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- Articles and reports: 12-001-X200800210756Description:
In longitudinal surveys nonresponse often occurs in a pattern that is not monotone. We consider estimation of time-dependent means under the assumption that the nonresponse mechanism is last-value-dependent. Since the last value itself may be missing when nonresponse is nonmonotone, the nonresponse mechanism under consideration is nonignorable. We propose an imputation method by first deriving some regression imputation models according to the nonresponse mechanism and then applying nonparametric regression imputation. We assume that the longitudinal data follow a Markov chain with finite second-order moments. No other assumption is imposed on the joint distribution of longitudinal data and their nonresponse indicators. A bootstrap method is applied for variance estimation. Some simulation results and an example concerning the Current Employment Survey are presented.
Release date: 2008-12-23 - Articles and reports: 12-001-X200800210757Description:
Sample weights can be calibrated to reflect the known population totals of a set of auxiliary variables. Predictors of finite population totals calculated using these weights have low bias if these variables are related to the variable of interest, but can have high variance if too many auxiliary variables are used. This article develops an "adaptive calibration" approach, where the auxiliary variables to be used in weighting are selected using sample data. Adaptively calibrated estimators are shown to have lower mean squared error and better coverage properties than non-adaptive estimators in many cases.
Release date: 2008-12-23 - 3. Variance estimation of changes in repeated surveys and its application to the Swiss survey of value added ArchivedArticles and reports: 12-001-X200800210758Description:
We propose a method for estimating the variance of estimators of changes over time, a method that takes account of all the components of these estimators: the sampling design, treatment of non-response, treatment of large companies, correlation of non-response from one wave to another, the effect of using a panel, robustification, and calibration using a ratio estimator. This method, which serves to determine the confidence intervals of changes over time, is then applied to the Swiss survey of value added.
Release date: 2008-12-23 - Articles and reports: 12-001-X200800210764Description:
This paper considers situations where the target response value is either zero or an observation from a continuous distribution. A typical example analyzed in the paper is the assessment of literacy proficiency with the possible outcome being either zero, indicating illiteracy, or a positive score measuring the level of literacy. Our interest is in how to obtain valid estimates of the average response, or the proportion of positive responses in small areas, for which only small samples or no samples are available. As in other small area estimation problems, the small sample sizes in at least some of the sampled areas and/or the existence of nonsampled areas requires the use of model based methods. Available methods, however, are not suitable for this kind of data because of the mixed distribution of the responses, having a large peak at zero, juxtaposed to a continuous distribution for the rest of the responses. We develop, therefore, a suitable two-part random effects model and show how to fit the model and assess its goodness of fit, and how to compute the small area estimators of interest and measure their precision. The proposed method is illustrated using simulated data and data obtained from a literacy survey conducted in Cambodia.
Release date: 2008-12-23 - Articles and reports: 82-622-X2008002Geography: CanadaDescription: This study uses data from the Canadian Survey of Experiences with Primary Health Care to assess the degree to which Canadians have access to primary health care teams and the impact of those teams on processes of care and on outcomes. The study is comprised of three projects: determinants of access to primary health care teams (Project 1); the impact of primary health care teams on various processes of care (Project 2); and identification of pathways through which primary health care teams affect outcomes of care (Project 3).Release date: 2008-07-15
- Articles and reports: 82-622-X2008001Geography: CanadaDescription: In this study, I examine the factorial validity of selected modules from the Canadian Survey of Experiences with Primary Health Care (CSE-PHC), in order to determine the potential for combining the items within each module into summary indices representing global primary health care concepts. The modules examined were: Patient Assessment of Chronic Illness Care (PACIC), Patient Activation (PA), Managing Own Health Care (MOHC), and Confidence in the Health Care System (CHCS). Confirmatory factor analyses were conducted on each module to assess the degree to which multiple observed items reflected the presence of common latent factors. While a four-factor model was initially specified for the PACIC instrument on the basis of priory theory and research, it did not fit the data well; rather, a revised two-factor model was found to be most appropriate. These two factors were labelled: "Whole Person Care" and "Coordination of Care". The remaining modules studied here (i.e., PA, MOHC, and CHCS) were all well-represented by single-factor models. The results suggest that the original factor structure of the PACIC developed within studies using clinical samples does not hold in general populations, although the precise reasons for this are not clear. Further empirical investigation will be required to shed more light on this discrepancy. The two factors identified here for the PACIC, as well as the single factors produced for the PA, MOHC, and CHCS could be used as the basis of summary indices for use in further analyses with the CSE-PHC.Release date: 2008-07-08
