Response and nonresponse

Filter results by

Search Help
Currently selected filters that can be removed

Keyword(s)

Geography

1 facets displayed. 0 facets selected.

Content

1 facets displayed. 0 facets selected.
Sort Help

Results

All (117)

All (117) (0 to 10 of 117 results)

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • Articles and reports: 11-522-X201300014277
    Description:

    This article gives an overview of adaptive design elements introduced to the PASS panel survey in waves four to seven. The main focus is on experimental interventions in later phases of the fieldwork. These interventions aim at balancing the sample by prioritizing low-propensity sample members. In wave 7, interviewers received a double premium for completion of interviews with low-propensity cases in the final phase of the fieldwork. This premium was restricted to a random half of the cases with low estimated response propensity and no final status after four months of prior fieldwork. This incentive was effective in increasing interviewer effort, however, led to no significant increase in response rates.

    Release date: 2014-10-31

  • Articles and reports: 12-001-X201300211885
    Description:

    Web surveys are generally connected with low response rates. Common suggestions in textbooks on Web survey research highlight the importance of the welcome screen in encouraging respondents to take part. The importance of this screen has been empirically proven in research, showing that most respondents breakoff at the welcome screen. However, there has been little research on the effect of the design of this screen on the level of the breakoff rate. In a study conducted at the University of Konstanz, three experimental treatments were added to a survey of the first-year student population (2,629 students) to assess the impact of different design features of this screen on the breakoff rates. The methodological experiments included varying the background color of the welcome screen, varying the promised task duration on this first screen, and varying the length of the information provided on the welcome screen explaining the privacy rights of the respondents. The analyses show that the longer stated length and the more attention given to explaining privacy rights on the welcome screen, the fewer respondents started and completed the survey. However, the use of a different background color does not result in the expected significant difference.

    Release date: 2014-01-15

  • Articles and reports: 82-003-X201300511792
    Geography: Canada
    Description:

    This article describes implementation of the indoor air component of the 2009 to 2011 Canadian Health Measures Survey and presents information about response rates and results of field quality control samples.

    Release date: 2013-05-15
Data (0)

Data (0) (0 results)

No content available at this time.

Analysis (112)

Analysis (112) (0 to 10 of 112 results)

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • Articles and reports: 11-522-X201300014277
    Description:

    This article gives an overview of adaptive design elements introduced to the PASS panel survey in waves four to seven. The main focus is on experimental interventions in later phases of the fieldwork. These interventions aim at balancing the sample by prioritizing low-propensity sample members. In wave 7, interviewers received a double premium for completion of interviews with low-propensity cases in the final phase of the fieldwork. This premium was restricted to a random half of the cases with low estimated response propensity and no final status after four months of prior fieldwork. This incentive was effective in increasing interviewer effort, however, led to no significant increase in response rates.

    Release date: 2014-10-31

  • Articles and reports: 12-001-X201300211885
    Description:

    Web surveys are generally connected with low response rates. Common suggestions in textbooks on Web survey research highlight the importance of the welcome screen in encouraging respondents to take part. The importance of this screen has been empirically proven in research, showing that most respondents breakoff at the welcome screen. However, there has been little research on the effect of the design of this screen on the level of the breakoff rate. In a study conducted at the University of Konstanz, three experimental treatments were added to a survey of the first-year student population (2,629 students) to assess the impact of different design features of this screen on the breakoff rates. The methodological experiments included varying the background color of the welcome screen, varying the promised task duration on this first screen, and varying the length of the information provided on the welcome screen explaining the privacy rights of the respondents. The analyses show that the longer stated length and the more attention given to explaining privacy rights on the welcome screen, the fewer respondents started and completed the survey. However, the use of a different background color does not result in the expected significant difference.

    Release date: 2014-01-15

  • Articles and reports: 82-003-X201300511792
    Geography: Canada
    Description:

    This article describes implementation of the indoor air component of the 2009 to 2011 Canadian Health Measures Survey and presents information about response rates and results of field quality control samples.

    Release date: 2013-05-15
Reference (8)

Reference (8) ((8 results))

  • Surveys and statistical programs – Documentation: 12-001-X201200211755
    Description:

    Non-response in longitudinal studies is addressed by assessing the accuracy of response propensity models constructed to discriminate between and predict different types of non-response. Particular attention is paid to summary measures derived from receiver operating characteristic (ROC) curves and logit rank plots. The ideas are applied to data from the UK Millennium Cohort Study. The results suggest that the ability to discriminate between and predict non-respondents is not high. Weights generated from the response propensity models lead to only small adjustments in employment transitions. Conclusions are drawn in terms of the potential of interventions to prevent non-response.

