Analysis

All (3) ((3 results))

• 1. An analysis of classification error for the revised Current Population Survey Employment questions Archived

  Articles and reports: 12-001-X20040027747

  Description:

  The reduced accuracy of the revised classification of unemployed persons in the Current Population Survey (CPS) was documented in Biemer and Bushery (2000). In this paper, we provide additional evidence of this anomaly and attempt to trace the source of the error through extended analysis of the CPS data before and after the redesign. The paper presents an novel approach decomposing the error in a complex classification process, such as the CPS labor force status classification, using Markov Latent Class Analysis (MLCA). To identify the cause of the apparent reduction in unemployed classification accuracy, we identify the key question components that determine the classifications and estimate the contribution of each of these question components to the total error in the classification process. This work provides guidance for further investigation into the root causes of the errors in the collection of labor force data in the CPS possibly through cognitive laboratory and/or field experiments.

  Release date: 2005-02-03

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• 2. On the validity of Markov latent class analysis for estimating classification error in labor force data Archived

  Articles and reports: 12-001-X20000025534

  Description:

  The primary goal of this research is to investigate the validity of Markov latent class analysis (MLCA) estimates of labor force classification error and to evaluate the efficacy of MLC analysis as an alternative to traditional methods for evaluating data quality.

  Release date: 2001-02-28

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• 3. Estimation of measurement bias using a model prediction approach Archived

  Articles and reports: 12-001-X199300214460

  Description:

  Methods for estimating response bias in surveys require “unbiased” remeasurements for at least a subsample of observations. The usual estimator of response bias is the difference between the mean of the original observations and the mean of the unbiased observations. In this article, we explore a number of alternative estimators of response bias derived from a model prediction approach. The assumed sampling design is a stratified two-phase design implementing simple random sampling in each phase. We assume that the characteristic, y, is observed for each unit selected in phase 1 while the true value of the characteristic, \mu, is obtained for each unit in the subsample selected at phase 2. We further assume that an auxiliary variable x is known for each unit in the phase 1 sample and that the population total of x is known. A number of models relating y, \mu and x are assumed which yield alternative estimators of E (y - \mu), the response bias. The estimators are evaluated using a bootstrap procedure for estimating variance, bias, and mean squared error. Our bootstrap procedure is an extension of the Bickel-Freedman single phase method to the case of a stratified two-phase design. As an illustration, the methodology is applied to data from the National Agricultural Statistics Service reinterview program. For these data, we show that the usual difference estimator is outperformed by the model-assisted estimator suggested by Särndal, Swensson and Wretman (1991), thus indicating that improvements over the traditional estimator are possible using the model prediction approach.

  Release date: 1993-12-15

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Articles and reports (3) ((3 results))

• 1. An analysis of classification error for the revised Current Population Survey Employment questions Archived

  Articles and reports: 12-001-X20040027747

  Description:

  The reduced accuracy of the revised classification of unemployed persons in the Current Population Survey (CPS) was documented in Biemer and Bushery (2000). In this paper, we provide additional evidence of this anomaly and attempt to trace the source of the error through extended analysis of the CPS data before and after the redesign. The paper presents an novel approach decomposing the error in a complex classification process, such as the CPS labor force status classification, using Markov Latent Class Analysis (MLCA). To identify the cause of the apparent reduction in unemployed classification accuracy, we identify the key question components that determine the classifications and estimate the contribution of each of these question components to the total error in the classification process. This work provides guidance for further investigation into the root causes of the errors in the collection of labor force data in the CPS possibly through cognitive laboratory and/or field experiments.

  Release date: 2005-02-03

  ---

• 2. On the validity of Markov latent class analysis for estimating classification error in labor force data Archived

  Articles and reports: 12-001-X20000025534

  Description:

  The primary goal of this research is to investigate the validity of Markov latent class analysis (MLCA) estimates of labor force classification error and to evaluate the efficacy of MLC analysis as an alternative to traditional methods for evaluating data quality.

  Release date: 2001-02-28

  ---

• 3. Estimation of measurement bias using a model prediction approach Archived

  Articles and reports: 12-001-X199300214460

  Description:

  Methods for estimating response bias in surveys require “unbiased” remeasurements for at least a subsample of observations. The usual estimator of response bias is the difference between the mean of the original observations and the mean of the unbiased observations. In this article, we explore a number of alternative estimators of response bias derived from a model prediction approach. The assumed sampling design is a stratified two-phase design implementing simple random sampling in each phase. We assume that the characteristic, y, is observed for each unit selected in phase 1 while the true value of the characteristic, \mu, is obtained for each unit in the subsample selected at phase 2. We further assume that an auxiliary variable x is known for each unit in the phase 1 sample and that the population total of x is known. A number of models relating y, \mu and x are assumed which yield alternative estimators of E (y - \mu), the response bias. The estimators are evaluated using a bootstrap procedure for estimating variance, bias, and mean squared error. Our bootstrap procedure is an extension of the Bickel-Freedman single phase method to the case of a stratified two-phase design. As an illustration, the methodology is applied to data from the National Agricultural Statistics Service reinterview program. For these data, we show that the usual difference estimator is outperformed by the model-assisted estimator suggested by Särndal, Swensson and Wretman (1991), thus indicating that improvements over the traditional estimator are possible using the model prediction approach.

  Release date: 1993-12-15

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