- Articles and reports: 11-522-X200600110424Description:
The International Tobacco Control (ITC) Policy Evaluation China Survey uses a multi-stage unequal probability sampling design with upper level clusters selected by the randomized systematic PPS sampling method. A difficulty arises in the execution of the survey: several selected upper level clusters refuse to participate in the survey and have to be replaced by substitute units, selected from units not included in the initial sample and once again using the randomized systematic PPS sampling method. Under such a scenario the first order inclusion probabilities of the final selected units are very difficult to calculate and the second order inclusion probabilities become virtually intractable. In this paper we develop a simulation-based approach for computing the first and the second order inclusion probabilities when direct calculation is prohibitive or impossible. The efficiency and feasibility of the proposed approach are demonstrated through both theoretical considerations and numerical examples. Several R/S-PLUS functions and codes for the proposed procedure are included. The approach can be extended to handle more complex refusal/substitution scenarios one may encounter in practice.
Release date: 2008-06-26 - 8. A Bayesian allocation of undecided voters ArchivedArticles and reports: 12-001-X200800110606Description:
Data from election polls in the US are typically presented in two-way categorical tables, and there are many polls before the actual election in November. For example, in the Buckeye State Poll in 1998 for governor there are three polls, January, April and October; the first category represents the candidates (e.g., Fisher, Taft and other) and the second category represents the current status of the voters (likely to vote and not likely to vote for governor of Ohio). There is a substantial number of undecided voters for one or both categories in all three polls, and we use a Bayesian method to allocate the undecided voters to the three candidates. This method permits modeling different patterns of missingness under ignorable and nonignorable assumptions, and a multinomial-Dirichlet model is used to estimate the cell probabilities which can help to predict the winner. We propose a time-dependent nonignorable nonresponse model for the three tables. Here, a nonignorable nonresponse model is centered on an ignorable nonresponse model to induce some flexibility and uncertainty about ignorabilty or nonignorability. As competitors we also consider two other models, an ignorable and a nonignorable nonresponse model. These latter two models assume a common stochastic process to borrow strength over time. Markov chain Monte Carlo methods are used to fit the models. We also construct a parameter that can potentially be used to predict the winner among the candidates in the November election.
Release date: 2008-06-26 - 9. Generalized regression estimators of a finite population total using the Box-Cox technique ArchivedArticles and reports: 12-001-X200800110610Description:
A new generalized regression estimator of a finite population total based on the Box-Cox transformation technique and its variance estimator are proposed under a general unequal probability sampling design. By being design consistent, the proposed estimator maintains the robustness property of the GREG estimator even if the underlying model fails. Furthermore, the Box-Cox technique automatically finds a reasonable transformation for the dependent variable using the data. The robustness and efficiency of the new estimator are evaluated analytically and via Monte Carlo simulation studies.
Release date: 2008-06-26 - 10. A noninformative Bayesian approach to finite population sampling using auxiliary variables ArchivedArticles and reports: 12-001-X200800110611Description:
In finite population sampling prior information is often available in the form of partial knowledge about an auxiliary variable, for example its mean may be known. In such cases, the ratio estimator and the regression estimator are often used for estimating the population mean of the characteristic of interest. The Polya posterior has been developed as a noninformative Bayesian approach to survey sampling. It is appropriate when little or no prior information about the population is available. Here we show that it can be extended to incorporate types of partial prior information about auxiliary variables. We will see that it typically yields procedures with good frequentist properties even in some problems where standard frequentist methods are difficult to apply.
