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The results of our model estimation are presented in Table 10.2. Before focusing on specific coefficients we make four general observations. First, a Wald test suggests that the assumption of conditional independence should be rejected ( = 0.356; chi-squared = 7.47; P = 0.006). The estimated coefficients, however, are little different when the conditional independence assumption is relaxed. Second, unobserved interviewer effects, while statistically significant, are relatively small in magnitude. In both the contact and response models the estimated proportion of the total variance attributable to an unobserved random interviewer effect is less than 7 %.12 The comparable percentages reported by Nicoletti and Buck (2004) varied from 13 % to 51 % in the contact models and from 13 % to 31 % in the response models. The implication again is that focusing on Model I results will not be misleading. Third, most of the covariates (but not all) have coefficients that are either in line with expectations or are, ex post, intuitively sensible (and, given the large sample size, most are highly significant). They are also extremely robust to model specification. Fourth, despite the array of significant coefficients, the overall explanatory power of these models is relatively poor. This is a desired outcome and presumably reflects the large random component in survey nonresponse. What then do our estimates reveal Focusing first on the probability of making contact, we observe the following: (1) The optimal interviewer workload for maximizing contact rates is 124 previous wave respondents. With larger or smaller workloads, the likelihood of contact is reduced. Interviewer continuity does not appear to be of any great benefit to contact probabilities. Only by wave 4 is there any evidence that interviewer continuity is code 128 in excel 2010 Code128 Barcode Fonts - Aeromium Barcode Fonts
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