Response and nonresponse

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All (141) (20 to 30 of 141 results)

21. Variance estimation under monotone non-response for a panel survey Archived
Articles and reports: 12-001-X201800254952
Description:
Panel surveys are frequently used to measure the evolution of parameters over time. Panel samples may suffer from different types of unit non-response, which is currently handled by estimating the response probabilities and by reweighting respondents. In this work, we consider estimation and variance estimation under unit non-response for panel surveys. Extending the work by Kim and Kim (2007) for several times, we consider a propensity score adjusted estimator accounting for initial non-response and attrition, and propose a suitable variance estimator. It is then extended to cover most estimators encountered in surveys, including calibrated estimators, complex parameters and longitudinal estimators. The properties of the proposed variance estimator and of a simplified variance estimator are estimated through a simulation study. An illustration of the proposed methods on data from the ELFE survey is also presented.
Release date: 2018-12-20
22. How to decompose the non-response variance: A total survey error approach Archived
Articles and reports: 12-001-X201800254957
Description:
When a linear imputation method is used to correct non-response based on certain assumptions, total variance can be assigned to non-responding units. Linear imputation is not as limited as it seems, given that the most common methods – ratio, donor, mean and auxiliary value imputation – are all linear imputation methods. We will discuss the inference framework and the unit-level decomposition of variance due to non-response. Simulation results will also be presented. This decomposition can be used to prioritize non-response follow-up or manual corrections, or simply to guide data analysis.
Release date: 2018-12-20
23. Strategies for subsampling nonrespondents for economic programs Archived
Articles and reports: 12-001-X201800154929
Description:
The U.S. Census Bureau is investigating nonrespondent subsampling strategies for usage in the 2017 Economic Census. Design constraints include a mandated lower bound on the unit response rate, along with targeted industry-specific response rates. This paper presents research on allocation procedures for subsampling nonrespondents, conditional on the subsampling being systematic. We consider two approaches: (1) equal-probability sampling and (2) optimized allocation with constraints on unit response rates and sample size with the objective of selecting larger samples in industries that have initially lower response rates. We present a simulation study that examines the relative bias and mean squared error for the proposed allocations, assessing each procedure’s sensitivity to the size of the subsample, the response propensities, and the estimation procedure.
Release date: 2018-06-21
24. A mixed latent class Markov approach for estimating labour market mobility with multiple indicators and retrospective interrogation Archived
Articles and reports: 12-001-X201700114820
Description:
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
25. A few remarks on a small example by Jean-Claude Deville regarding non-ignorable non-response Archived
Articles and reports: 12-001-X201600214661
Description:
An example presented by Jean-Claude Deville in 2005 is subjected to three estimation methods: the method of moments, the maximum likelihood method, and generalized calibration. The three methods yield exactly the same results for the two non-response models. A discussion follows on how to choose the most appropriate model.
Release date: 2016-12-20
26. Tests for evaluating nonresponse bias in surveys Archived
Articles and reports: 12-001-X201600214677
Description:
How do we tell whether weighting adjustments reduce nonresponse bias? If a variable is measured for everyone in the selected sample, then the design weights can be used to calculate an approximately unbiased estimate of the population mean or total for that variable. A second estimate of the population mean or total can be calculated using the survey respondents only, with weights that have been adjusted for nonresponse. If the two estimates disagree, then there is evidence that the weight adjustments may not have removed the nonresponse bias for that variable. In this paper we develop the theoretical properties of linearization and jackknife variance estimators for evaluating the bias of an estimated population mean or total by comparing estimates calculated from overlapping subsets of the same data with different sets of weights, when poststratification or inverse propensity weighting is used for the nonresponse adjustments to the weights. We provide sufficient conditions on the population, sample, and response mechanism for the variance estimators to be consistent, and demonstrate their small-sample properties through a simulation study.
Release date: 2016-12-20
27. One step or two? Calibration weighting from a complete list frame with nonresponse Archived
Articles and reports: 12-001-X201500114172
Description:
