Keyword search
Filter results by
Search HelpKeyword(s)
Subject
Type
Year of publication
Survey or statistical program
Results
All (30)
All (30) (0 to 10 of 30 results)
- Articles and reports: 12-001-X202200200001Description:
Conceptual arguments and examples are presented suggesting that the Bayesian approach to survey inference can address the many and varied challenges of survey analysis. Bayesian models that incorporate features of the complex design can yield inferences that are relevant for the specific data set obtained, but also have good repeated-sampling properties. Examples focus on the role of auxiliary variables and sampling weights, and methods for handling nonresponse. The article offers ten top reasons for favoring the Bayesian approach to survey inference.
Release date: 2022-12-15 - Articles and reports: 12-001-X202200100006Description:
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 - Articles and reports: 12-001-X201900200008Description:
High nonresponse occurs in many sample surveys today, including important surveys carried out by government statistical agencies. An adaptive data collection can be advantageous in those conditions: Lower nonresponse bias in survey estimates can be gained, up to a point, by producing a well-balanced set of respondents. Auxiliary variables serve a twofold purpose: Used in the estimation phase, through calibrated adjustment weighting, they reduce, but do not entirely remove, the bias. In the preceding adaptive data collection phase, auxiliary variables also play a major role: They are instrumental in reducing the imbalance in the ultimate set of respondents. For such combined use of auxiliary variables, the deviation of the calibrated estimate from the unbiased estimate (under full response) is studied in the article. We show that this deviation is a sum of two components. The reducible component can be decreased through adaptive data collection, all the way to zero if perfectly balanced response is realized with respect to a chosen auxiliary vector. By contrast, the resisting component changes little or not at all by a better balanced response; it represents a part of the deviation that adaptive design does not get rid of. The relative size of the former component is an indicator of the potential payoff from an adaptive survey design.
Release date: 2019-06-27 - Articles and reports: 12-001-X201900200009Description:
In recent years, there has been a strong interest in indirect measures of nonresponse bias in surveys or other forms of data collection. This interest originates from gradually decreasing propensities to respond to surveys parallel to pressures on survey budgets. These developments led to a growing focus on the representativeness or balance of the responding sample units with respect to relevant auxiliary variables. One example of a measure is the representativeness indicator, or R-indicator. The R-indicator is based on the design-weighted sample variation of estimated response propensities. It pre-supposes linked auxiliary data. One of the criticisms of the indicator is that it cannot be used in settings where auxiliary information is available only at the population level. In this paper, we propose a new method for estimating response propensities that does not need auxiliary information for non-respondents to the survey and is based on population auxiliary information. These population-based response propensities can then be used to develop R-indicators that employ population contingency tables or population frequency counts. We discuss the statistical properties of the indicators, and evaluate their performance using an evaluation study based on real census data and an application from the Dutch Health Survey.
Release date: 2019-06-27 - Articles and reports: 12-001-X201800254952Description:
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 - Articles and reports: 12-001-X201800154929Description:
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 - Articles and reports: 12-001-X201200211755Description:
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 - Articles and reports: 12-001-X201200111688Description:
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: 62F0026M2010001Description:
This report describes the quality indicators produced for the 2004 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2010-04-26 - Surveys and statistical programs – Documentation: 62F0026M2010002Description:
This report describes the quality indicators produced for the 2005 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2010-04-26
Data (0)
Data (0) (0 results)
No content available at this time.
Analysis (27)
Analysis (27) (0 to 10 of 27 results)
- Articles and reports: 12-001-X202200200001Description:
Conceptual arguments and examples are presented suggesting that the Bayesian approach to survey inference can address the many and varied challenges of survey analysis. Bayesian models that incorporate features of the complex design can yield inferences that are relevant for the specific data set obtained, but also have good repeated-sampling properties. Examples focus on the role of auxiliary variables and sampling weights, and methods for handling nonresponse. The article offers ten top reasons for favoring the Bayesian approach to survey inference.