Release date: 2008-06-26
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Analysis (33)
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- Articles and reports: 12-001-X200800210756Description:
In longitudinal surveys nonresponse often occurs in a pattern that is not monotone. We consider estimation of time-dependent means under the assumption that the nonresponse mechanism is last-value-dependent. Since the last value itself may be missing when nonresponse is nonmonotone, the nonresponse mechanism under consideration is nonignorable. We propose an imputation method by first deriving some regression imputation models according to the nonresponse mechanism and then applying nonparametric regression imputation. We assume that the longitudinal data follow a Markov chain with finite second-order moments. No other assumption is imposed on the joint distribution of longitudinal data and their nonresponse indicators. A bootstrap method is applied for variance estimation. Some simulation results and an example concerning the Current Employment Survey are presented.
Release date: 2008-12-23 - Articles and reports: 12-001-X200800210757Description:
Sample weights can be calibrated to reflect the known population totals of a set of auxiliary variables. Predictors of finite population totals calculated using these weights have low bias if these variables are related to the variable of interest, but can have high variance if too many auxiliary variables are used. This article develops an "adaptive calibration" approach, where the auxiliary variables to be used in weighting are selected using sample data. Adaptively calibrated estimators are shown to have lower mean squared error and better coverage properties than non-adaptive estimators in many cases.
Release date: 2008-12-23 - 3. Variance estimation of changes in repeated surveys and its application to the Swiss survey of value added ArchivedArticles and reports: 12-001-X200800210758Description:
We propose a method for estimating the variance of estimators of changes over time, a method that takes account of all the components of these estimators: the sampling design, treatment of non-response, treatment of large companies, correlation of non-response from one wave to another, the effect of using a panel, robustification, and calibration using a ratio estimator. This method, which serves to determine the confidence intervals of changes over time, is then applied to the Swiss survey of value added.
Release date: 2008-12-23 - Articles and reports: 12-001-X200800210764Description:
This paper considers situations where the target response value is either zero or an observation from a continuous distribution. A typical example analyzed in the paper is the assessment of literacy proficiency with the possible outcome being either zero, indicating illiteracy, or a positive score measuring the level of literacy. Our interest is in how to obtain valid estimates of the average response, or the proportion of positive responses in small areas, for which only small samples or no samples are available. As in other small area estimation problems, the small sample sizes in at least some of the sampled areas and/or the existence of nonsampled areas requires the use of model based methods. Available methods, however, are not suitable for this kind of data because of the mixed distribution of the responses, having a large peak at zero, juxtaposed to a continuous distribution for the rest of the responses. We develop, therefore, a suitable two-part random effects model and show how to fit the model and assess its goodness of fit, and how to compute the small area estimators of interest and measure their precision. The proposed method is illustrated using simulated data and data obtained from a literacy survey conducted in Cambodia.
Release date: 2008-12-23 - Articles and reports: 82-622-X2008002Geography: CanadaDescription: This study uses data from the Canadian Survey of Experiences with Primary Health Care to assess the degree to which Canadians have access to primary health care teams and the impact of those teams on processes of care and on outcomes. The study is comprised of three projects: determinants of access to primary health care teams (Project 1); the impact of primary health care teams on various processes of care (Project 2); and identification of pathways through which primary health care teams affect outcomes of care (Project 3).Release date: 2008-07-15
- Articles and reports: 82-622-X2008001Geography: CanadaDescription: In this study, I examine the factorial validity of selected modules from the Canadian Survey of Experiences with Primary Health Care (CSE-PHC), in order to determine the potential for combining the items within each module into summary indices representing global primary health care concepts. The modules examined were: Patient Assessment of Chronic Illness Care (PACIC), Patient Activation (PA), Managing Own Health Care (MOHC), and Confidence in the Health Care System (CHCS). Confirmatory factor analyses were conducted on each module to assess the degree to which multiple observed items reflected the presence of common latent factors. While a four-factor model was initially specified for the PACIC instrument on the basis of priory theory and research, it did not fit the data well; rather, a revised two-factor model was found to be most appropriate. These two factors were labelled: "Whole Person Care" and "Coordination of Care". The remaining modules studied here (i.e., PA, MOHC, and CHCS) were all well-represented by single-factor models. The results suggest that the original factor structure of the PACIC developed within studies using clinical samples does not hold in general populations, although the precise reasons for this are not clear. Further empirical investigation will be required to shed more light on this discrepancy. The two factors identified here for the PACIC, as well as the single factors produced for the PA, MOHC, and CHCS could be used as the basis of summary indices for use in further analyses with the CSE-PHC.Release date: 2008-07-08