When a random sample drawn from a complete list frame suffers from unit nonresponse, calibration weighting to population totals can be used to remove nonresponse bias under either an assumed response (selection) or an assumed prediction (outcome) model. Calibration weighting in this way can not only provide double protection against nonresponse bias, it can also decrease variance. By employing a simple trick one can estimate the variance under the assumed prediction model and the mean squared error under the combination of an assumed response model and the probability-sampling mechanism simultaneously. Unfortunately, there is a practical limitation on what response model can be assumed when design weights are calibrated to population totals in a single step. In particular, the choice for the response function cannot always be logistic. That limitation does not hinder calibration weighting when performed in two steps: from the respondent sample to the full sample to remove the response bias and then from the full sample to the population to decrease variance. There are potential efficiency advantages from using the two-step approach as well even when the calibration variables employed in each step is a subset of the calibration variables in the single step. Simultaneous mean-squared-error estimation using linearization is possible, but more complicated than when calibrating in a single step.
Release date: 2015-06-29
28. Dealing with non-ignorable nonresponse in survey sampling: A latent modeling approach Archived
Articles and reports: 12-001-X201500114173
Description:
Nonresponse is present in almost all surveys and can severely bias estimates. It is usually distinguished between unit and item nonresponse. By noting that for a particular survey variable, we just have observed and unobserved values, in this work we exploit the connection between unit and item nonresponse. In particular, we assume that the factors that drive unit response are the same as those that drive item response on selected variables of interest. Response probabilities are then estimated using a latent covariate that measures the will to respond to the survey and that can explain a part of the unknown behavior of a unit to participate in the survey. This latent covariate is estimated using latent trait models. This approach is particularly relevant for sensitive items and, therefore, can handle non-ignorable nonresponse. Auxiliary information known for both respondents and nonrespondents can be included either in the latent variable model or in the response probability estimation process. The approach can also be used when auxiliary information is not available, and we focus here on this case. We propose an estimator using a reweighting system based on the previous latent covariate when no other observed auxiliary information is available. Results on its performance are encouraging from simulation studies on both real and simulated data.
Release date: 2015-06-29
29. Measurement Error for Welfare Receipt and Its Impact on Fixed-Effects Models Archived
Articles and reports: 11-522-X201300014262
Description:
Measurement error is one source of bias in statistical analysis. However, its possible implications are mostly ignored One class of models that can be especially affected by measurement error are fixed-effects models. By validating the survey response of five panel survey waves for welfare receipt with register data, the size and form of longitudinal measurement error can be determined. It is shown, that the measurement error for welfare receipt is serially correlated and non-differential. However, when estimating the coefficients of longitudinal fixed effect models of welfare receipt on subjective health for men and women, the coefficients are biased only for the male subpopulation.
Release date: 2014-10-31
30. Modeling Self-enumeration and Follow-up Response Indicators as Discrete-time Survival Archived
Articles and reports: 11-522-X201300014263
Description:
Collecting information from sampled units over the Internet or by mail is much more cost-efficient than conducting interviews. These methods make self-enumeration an attractive data-collection method for surveys and censuses. Despite the benefits associated with self-enumeration data collection, in particular Internet-based data collection, self-enumeration can produce low response rates compared with interviews. To increase response rates, nonrespondents are subject to a mixed mode of follow-up treatments, which influence the resulting probability of response, to encourage them to participate. Factors and interactions are commonly used in regression analyses, and have important implications for the interpretation of statistical models. Because response occurrence is intrinsically conditional, we first record response occurrence in discrete intervals, and we characterize the probability of response by a discrete time hazard. This approach facilitates examining when a response is most likely to occur and how the probability of responding varies over time. The nonresponse bias can be avoided by multiplying the sampling weight of respondents by the inverse of an estimate of the response probability. Estimators for model parameters as well as for finite population parameters are given. Simulation results on the performance of the proposed estimators are also presented.
Release date: 2014-10-31