Release date: 2022-12-15 - Articles and reports: 12-001-X202200100006Description:
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 - Articles and reports: 12-001-X201900200008Description:
High nonresponse occurs in many sample surveys today, including important surveys carried out by government statistical agencies. An adaptive data collection can be advantageous in those conditions: Lower nonresponse bias in survey estimates can be gained, up to a point, by producing a well-balanced set of respondents. Auxiliary variables serve a twofold purpose: Used in the estimation phase, through calibrated adjustment weighting, they reduce, but do not entirely remove, the bias. In the preceding adaptive data collection phase, auxiliary variables also play a major role: They are instrumental in reducing the imbalance in the ultimate set of respondents. For such combined use of auxiliary variables, the deviation of the calibrated estimate from the unbiased estimate (under full response) is studied in the article. We show that this deviation is a sum of two components. The reducible component can be decreased through adaptive data collection, all the way to zero if perfectly balanced response is realized with respect to a chosen auxiliary vector. By contrast, the resisting component changes little or not at all by a better balanced response; it represents a part of the deviation that adaptive design does not get rid of. The relative size of the former component is an indicator of the potential payoff from an adaptive survey design.
Release date: 2019-06-27 - Articles and reports: 12-001-X201900200009Description:
In recent years, there has been a strong interest in indirect measures of nonresponse bias in surveys or other forms of data collection. This interest originates from gradually decreasing propensities to respond to surveys parallel to pressures on survey budgets. These developments led to a growing focus on the representativeness or balance of the responding sample units with respect to relevant auxiliary variables. One example of a measure is the representativeness indicator, or R-indicator. The R-indicator is based on the design-weighted sample variation of estimated response propensities. It pre-supposes linked auxiliary data. One of the criticisms of the indicator is that it cannot be used in settings where auxiliary information is available only at the population level. In this paper, we propose a new method for estimating response propensities that does not need auxiliary information for non-respondents to the survey and is based on population auxiliary information. These population-based response propensities can then be used to develop R-indicators that employ population contingency tables or population frequency counts. We discuss the statistical properties of the indicators, and evaluate their performance using an evaluation study based on real census data and an application from the Dutch Health Survey.
Release date: 2019-06-27 - Articles and reports: 12-001-X201800254952Description:
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 - Articles and reports: 12-001-X201800154929Description:
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 - Articles and reports: 12-001-X201200211755Description:
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 - Articles and reports: 12-001-X201200111688Description:
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 - 9. International surveys: Motives and methodologies ArchivedArticles and reports: 11-522-X200800010937Description:
The context of the discussion is the increasing incidence of international surveys, of which one is the International Tobacco Control (ITC) Policy Evaluation Project, which began in 2002. The ITC country surveys are longitudinal, and their aim is to evaluate the effects of policy measures being introduced in various countries under the WHO Framework Convention on Tobacco Control. The challenges of organization, data collection and analysis in international surveys are reviewed and illustrated. Analysis is an increasingly important part of the motivation for large scale cross-cultural surveys. The fundamental challenge for analysis is to discern the real response (or lack of response) to policy change, separating it from the effects of data collection mode, differential non-response, external events, time-in-sample, culture, and language. Two problems relevant to statistical analysis are discussed. The first problem is the question of when and how to analyze pooled data from several countries, in order to strengthen conclusions which might be generally valid. While in some cases this seems to be straightforward, there are differing opinions on the extent to which pooling is possible and reasonable. It is suggested that for formal comparisons, random effects models are of conceptual use. The second problem is to find models of measurement across cultures and data collection modes which will enable calibration of continuous, binary and ordinal responses, and produce comparisons from which extraneous effects have been removed. It is noted that hierarchical models provide a natural way of relaxing requirements of model invariance across groups.
Release date: 2009-12-03 - Articles and reports: 11-522-X200800010958Description:
Telephone Data Entry (TDE) is a system by which survey respondents can return their data to the Office for National Statistics (ONS) using the keypad on their telephone and currently accounts for approximately 12% of total responses to ONS business surveys. ONS is currently increasing the number of surveys which use TDE as the primary mode of response and this paper gives an overview of the redevelopment project covering; the redevelopment of the paper questionnaire, enhancements made to the TDE system and the results from piloting these changes. Improvements to the quality of the data received and increased response via TDE as a result of these developments suggest that data quality improvements and cost savings are possible as a result of promoting TDE as the primary mode of response to short term surveys.
Release date: 2009-12-03
Reference (3)
Reference (3) ((3 results))
- Surveys and statistical programs – Documentation: 62F0026M2010001Description:
This report describes the quality indicators produced for the 2004 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2010-04-26 - Surveys and statistical programs – Documentation: 62F0026M2010002Description:
This report describes the quality indicators produced for the 2005 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2010-04-26 - Surveys and statistical programs – Documentation: 62F0026M2010003Description:
This report describes the quality indicators produced for the 2006 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2010-04-26
- Date modified: