Response and nonresponse

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All (136) (20 to 30 of 136 results)

21. 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
22. 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
23. 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
24. 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
25. 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
26. Introducing Adaptive Design Elements in the Panel Study “Labour Market and Social Security” (PASS) Archived
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
27. Does the first impression count? Examining the effect of the welcome screen design on the response rate Archived
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
28. Implementation of the indoor air component of cycle 2 of the Canadian Health Measures Survey Archived
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
29. Imputation for nonmonotone nonresponse in the survey of industrial research and development Archived
Articles and reports: 12-001-X201200211753
Description:
Nonresponse in longitudinal studies often occurs in a nonmonotone pattern. In the Survey of Industrial Research and Development (SIRD), it is reasonable to assume that the nonresponse mechanism is past-value-dependent in the sense that the response propensity of a study variable at time point t depends on response status and observed or missing values of the same variable at time points prior to t. Since this nonresponse is nonignorable, the parametric likelihood approach is sensitive to the specification of parametric models on both the joint distribution of variables at different time points and the nonresponse mechanism. The nonmonotone nonresponse also limits the application of inverse propensity weighting methods. By discarding all observed data from a subject after its first missing value, one can create a dataset with a monotone ignorable nonresponse and then apply established methods for ignorable nonresponse. However, discarding observed data is not desirable and it may result in inefficient estimators when many observed data are discarded. We propose to impute nonrespondents through regression under imputation models carefully created under the past-value-dependent nonresponse mechanism. This method does not require any parametric model on the joint distribution of the variables across time points or the nonresponse mechanism. Performance of the estimated means based on the proposed imputation method is investigated through some simulation studies and empirical analysis of the SIRD data.
Release date: 2012-12-19
30. Some theory for propensity-score-adjustment estimators in survey sampling Archived
Articles and reports: 12-001-X201200211754
Description:
The propensity-scoring-adjustment approach is commonly used to handle selection bias in survey sampling applications, including unit nonresponse and undercoverage. The propensity score is computed using auxiliary variables observed throughout the sample. We discuss some asymptotic properties of propensity-score-adjusted estimators and derive optimal estimators based on a regression model for the finite population. An optimal propensity-score-adjusted estimator can be implemented using an augmented propensity model. Variance estimation is discussed and the results from two simulation studies are presented.
Release date: 2012-12-19

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Analysis (135)

Analysis (135) (0 to 10 of 135 results)

1. Ensuring that the Labour Force Survey Remains the Cornerstone of Canada’s Labour Market Information Ecosystem
Articles and reports: 75-005-M2024001
Description: From 2010 to 2019, the Labour Force Survey (LFS) response rate – or the proportion of selected households who complete an LFS interview – had been on a slow downward trend, due to a range of social and technological changes which have made it more challenging to contact selected households and to persuade Canadians to participate when they are contacted. These factors were exacerbated by the COVID-19 pandemic, which resulted in the suspension of face-to-face interviewing between April 2020 and fall 2022. Statistics Canada is committed to restoring LFS response rates to the greatest extent possible. This technical paper discusses two initiatives that are underway to ensure that the LFS estimates continue to provide an accurate and representative portrait of the Canadian labour market.
Release date: 2024-02-16
2. Sample designs and estimators for multimode surveys with face-to-face data collection
Articles and reports: 12-001-X202300200006
Description: Survey researchers are increasingly turning to multimode data collection to deal with declines in survey response rates and increasing costs. An efficient approach offers the less costly modes (e.g., web) followed with a more expensive mode for a subsample of the units (e.g., households) within each primary sampling unit (PSU). We present two alternatives to this traditional design. One alternative subsamples PSUs rather than units to constrain costs. The second is a hybrid design that includes a clustered (two-stage) sample and an independent, unclustered sample. Using a simulation, we demonstrate the hybrid design has considerable advantages.
Release date: 2024-01-03
3. Attrition in the Longitudinal and International Study of Adults, Wave 1 (2012) to Wave 4 (2018)
Articles and reports: 89-648-X2022001
Description:
This report explores the size and nature of the attrition challenges faced by the Longitudinal and International Study of Adults (LISA) survey, as well as the use of a non-response weight adjustment and calibration strategy to mitigate the effects of attrition on the LISA estimates. The study focuses on data from waves 1 (2012) to 4 (2018) and uses practical examples based on selected demographic variables, to illustrate how attrition be assessed and treated.

Release date: 2022-11-14
4. Study: Attrition in the Longitudinal and International Study of Adults, Wave 1 (2012) to Wave 4 (2018)
Stats in brief: 11-001-X202231822683
Description: Release published in The Daily – Statistics Canada’s official release bulletin
Release date: 2022-11-14
5. Is undesirable answer behaviour consistent across surveys? An investigation into respondent characteristics
Articles and reports: 12-001-X202200100001
Description:
In this study, we investigate to what extent the respondent characteristics age and educational level may be associated with undesirable answer behaviour (UAB) consistently across surveys. We use data from panel respondents who participated in ten general population surveys of CentERdata and Statistics Netherlands. A new method to visually present UAB and an inventive adaptation of a non-parametric effect size measure are used. The occurrence of UAB of respondents with specific characteristics is summarized in density distributions that we refer to as respondent profiles. An adaptation of the robust effect size Cliff’s Delta is used to compare respondent profiles on the potentially consistent occurrence of UAB across surveys. Taking all surveys together, the degree of UAB varies by age and education. The results do not show consistent UAB across individual surveys: Age and educational level are associated with a relatively higher occurrence of UAB for some surveys, but a relatively lower occurrence for other surveys. We conclude that the occurrence of UAB across surveys may be more dependent on the survey and its items than on respondent’s cognitive ability.

