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) (130 to 140 of 140 results)

131. The maximum likelihood method for non-response in sample surveys Archived
Articles and reports: 12-001-X198600114441
Description:
The analysis of survey data becomes difficult in the presence of incomplete responses. By the use of the maximum likelihood method, estimators for the parameters of interest and test statistics can be generated. In this paper the maximum likelihood estimators are given for the case where the data is considered missing at random. A method for imputing the missing values is considered along with the problem of estimating the change points in the mean. Possible extensions of the results to structured covariances and to non-randomly incomplete data are also proposed.
Release date: 1986-06-16
132. Some aspects of nonresponse adjustments Archived
Articles and reports: 12-001-X198500114359
Description:
Unit and item nonresponse almost always occur in surveys and censuses. The larger its size the larger its potential effect will be on survey estimates. It is, therefore, important to cope with it at every stage where they can be affected. At varying degrees the size of nonresponse can be coped with at design, field and processing stages. The nonresponse problems have an impact on estimation formulas for various statistics as a result of imputations and weight adjustments along with survey weights in the estimates of means, totals, or other statistics. The formulas may be decomposed into components that include response errors, the effect of weight adjustment for unit nonresponse, and the effect of substitution for nonresponse. The impacts of the design, field, and processing stages on the components of the estimates are examined.
Release date: 1985-06-14
133. Characteristics of respondent and non-respondent households in the Canadian Labour Force Survey Archived
Articles and reports: 12-001-X198200114329
Description:
This article presents findings from a study to characterize responding and non-responding households in the Labour Force Survey (LFS). This study was motivated by two projects associated with the LFS Redesign, namely, the family estimation project and evaluation of non-response compensation procedures. However, the results of the study are of general interest in the assessment of the quality of data emanating from the LFS.
Release date: 1982-06-15
134. Rotation group bias in the Labour Force Survey (LFS) estimates Archived
Articles and reports: 12-001-X198200114330
Description:
The paper attempts to evaluate the impact of non-response adjustment by rotation groups on rotation group bias in the estimates from the Canadian Labour Force Survey. Results on bias and non-response characteristics are presented and discussed. An index used to measure rotation group bias is given and some empirical results are analyzed.
Release date: 1982-06-15
135. The nonresponse problem Archived
Articles and reports: 12-001-X198100214320
Description:
This paper presents an outline of the nonresponse research which is carried out at the Netherlands Central Bureau of Statistics. The phenomenon of nonresponse is put into a general frame-work. The extent of nonresponse is indicated with figures from a number of CBS-surveys. The use of auxiliary variables is discussed as a means for obtaining information about nonrespondents. These variables can be used either to characterize nonrespondents or as stratification variables in adjustment procedures.
Adjustment for nonresponse bias by means of subgroup weighting is considered in more detail. Finally, the last section lists a number of other methods which also aim at reduction of the bias.
Release date: 1981-12-15
136. The measurement and magnitude of nonresponse in U.S. consumer telephone surveys Archived
Articles and reports: 12-001-X198000254944
Description:
Due to the absence of hard data and the lack of standardization with respect to nonresponse terminology and reporting procedures, U.S. commercial survey researchers have been unable to obtain an accurate assessment of the nature and extent of the nonresponse problem. However, the results of two recent studies conducted by the author among leading U.S. based market and public opinion research firms revealed that nonresponse is one of the major problems now confronting the commercial survey research industry. This paper discusses the results of the two studies and their implications.
Release date: 1980-12-15
137. Causes of incomplete data, adjustments and effects Archived
Articles and reports: 12-001-X198000254945
Description:
The article provides a general overview of the concepts of incomplete data and non-response. It is recognized that non-response is an important indicator of data quality, as it affects the estimators by introducing bias and increasing variance due to a reduction in the effective sample size. The relationship between bias and the non-response rate is less obvious, since it depends on the extent of non-response and on the difference in the various characteristics between respondents and non-respondents.
The most effective way of dealing with the effects of non-response is to minimize its extent. However, any attempt to control the extent of non-response must be based on a good understanding of its origins. The causes and extent of non-response are fundamentally related to (i) the type of survey, (ii) the data capture methods, and (iii) the sample design. However, given a sample design, the extent of non-response will be influenced by factors such as the type of region and the type of non-response.
There are several ways to handle incomplete data. Each one, in the end, assigns a value to the missing or incorrect data, unless it is decided to publish “raw” data. The procedure for assigning values is called imputation and such an imputed value presumably describes the characteristic of the non-respondent.
The article provides a brief philosophical explanation about validation and imputation and their applications in the methodology of the various imputation procedures. These include weighting, replication, hot deck imputation using previous data and substitution by a zero value. The using of imputation compared with the methods used in the Canadian Labour Force Survey (LFS) is also discussed. A decision table is provided indicating the various steps to follow for a particular case of a partially completed LFS questionnaire.
Release date: 1980-12-15
138. Non-response in the Canadian Labour Force Survey Archived
Articles and reports: 12-001-X197900100002
Description: This paper includes a description of interviewer techniques and procedures used to minimize non-response, an outline of methods used to monitor and control non-response, and a discussion of how non-respondents are treated in the data processing and estimation stages of the Canadian Labour Force Survey. Recent non-response rates as well as data on the characteristics of non-respondents are also given. It is concluded that a yearly non-response rate of approximately 5 percent is probably the best that can be achieved in the Labour Force Survey.
Release date: 1979-06-15
139. Non-response and imputation Archived
Articles and reports: 12-001-X197800254830
Description:
The problems of dealing with non-response at various stages of survey planning are discussed with implications for the mean square error, practicality and possible advantages and disadvantages. Conceptual issues of editing and imputation are also considered with regard to complexity and levels of imputation. The methods of imputation include weighting, duplication, and substitution of historical records. The paper includes some methodology on the bias and variance.
Release date: 1978-12-15
140. Some factors affecting non-response Archived
Articles and reports: 12-001-X197700254828
Description:
Non-response exists in any survey, but its magnitude depends upon the type of survey, the interviewers’ ability to conduct an interview, and the respondents’ motivation to respond to survey questions. This paper discusses non-response in relation to a number of household surveys and in particular the behaviour of non-response rates over time in a continuous survey such as the Canadian Labour Force Survey.
A profile of interviewers employed by Statistics Canada shows that the correlation between non-response and a number of interviewer characteristics is not significant. Respondents themselves, and their motivation, are the key elements in an interview process and therefore in respondent relations.
This article draws on the results of various studies conducted to investigate the effects of response burden, choice of respondent and response incentives to provide some insight into the characteristics of non-respondents.
Release date: 1977-12-15

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:: 2024-07-03