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• 1. Modelling of complex survey data: Why model? Why is it a problem? How can we approach it? Archived

  Articles and reports: 12-001-X201100211602

  Description:

  This article attempts to answer the three questions appearing in the title. It starts by discussing unique features of complex survey data not shared by other data sets, which require special attention but suggest a large variety of diverse inference procedures. Next a large number of different approaches proposed in the literature for handling these features are reviewed with discussion on their merits and limitations. The approaches differ in the conditions underlying their use, additional data required for their application, goodness of fit testing, the inference objectives that they accommodate, statistical efficiency, computational demands, and the skills required from analysts fitting the model. The last part of the paper presents simulation results, which compare the approaches when estimating linear regression coefficients from a stratified sample in terms of bias, variance, and coverage rates. It concludes with a short discussion of pending issues.

  Release date: 2011-12-21

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• 2. On bias-robust mean squared error estimation for pseudo-linear small area estimators Archived

  Articles and reports: 12-001-X201100211604

  Description:

  We propose a method of mean squared error (MSE) estimation for estimators of finite population domain means that can be expressed in pseudo-linear form, i.e., as weighted sums of sample values. In particular, it can be used for estimating the MSE of the empirical best linear unbiased predictor, the model-based direct estimator and the M-quantile predictor. The proposed method represents an extension of the ideas in Royall and Cumberland (1978) and leads to MSE estimators that are simpler to implement, and potentially more bias-robust, than those suggested in the small area literature. However, it should be noted that the MSE estimators defined using this method can also exhibit large variability when the area-specific sample sizes are very small. We illustrate the performance of the method through extensive model-based and design-based simulation, with the latter based on two realistic survey data sets containing small area information.

  Release date: 2011-12-21

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• 3. User's Guide for Cross-Sectional Public-Use Microdata File: Survey of Labour and Income Dynamics (SLID), 2009 Archived

  Surveys and statistical programs – Documentation: 75F0002M2011004

  Description:

  This series provides detailed documentation on income developments, including survey design issues, data quality evaluation and exploratory research for the Survey of Labour and Income Dynamics in 2009.

  Release date: 2011-10-27

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• 1. Modelling of complex survey data: Why model? Why is it a problem? How can we approach it? Archived

  Articles and reports: 12-001-X201100211602

  Description:

  This article attempts to answer the three questions appearing in the title. It starts by discussing unique features of complex survey data not shared by other data sets, which require special attention but suggest a large variety of diverse inference procedures. Next a large number of different approaches proposed in the literature for handling these features are reviewed with discussion on their merits and limitations. The approaches differ in the conditions underlying their use, additional data required for their application, goodness of fit testing, the inference objectives that they accommodate, statistical efficiency, computational demands, and the skills required from analysts fitting the model. The last part of the paper presents simulation results, which compare the approaches when estimating linear regression coefficients from a stratified sample in terms of bias, variance, and coverage rates. It concludes with a short discussion of pending issues.

  Release date: 2011-12-21

  ---

• 2. On bias-robust mean squared error estimation for pseudo-linear small area estimators Archived

  Articles and reports: 12-001-X201100211604

  Description:

  We propose a method of mean squared error (MSE) estimation for estimators of finite population domain means that can be expressed in pseudo-linear form, i.e., as weighted sums of sample values. In particular, it can be used for estimating the MSE of the empirical best linear unbiased predictor, the model-based direct estimator and the M-quantile predictor. The proposed method represents an extension of the ideas in Royall and Cumberland (1978) and leads to MSE estimators that are simpler to implement, and potentially more bias-robust, than those suggested in the small area literature. However, it should be noted that the MSE estimators defined using this method can also exhibit large variability when the area-specific sample sizes are very small. We illustrate the performance of the method through extensive model-based and design-based simulation, with the latter based on two realistic survey data sets containing small area information.

  Release date: 2011-12-21

Reference (1) ((1 result))

• 1. User's Guide for Cross-Sectional Public-Use Microdata File: Survey of Labour and Income Dynamics (SLID), 2009 Archived

  Surveys and statistical programs – Documentation: 75F0002M2011004

  Description:

  This series provides detailed documentation on income developments, including survey design issues, data quality evaluation and exploratory research for the Survey of Labour and Income Dynamics in 2009.

  Release date: 2011-10-27