Release date: 2022-06-21
6. Non-response follow-up for business surveys
Articles and reports: 12-001-X202200100006
Description:
In the last two decades, survey response rates have been steadily falling. In that context, it has become increasingly important for statistical agencies to develop and use methods that reduce the adverse effects of non-response on the accuracy of survey estimates. Follow-up of non-respondents may be an effective, albeit time and resource-intensive, remedy for non-response bias. We conducted a simulation study using real business survey data to shed some light on several questions about non-response follow-up. For instance, assuming a fixed non-response follow-up budget, what is the best way to select non-responding units to be followed up? How much effort should be dedicated to repeatedly following up non-respondents until a response is received? Should they all be followed up or a sample of them? If a sample is followed up, how should it be selected? We compared Monte Carlo relative biases and relative root mean square errors under different follow-up sampling designs, sample sizes and non-response scenarios. We also determined an expression for the minimum follow-up sample size required to expend the budget, on average, and showed that it maximizes the expected response rate. A main conclusion of our simulation experiment is that this sample size also appears to approximately minimize the bias and mean square error of the estimates.

Release date: 2022-06-21
7. Assessing the coverage of confidence intervals under nonresponse. A case study on income mean and quantiles in some municipalities from the 2015 Mexican Intercensal Survey
Articles and reports: 12-001-X202100200004
Description:
This note presents a comparative study of three methods for constructing confidence intervals for the mean and quantiles based on survey data with nonresponse. These methods, empirical likelihood, linearization, and that of Woodruff’s (1952), were applied to data on income obtained from the 2015 Mexican Intercensal Survey, and to simulated data. A response propensity model was used for adjusting the sampling weights, and the empirical performance of the methods was assessed in terms of the coverage of the confidence intervals through simulation studies. The empirical likelihood and linearization methods had a good performance for the mean, except when the variable of interest had some extreme values. For quantiles, the linearization method had a poor performance, while the empirical likelihood and Woodruff methods had a better one, though without reaching the nominal coverage when the variable of interest had values with high frequency near the quantile of interest.
Release date: 2022-01-06
8. A note on propensity score weighting method using paradata in survey sampling
Articles and reports: 12-001-X201900300002
Description:
Paradata is often collected during the survey process to monitor the quality of the survey response. One such paradata is a respondent behavior, which can be used to construct response models. The propensity score weight using the respondent behavior information can be applied to the final analysis to reduce the nonresponse bias. However, including the surrogate variable in the propensity score weighting does not always guarantee the efficiency gain. We show that the surrogate variable is useful only when it is correlated with the study variable. Results from a limited simulation study confirm the finding. A real data application using the Korean Workplace Panel Survey data is also presented.
Release date: 2019-12-17
9. "Optimal" calibration weights under unit nonresponse in survey sampling
Articles and reports: 12-001-X201900300006
Description:
High nonresponse is a very common problem in sample surveys today. In statistical terms we are worried about increased bias and variance of estimators for population quantities such as totals or means. Different methods have been suggested in order to compensate for this phenomenon. We can roughly divide them into imputation and calibration and it is the latter approach we will focus on here. A wide spectrum of possibilities is included in the class of calibration estimators. We explore linear calibration, where we suggest using a nonresponse version of the design-based optimal regression estimator. Comparisons are made between this estimator and a GREG type estimator. Distance measures play a very important part in the construction of calibration estimators. We show that an estimator of the average response propensity (probability) can be included in the “optimal” distance measure under nonresponse, which will help to reduce the bias of the resulting estimator. To illustrate empirically the theoretically derived results for the suggested estimators, a simulation study has been carried out. The population is called KYBOK and consists of clerical municipalities in Sweden, where the variables include financial as well as size measurements. The results are encouraging for the “optimal” estimator in combination with the estimated average response propensity, where the bias was reduced for most of the Poisson sampling cases in the study.
Release date: 2019-12-17
10. A method to correct for frame membership error in dual frame estimators
Articles and reports: 12-001-X201900300008
Description:
Dual frame surveys are useful when no single frame with adequate coverage exists. However estimators from dual frame designs require knowledge of the frame memberships of each sampled unit. When this information is not available from the frame itself, it is often collected from the respondent. When respondents provide incorrect membership information, the resulting estimators of means or totals can be biased. A method for reducing this bias, using accurate membership information obtained about a subsample of respondents, is proposed. The properties of the new estimator are examined and compared to alternative estimators. The proposed estimator is applied to the data from the motivating example, which was a recreational angler survey, using an address frame and an incomplete fishing license frame.
Release date: 2019-12-17

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:: 2024-05-23