Response and nonresponse
Results
All (2)
All (2) ((2 results))
- Articles and reports: 12-001-X201700114820Description:
Measurement errors can induce bias in the estimation of transitions, leading to erroneous conclusions about labour market dynamics. Traditional literature on gross flows estimation is based on the assumption that measurement errors are uncorrelated over time. This assumption is not realistic in many contexts, because of survey design and data collection strategies. In this work, we use a model-based approach to correct observed gross flows from classification errors with latent class Markov models. We refer to data collected with the Italian Continuous Labour Force Survey, which is cross-sectional, quarterly, with a 2-2-2 rotating design. The questionnaire allows us to use multiple indicators of labour force conditions for each quarter: two collected in the first interview, and a third collected one year later. Our approach provides a method to estimate labour market mobility, taking into account correlated errors and the rotating design of the survey. The best-fitting model is a mixed latent class Markov model with covariates affecting latent transitions and correlated errors among indicators; the mixture components are of mover-stayer type. The better fit of the mixture specification is due to more accurately estimated latent transitions.
Release date: 2017-06-22 - Articles and reports: 12-001-X20050029046Description:
Nonresponse weighting is a common method for handling unit nonresponse in surveys. The method is aimed at reducing nonresponse bias, and it is often accompanied by an increase in variance. Hence, the efficacy of weighting adjustments is often seen as a bias-variance trade-off. This view is an oversimplification, nonresponse weighting can in fact lead to a reduction in variance as well as bias. A covariate for a weighting adjustment must have two characteristics to reduce nonresponse bias: it needs to be related to the probability of response, and it needs to be related to the survey outcome. If the latter is true, then weighting can reduce, not increase, sampling variance. A detailed analysis of bias and variance is provided in the setting of weighting for an estimate of a survey mean based on adjustment cells. The analysis suggests that the most important feature of variables for inclusion in weighting adjustments is that they are predictive of survey outcomes; prediction of the propensity to respond is a secondary, though useful, goal. Empirical estimates of root mean squared error for assessing when weighting is effective are proposed and evaluated in a simulation study. A simple composite estimator based on the empirical root mean squared error yields some gains over the weighted estimator in the simulations.
Release date: 2006-02-17
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Analysis (2)
Analysis (2) ((2 results))
- Articles and reports: 12-001-X201700114820Description:
Measurement errors can induce bias in the estimation of transitions, leading to erroneous conclusions about labour market dynamics. Traditional literature on gross flows estimation is based on the assumption that measurement errors are uncorrelated over time. This assumption is not realistic in many contexts, because of survey design and data collection strategies. In this work, we use a model-based approach to correct observed gross flows from classification errors with latent class Markov models. We refer to data collected with the Italian Continuous Labour Force Survey, which is cross-sectional, quarterly, with a 2-2-2 rotating design. The questionnaire allows us to use multiple indicators of labour force conditions for each quarter: two collected in the first interview, and a third collected one year later. Our approach provides a method to estimate labour market mobility, taking into account correlated errors and the rotating design of the survey. The best-fitting model is a mixed latent class Markov model with covariates affecting latent transitions and correlated errors among indicators; the mixture components are of mover-stayer type. The better fit of the mixture specification is due to more accurately estimated latent transitions.
Release date: 2017-06-22 - Articles and reports: 12-001-X20050029046Description:
Nonresponse weighting is a common method for handling unit nonresponse in surveys. The method is aimed at reducing nonresponse bias, and it is often accompanied by an increase in variance. Hence, the efficacy of weighting adjustments is often seen as a bias-variance trade-off. This view is an oversimplification, nonresponse weighting can in fact lead to a reduction in variance as well as bias. A covariate for a weighting adjustment must have two characteristics to reduce nonresponse bias: it needs to be related to the probability of response, and it needs to be related to the survey outcome. If the latter is true, then weighting can reduce, not increase, sampling variance. A detailed analysis of bias and variance is provided in the setting of weighting for an estimate of a survey mean based on adjustment cells. The analysis suggests that the most important feature of variables for inclusion in weighting adjustments is that they are predictive of survey outcomes; prediction of the propensity to respond is a secondary, though useful, goal. Empirical estimates of root mean squared error for assessing when weighting is effective are proposed and evaluated in a simulation study. A simple composite estimator based on the empirical root mean squared error yields some gains over the weighted estimator in the simulations.
Release date: 2006-02-17
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