- Articles and reports: 11-522-X200600110424Description:
The International Tobacco Control (ITC) Policy Evaluation China Survey uses a multi-stage unequal probability sampling design with upper level clusters selected by the randomized systematic PPS sampling method. A difficulty arises in the execution of the survey: several selected upper level clusters refuse to participate in the survey and have to be replaced by substitute units, selected from units not included in the initial sample and once again using the randomized systematic PPS sampling method. Under such a scenario the first order inclusion probabilities of the final selected units are very difficult to calculate and the second order inclusion probabilities become virtually intractable. In this paper we develop a simulation-based approach for computing the first and the second order inclusion probabilities when direct calculation is prohibitive or impossible. The efficiency and feasibility of the proposed approach are demonstrated through both theoretical considerations and numerical examples. Several R/S-PLUS functions and codes for the proposed procedure are included. The approach can be extended to handle more complex refusal/substitution scenarios one may encounter in practice.
Release date: 2008-06-26 - 8. A Bayesian allocation of undecided voters ArchivedArticles and reports: 12-001-X200800110606Description:
Data from election polls in the US are typically presented in two-way categorical tables, and there are many polls before the actual election in November. For example, in the Buckeye State Poll in 1998 for governor there are three polls, January, April and October; the first category represents the candidates (e.g., Fisher, Taft and other) and the second category represents the current status of the voters (likely to vote and not likely to vote for governor of Ohio). There is a substantial number of undecided voters for one or both categories in all three polls, and we use a Bayesian method to allocate the undecided voters to the three candidates. This method permits modeling different patterns of missingness under ignorable and nonignorable assumptions, and a multinomial-Dirichlet model is used to estimate the cell probabilities which can help to predict the winner. We propose a time-dependent nonignorable nonresponse model for the three tables. Here, a nonignorable nonresponse model is centered on an ignorable nonresponse model to induce some flexibility and uncertainty about ignorabilty or nonignorability. As competitors we also consider two other models, an ignorable and a nonignorable nonresponse model. These latter two models assume a common stochastic process to borrow strength over time. Markov chain Monte Carlo methods are used to fit the models. We also construct a parameter that can potentially be used to predict the winner among the candidates in the November election.
Release date: 2008-06-26 - 9. Generalized regression estimators of a finite population total using the Box-Cox technique ArchivedArticles and reports: 12-001-X200800110610Description:
A new generalized regression estimator of a finite population total based on the Box-Cox transformation technique and its variance estimator are proposed under a general unequal probability sampling design. By being design consistent, the proposed estimator maintains the robustness property of the GREG estimator even if the underlying model fails. Furthermore, the Box-Cox technique automatically finds a reasonable transformation for the dependent variable using the data. The robustness and efficiency of the new estimator are evaluated analytically and via Monte Carlo simulation studies.
Release date: 2008-06-26 - 10. A noninformative Bayesian approach to finite population sampling using auxiliary variables ArchivedArticles and reports: 12-001-X200800110611Description:
In finite population sampling prior information is often available in the form of partial knowledge about an auxiliary variable, for example its mean may be known. In such cases, the ratio estimator and the regression estimator are often used for estimating the population mean of the characteristic of interest. The Polya posterior has been developed as a noninformative Bayesian approach to survey sampling. It is appropriate when little or no prior information about the population is available. Here we show that it can be extended to incorporate types of partial prior information about auxiliary variables. We will see that it typically yields procedures with good frequentist properties even in some problems where standard frequentist methods are difficult to apply.
Release date: 2008-06-26
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