摘要:

The 1990 Post-Enumeration Survey (PES) stratified the population into 1,392 subpopulations called post-strata based on location, race, tenure, sex and age, in the hope that these subpopulations were homogeneous in relation to factors affecting the Census coverage. Homogeneity is necessary to justify the use of the same adjustment factor for many, sometimes quite small, subgroups of the post-strata. With block-level data from the PES for sites around Detroit and Texas, we are able to examine empirically the extent to which this hope was realized. Using various measures, we find that between-block variation in erroneous enumeration and gross omission rates is about the same magnitude as, and largely in addition to, the corresponding between-post-stratum variation.

ISSN:0162-1459    

DOI:10.1080/01621459.1993.10476382

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1993.10476383

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1993.10476384

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1993.10476385

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

摘要:

We show how conditional logistic regression can be used to estimate the probability of being enumerated in a census and apply the model to the 1990 Post-Enumeration Survey (PES) in the United States. The estimates can be used in the estimation of population size and the estimation of correlation bias, for example. Unlike the classical stratification approach, the logistic approach permits the use of continuous explanatory variables. Model choice can be based on the standard techniques of the generalized linear models. We discuss some special problems caused by the fact that the PES sample area is open to migration between the captures. We also consider the effect of data errors in estimation. We characterize hard-to-enumerate populations and give some tentative estimates of correlation bias.

ISSN:0162-1459    

DOI:10.1080/01621459.1993.10476386

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

摘要:

A central assumption in the standard capture-recapture approach to the estimation of the size of a closed population is the homogeneity of the “capture” probabilities. In this article we develop an approach that allows for varying susceptibility to capture through individual parameters using a variant of the Rasch model from psychological measurement situations. Our approach requires an additional recapture. In the context of census undercount estimation, this requirement amounts to the use of a second independent sample or alternative data source to be matched with census and Post-Enumeration Survey (PES) data. The models we develop provide a mechanism for separating out the dependence between census and PES induced by individual heterogeneity. The resulting data take the form of an incomplete 23contingency table, and we describe how to estimate the expected values of the observable cells of this table using log-linear quasi-symmetry models. The projection of these estimates onto the unobserved cell corresponding to those individuals missed by all three sources involves the log-linear model of no second-order interaction, which is quite plausible under the Rasch model. We illustrate the models and their estimation using data from a 1988 dress-rehearsal study for the 1990 census conducted by the U.S. Bureau of the Census, which explored the use of administrative data as a supplement to the PES. The article includes a discussion of extensions and related models.

ISSN:0162-1459    

DOI:10.1080/01621459.1993.10476387

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

摘要:

In the process of collecting Post-Enumeration Survey (PES) data to evaluate census coverage, it is inevitable that there will be some individuals whose enumeration status (outcome in the census-PES match) remains unresolved even after extensive field follow-up operations. Earlier work developed a logistic regression framework for imputing the probability that unresolved individuals were enumerated in the census, so that the probability of having been enumerated is allowed to depend on covariates. The covariates may include demographic characteristics, geographic information, and census codes that summarize information on the characteristics of the match (e.g., the before-follow-up match code assigned by clerks to describe the type of match between PES and census records). In the production of 1990 undercount estimates, the basic logistic regression model was expanded into a mixed hierarchical model to allow for the presence of group-specific effects, where groups are characterized by common before-follow-up match code. Parameter estimates for individual match-code groups thus “borrow strength” across groups by making use of observed relationships between group-specific parameter estimates in the various groups and the characteristics of the groups. This allows predictions to be made for groups for which there are few or no resolved cases to which to fit the model. The model was fitted by an approximate expectation-conditional-maximization (ECM) algorithm, using a large-sample approximation to the posterior distributions of group parameters. Uncertainty in estimation of model parameters was evaluated using a resampling procedure and became part of the evaluation of total error in PES estimates of population. Results from fitting the model in the 1990 Census and PES are described.

ISSN:0162-1459    

DOI:10.1080/01621459.1993.10476388

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1993.10476389

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1993.10476390

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor

ISSN:0162-1459    

DOI:10.1080/01621459.1993.10476391

出版商:Taylor & Francis Group    

年代:1993

数据来源: Taylor