Response and nonresponse

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  • Articles and reports: 12-001-X20070019855
    Description:

    In surveys under cluster sampling, nonresponse on a variable is often dependent on a cluster level random effect and, hence, is nonignorable. Estimators of the population mean obtained by mean imputation or reweighting under the ignorable nonresponse assumption are then biased. We propose an unbiased estimator of the population mean by imputing or reweighting within each sampled cluster or a group of sampled clusters sharing some common feature. Some simulation results are presented to study the performance of the proposed estimator.

    Release date: 2007-06-28

  • Articles and reports: 11-522-X20050019446
    Description:

    One method used to examine the effect of nonresponse involves comparing survey participants who require less effort on the part of the interviewer with those who require more effort. A persistent problem for researchers involves the criteria to use in determining membership in high effort groups.

    Release date: 2007-03-02

  • Articles and reports: 11-522-X20050019447
    Description:

    To understand the selection biases in model estimation when using longitudinal survey panel microdata, we consider a structural model composed of three equations for non-attrition/response, employment and wages. The three equations are freely correlated.

    Release date: 2007-03-02

  • Articles and reports: 11-522-X20050019464
    Description:

    The Quarterly Services Survey has maintained comprehensive response data since the survey's inception. In analyzing the data, we concentrate on three fundamental features of response: rate, timeliness, and quality. We examine these three components across multiple dimensions. We observe the effect associated with NAICS classification, company size and response mode.

    Release date: 2007-03-02

  • Articles and reports: 11-522-X20050019468
    Description:

    At the time of recruitment, the participants in a longitudinal survey are chosen to be representative of a population. As time goes on, typically some of the participants will drop out, and dropout may be informative in the sense of depending on the response variables of interest. However, even if dropout is minimal, the participants who continue to the second and third waves of a longitudinal survey may differ from those they supposedly represent in subtle ways. It is clearly important to take such possibilities into account when designing and analyzing longitudinal survey data before and after an intervention.

    Release date: 2007-03-02
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  • Articles and reports: 12-001-X20070019855
    Description:

    In surveys under cluster sampling, nonresponse on a variable is often dependent on a cluster level random effect and, hence, is nonignorable. Estimators of the population mean obtained by mean imputation or reweighting under the ignorable nonresponse assumption are then biased. We propose an unbiased estimator of the population mean by imputing or reweighting within each sampled cluster or a group of sampled clusters sharing some common feature. Some simulation results are presented to study the performance of the proposed estimator.

    Release date: 2007-06-28

  • Articles and reports: 11-522-X20050019446
    Description:

    One method used to examine the effect of nonresponse involves comparing survey participants who require less effort on the part of the interviewer with those who require more effort. A persistent problem for researchers involves the criteria to use in determining membership in high effort groups.

    Release date: 2007-03-02

  • Articles and reports: 11-522-X20050019447
    Description:

    To understand the selection biases in model estimation when using longitudinal survey panel microdata, we consider a structural model composed of three equations for non-attrition/response, employment and wages. The three equations are freely correlated.

    Release date: 2007-03-02

  • Articles and reports: 11-522-X20050019464
    Description:

    The Quarterly Services Survey has maintained comprehensive response data since the survey's inception. In analyzing the data, we concentrate on three fundamental features of response: rate, timeliness, and quality. We examine these three components across multiple dimensions. We observe the effect associated with NAICS classification, company size and response mode.

    Release date: 2007-03-02

  • Articles and reports: 11-522-X20050019468
    Description:

    At the time of recruitment, the participants in a longitudinal survey are chosen to be representative of a population. As time goes on, typically some of the participants will drop out, and dropout may be informative in the sense of depending on the response variables of interest. However, even if dropout is minimal, the participants who continue to the second and third waves of a longitudinal survey may differ from those they supposedly represent in subtle ways. It is clearly important to take such possibilities into account when designing and analyzing longitudinal survey data before and after an intervention.

    Release date: 2007-03-02
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