    Release date: 2012-12-19

  • Surveys and statistical programs – Documentation: 12-001-X201200111688
    Description:

    We study the problem of nonignorable nonresponse in a two dimensional contingency table which can be constructed for each of several small areas when there is both item and unit nonresponse. In general, the provision for both types of nonresponse with small areas introduces significant additional complexity in the estimation of model parameters. For this paper, we conceptualize the full data array for each area to consist of a table for complete data and three supplemental tables for missing row data, missing column data, and missing row and column data. For nonignorable nonresponse, the total cell probabilities are allowed to vary by area, cell and these three types of "missingness". The underlying cell probabilities (i.e., those which would apply if full classification were always possible) for each area are generated from a common distribution and their similarity across the areas is parametrically quantified. Our approach is an extension of the selection approach for nonignorable nonresponse investigated by Nandram and Choi (2002a, b) for binary data; this extension creates additional complexity because of the multivariate nature of the data coupled with the small area structure. As in that earlier work, the extension is an expansion model centered on an ignorable nonresponse model so that the total cell probability is dependent upon which of the categories is the response. Our investigation employs hierarchical Bayesian models and Markov chain Monte Carlo methods for posterior inference. The models and methods are illustrated with data from the third National Health and Nutrition Examination Survey.

    Release date: 2012-06-27

  • Surveys and statistical programs – Documentation: 12-001-X200900211037
    Description:

    Randomized response strategies, which have originally been developed as statistical methods to reduce nonresponse as well as untruthful answering, can also be applied in the field of statistical disclosure control for public use microdata files. In this paper a standardization of randomized response techniques for the estimation of proportions of identifying or sensitive attributes is presented. The statistical properties of the standardized estimator are derived for general probability sampling. In order to analyse the effect of different choices of the method's implicit "design parameters" on the performance of the estimator we have to include measures of privacy protection in our considerations. These yield variance-optimum design parameters given a certain level of privacy protection. To this end the variables have to be classified into different categories of sensitivity. A real-data example applies the technique in a survey on academic cheating behaviour.

    Release date: 2009-12-23

  • Surveys and statistical programs – Documentation: 12-001-X20000025532
    Description:

    When a survey response mechanism depends on a variable of interest measured within the same survey and observed for only part of the sample, the situation is one of nonignorable nonresponse. In such a situation, ignoring the nonresponse can generate significant bias in the estimation of a mean or of a total. To solve this problem, one option is the joint modeling of the response mechanism and the variable of interest, followed by estimation using the maximum likelihood method. The main criticism levelled at this method is that estimation using the maximum likelihood method is based on the hypothesis of error normality for the model involving the variable of interest, and this hypothesis is difficult to verify. In this paper, the author proposes an estimation method that is robust to the hypothesis of normality, so constructed that there is no need to specify the distribution of errors. The method is evaluated using Monte Carlo simulations. The author also proposes a simple method of verifying the validity of the hypothesis of error normality whenever nonresponse is not ignorable.

    Release date: 2001-02-28

  • Surveys and statistical programs – Documentation: 12-001-X20000015183
    Description:

    For surveys which involve more than one stage of data collection, one method recommended for adjusting weights for nonresponse (after the first stage of data collection) entails utilizing auxiliary variables (from previous stages of data collection) which are identified as predictors of nonresponse.

    Release date: 2000-08-30

  • Surveys and statistical programs – Documentation: 12-001-X19980024349
    Description:

    Measurement of gross flows in labour force status is an important objective of the continuing labour force surveys carried out by many national statistics agencies. However, it is well known that estimation of these flows can be complicated by nonresponse, measurement errors, sample rotation and complex design effects. Motivated by nonresponse patterns in household-based surveys, this paper focuses on estimation of labour force gross flows, while simultaneously adjusting for nonignorable nonresponse. Previous model-based approaches to gross flows estimation have assumed nonresponse to be an individual-level process. We propose a class of models that allow for nonignorable household-level nonresponse. A simulation study is used to show, that individual-level labour force gross flows estimates from household-based survey data, may be biased and that estimates using household-level models can offer a reduction in this bias.

    Release date: 1999-01-14

  • Surveys and statistical programs – Documentation: 12-001-X19980024352
    Description:

    The National Population Health Survey (NPHS) is one of Statistics Canada's three major longitudinal household surveys providing an extensive coverage of the Canadian population. A panel of approximately 17,000 people are being followed up every two years for up to twenty years. The survey data are used for longitudinal analyses, although an important objective is the production of cross-sectional estimates. Each cycle panel respondents provide detailed health information (H) while, to augment the cross-sectional sample, general socio-demographic and health information (G) are collected from all members of their households. This particular collection strategy presents several observable response patterns for Panel Members after two cycles: GH-GH, GH-G*, GH-**, G*-GH, G*-G* and G*-**, where "*" denotes a missing portion of data. The article presents the methodology developed to deal with these types of longitudinal nonresponse as well as with nonresponse from a cross-sectional perspective. The use of weight adjustments for nonresponse and the creation of adjustment cells for weighting using a CHAID algorithm are discussed.

    Release date: 1999-01-14

  • Surveys and statistical programs – Documentation: 12-001-X19970013103
    Description:

    This paper discusses the use of some simple diagnostics to guide the formation of nonresponse adjustment cells. Following Little (1986), we consider construction of adjustment cells by grouping sample units according to their estimated response probabilities or estimated survey items. Four issues receive principal attention: assessment of the sensitivity of adjusted mean estimates to changes in k, the number of cells used; identification of specific cells that require additional refinement; comparison of adjusted and unadjusted mean estimates; and comparison of estimation results from estimated-probability and estimated-item based cells. The proposed methods are motivated and illustrated with an application involving estimation of mean consumer unit income from the U.S. Consumer Expenditure Survey.

    Release date: 1997-08-18
Date modified: