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The first is to use a hierarchical probit for consent with interviewers at the group level.
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It uses a hierarchical ordered probit (HOPIT) modeling approach to combine information from anchoring vignettes to identify and correct self-rating responses for reporting heterogeneity (17).
Given the importance of vignette-adjustment to our results, we use a Compound Hierarchical Probit (CHOPIT) specification to look at the heterogeneous effects of individual vignette choice on the self-evaluation risk measure.
We used a hierarchical modeling approach.
We used a hierarchical diagnostic scheme.
We use a Heckman probit model to generate predicted values of HIV prevalence, and compare these with estimates from standard probit models.
We decided to use a probit instead of a logit model solely for convenience reasons, since probit models are easier to interpret.
We use an ordered probit model for investigating changes in the probability of finding negative, positive, and insignificant impacts.
In addition, we check that our results hold when using a probit or IV-probit model.
We check that results from Tables 6, 7, and 8 hold when using a probit or IV-probit model; these results are reported in the Appendix.
Results in the appendix confirm that results reported in Tables 3, 4, and 5 hold when using a probit or IV-probit model.
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