Article

An iterative estimating procedure for probit-type nonresponse models in surveys with call backs

Test (Impact Factor: 1.02). 02/2000; 9(1):233-253. DOI: 10.1007/BF02595860
Source: RePEc

ABSTRACT This work attempts to treat the negatives to respond in sample plans when several tries or call backs in the capture of individual
data are assumed. We also maintain the assumption that the respondents supply all the variables of interest when they are
captured although the retries are kept on, even after previous captures, for a predetermined number of tries,r, fixed only for estimating purposes. Supposing that the different retries or call backs are exerted with different capture
intensities, the response probabilities, which may vary from one individual to another, are searched by probit models whose
parameters are estimated using conditional likelihoods evaluated on the respondents only (other models, derived from error
distributions different from normal, could also be possible by approximating numerical techniques quite similar to the ones
proposed here). We present a numerical estimating algorithm, quite easy to implement, which may be used when the recorded
information about data captures includes at least marginal results. Finally, we include some encouraging empirical simulations
whose purpose is centred in testing and evaluating the practical performance of the procedure.

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    • "Concerns about endogeneity of the number of contact attempts with the outcome of survey participation and " censoring " for cases that are never contacted or interviewed have led to the use of discrete time hazard models that change the outcome to the conditional probability of an interview on a given call, given no contact or participation on prior calls (Durrant and Steele 2009; Groves and Heeringa 2006; Kennickell 1999; Olson and Groves 2009). Other probability-based models have been used to estimate response probabilities at each call attempt as a function of respondent characteristics, sometimes permitting a " hard core " nonresponding group (Alho 1990; Anido and Valdés 2000; Colombo 1992; Drew and Fuller 1980; Potthoff, Manton, and Woodbury 1993; Wood, White, and Hotopf 2006). A recent expansion of the callback models uses latent class models, characteristics of respondents and nonrespondents, and reports from the survey to create weights based on the level of effort exerted to the case (Biemer 2009; Biemer and Wang 2007; Biemer and Link 2006). "
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    • "Kuk et al.(2001) suggested imputation and prediction approaches in estimating finite population quantities. Approaches have also been suggested by Potthoff et al. (1993), Anido and Valdes (2000) and Copas and Farewell (1998). In this paper we show how to estimate and adjust for non-ignorable non-response under two assumptions. "
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    Journal of the Royal Statistical Society Series A (Statistics in Society) 07/2006; 169(3):525-542. DOI:10.1111/j.1467-985X.2006.00405.x · 1.57 Impact Factor