e. random-effects logit regression of sample EQ5D < 0.69 and random-effects logit regression of sample EQ5D ≥ 0.69).
Besides the employed SURE, OLS and logit regression models, a random-effects or fixed-effects panel data model would also be appropriate.
Analysis entails the estimation of random-effects logit models.
The random-effects logit regression only included attributes as covariates.
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