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Analysis (140)

Analysis (140) (70 to 80 of 140 results)

71. The impact of unconditional incentives upon a multi-stage survey: the Health Survey for England Archived
Articles and reports: 11-522-X200600110451
Description:
Household response rates have steadily declined across many large scale social surveys. The Health Survey for England has observed a 9 percentage points decline in response across an eleven year period. Evidence from other studies has suggested that unconditional gifts or incentives, with small monetary value, can improve rates of co-operation. An incentive experiment conducted on the Health Survey for England aimed to replicate findings of a previous experiment carried out on the Family Resources Study, which showed significant increases in household response among those who had received a book of first class stamps with the advance letter. The HSE incentive experiment, however, did not show any significant differences in household response rates, response to other stages of the survey or in respondent profile between two experimental conditions (stamps included with the advance letter, bookmark sent with the advance letter) and the control group (the advance letter alone).
Release date: 2008-03-17
72. Weighting for two-phase surveyed data Archived
Articles and reports: 12-001-X200700210489
Description:
Missingness may occur in various forms. In this paper, we consider unit non-response, and hence make attempts for adjustments by appropriate weighting. Our empirical case concerns two-phase sampling so that first, a large sample survey was conducted using a fairly general questionnaire. At the end of this contact the interviewer asked whether the respondent was willing to participate in the second phase survey with a more detailed questionnaire concentrating on some themes of the first survey. This procedure leads to three missingness mechanisms. Our problem is how to weight the second survey respondents as correctly as possible so that the results from this survey are consistent with those obtained with the first phase survey. The paper first analyses missingness differences in these three steps using a human survey dataset, and then compares different weighting approaches. Our recommendation is that all available auxiliary data should have been used in the best way. This works well with a mixture of the two classic methods that first exploits response propensity weighting and then calibrates these weights to the known population distributions.
Release date: 2008-01-03
73. Handling survey nonresponse in cluster sampling Archived
Articles and reports: 12-001-X20070019855
Description:
In surveys under cluster sampling, nonresponse on a variable is often dependent on a cluster level random effect and, hence, is nonignorable. Estimators of the population mean obtained by mean imputation or reweighting under the ignorable nonresponse assumption are then biased. We propose an unbiased estimator of the population mean by imputing or reweighting within each sampled cluster or a group of sampled clusters sharing some common feature. Some simulation results are presented to study the performance of the proposed estimator.
Release date: 2007-06-28
74. Evaluating high effort measures for studying nonresponse bias Archived
Articles and reports: 11-522-X20050019446
Description:
One method used to examine the effect of nonresponse involves comparing survey participants who require less effort on the part of the interviewer with those who require more effort. A persistent problem for researchers involves the criteria to use in determining membership in high effort groups.
Release date: 2007-03-02
75. Model estimation for selectivity of attrition, employment and wages using the Survey of Labour and Income Dynamics Archived
Articles and reports: 11-522-X20050019447
Description:
To understand the selection biases in model estimation when using longitudinal survey panel microdata, we consider a structural model composed of three equations for non-attrition/response, employment and wages. The three equations are freely correlated.
Release date: 2007-03-02
76. Quarterly Services Survey analysis of response Archived
Articles and reports: 11-522-X20050019464
Description:
The Quarterly Services Survey has maintained comprehensive response data since the survey's inception. In analyzing the data, we concentrate on three fundamental features of response: rate, timeliness, and quality. We examine these three components across multiple dimensions. We observe the effect associated with NAICS classification, company size and response mode.
Release date: 2007-03-02
77. Incorporating time-in-sample in longitudinal survey models Archived
Articles and reports: 11-522-X20050019468
Description:
At the time of recruitment, the participants in a longitudinal survey are chosen to be representative of a population. As time goes on, typically some of the participants will drop out, and dropout may be informative in the sense of depending on the response variables of interest. However, even if dropout is minimal, the participants who continue to the second and third waves of a longitudinal survey may differ from those they supposedly represent in subtle ways. It is clearly important to take such possibilities into account when designing and analyzing longitudinal survey data before and after an intervention.
Release date: 2007-03-02
78. Using calibration weighting to adjust for nonresponse and coverage errors Archived
Articles and reports: 12-001-X20060029547
Description:
Calibration weighting can be used to adjust for unit nonresponse and/or coverage errors under appropriate quasi-randomization models. Alternative calibration adjustments that are asymptotically identical in a purely sampling context can diverge when used in this manner. Introducing instrumental variables into calibration weighting makes it possible for nonresponse (say) to be a function of a set of characteristics other than those in the calibration vector. When the calibration adjustment has a nonlinear form, a variant of the jackknife can remove the need for iteration in variance estimation.
Release date: 2006-12-21
79. A nonresponse model approach to inference under imputation for missing survey data Archived
Articles and reports: 12-001-X20060019257
Description:
In the presence of item nonreponse, two approaches have been traditionally used to make inference on parameters of interest. The first approach assumes uniform response within imputation cells whereas the second approach assumes ignorable response but make use of a model on the variable of interest as the basis for inference. In this paper, we propose a third appoach that assumes a specified ignorable response mechanism without having to specify a model on the variable of interest. In this case, we show how to obtain imputed values which lead to estimators of a total that are approximately unbiased under the proposed approach as well as the second approach. Variance estimators of the imputed estimators that are approximately unbiased are also obtained using an approach of Fay (1991) in which the order of sampling and response is reversed. Finally, simulation studies are conducted to investigate the finite sample performance of the methods in terms of bias and mean square error.
Release date: 2006-07-20
80. A model for estimating and imputing nonrespondent census households under sampling for nonresponse follow-up Archived
Articles and reports: 12-001-X20060019264
Description:
Sampling for nonresponse follow-up (NRFU) was an innovation for U.S. Decennial Census methodology considered for the year 2000. Sampling for NRFU involves sending field enumerators to only a sample of the housing units that did not respond to the initial mailed questionnaire, thereby reducing costs but creating a major small-area estimation problem. We propose a model to impute the characteristics of the housing units that did not respond to the mailed questionnaire, to benefit from the large cost savings of NRFU sampling while still attaining acceptable levels of accuracy for small areas. Our strategy is to model household characteristics using low-dimensional covariates at detailed levels of geography and more detailed covariates at larger levels of geography. To do this, households are first classified into a small number of types. A hierarchical loglinear model then estimates the distribution of household types among the nonsample nonrespondent households in each block. This distribution depends on the characteristics of mailback respondents in the same block and sampled nonrespondents in nearby blocks. Nonsample nonrespondent households can then be imputed according to this estimated household type distribution. We evaluate the performance of our loglinear model through simulation. Results show that, when compared to estimates from alternative models, our loglinear model produces estimates with much smaller MSE in many cases and estimates with approximately the same size MSE in most other cases. Although sampling for NRFU was not used in the 2000 census, our estimation and imputation strategy can be used in any census or survey using sampling for NRFU where units are clustered such that the characteristics of nonrespondents are related to the characteristics of respondents in the same area and also related to the characteristics of sampled nonrespondents in nearby areas.
Release date: 2006-07-20

Reference (1)

Reference (1) ((1 result))

1. Labour Force Survey Response Rates, September 2023
Surveys and statistical programs – Documentation: 75-005-M2023001
Description: This document provides information on the evolution of response rates for the Labour Force Survey (LFS) and a discussion of the evaluation of two aspects of data quality that ensure the LFS estimates continue providing an accurate portrait of the Canadian labour market.
Release date: 2023-10-30

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Date modified:: 2024-11-04