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- 41. Another look at the regression discontinuity design ArchivedArticles and reports: 11-522-X20020016745Description:
The attractiveness of the Regression Discontinuity Design (RDD) rests on its close similarity to a normal experimental design. On the other hand, it is of limited applicability since it is not often the case that units are assigned to the treatment group on the basis of an observable (to the analyst) pre-program measure. Besides, it only allows identification of the mean impact on a very specific subpopulation. In this technical paper, we show that the RDD straightforwardly generalizes to the instances in which the units' eligibility is established on an observable pre-program measure with eligible units allowed to freely self-select into the program. This set-up also proves to be very convenient for building a specification test on conventional non-experimental estimators of the program mean impact. The data requirements are clearly described.Release date: 2004-09-13
- 42. Use of generalized variance function models in inference from social and economic survey data ArchivedArticles and reports: 11-522-X20020016750Description:
Analyses of data from social and economic surveys sometimes use generalized variance function models to approximate the design variance of point estimators of population means and proportions. Analysts may use the resulting standard error estimates to compute associated confidence intervals or test statistics for the means and proportions of interest. In comparison with design-based variance estimators computed directly from survey microdata, generalized variance function models have several potential advantages, as will be discussed in this paper, including operational simplicity; increased stability of standard errors; and, for cases involving public-use datasets, reduction of disclosure limitation problems arising from the public release of stratum and cluster indicators.
These potential advantages, however, may be offset in part by several inferential issues. First, the properties of inferential statistics based on generalized variance functions (e.g., confidence interval coverage rates and widths) depend heavily on the relative empirical magnitudes of the components of variability associated, respectively, with:
(a) the random selection of a subset of items used in estimation of the generalized variance function model(b) the selection of sample units under a complex sample design (c) the lack of fit of the generalized variance function model (d) the generation of a finite population under a superpopulation model.
Second, under conditions, one may link each of components (a) through (d) with different empirical measures of the predictive adequacy of a generalized variance function model. Consequently, these measures of predictive adequacy can offer us some insight into the extent to which a given generalized variance function model may be appropriate for inferential use in specific applications.
Some of the proposed diagnostics are applied to data from the US Survey of Doctoral Recipients and the US Current Employment Survey. For the Survey of Doctoral Recipients, components (a), (c) and (d) are of principal concern. For the Current Employment Survey, components (b), (c) and (d) receive principal attention, and the availability of population microdata allow the development of especially detailed models for components (b) and (c).Release date: 2004-09-13
- Articles and reports: 12-001-X20030026785Description:
To avoid disclosures, one approach is to release partially synthetic, public use microdata sets. These comprise the units originally surveyed, but some collected values, for example sensitive values at high risk of disclosure or values of key identifiers, are replaced with multiple imputations. Although partially synthetic approaches are currently used to protect public use data, valid methods of inference have not been developed for them. This article presents such methods. They are based on the concepts of multiple imputation for missing data but use different rules for combining point and variance estimates. The combining rules also differ from those for fully synthetic data sets developed by Raghunathan, Reiter and Rubin (2003). The validity of these new rules is illustrated in simulation studies.Release date: 2004-01-27
- Articles and reports: 12-001-X20030016610Description:
In the presence of item nonreponse, unweighted imputation methods are often used in practice but they generally lead to biased estimators under uniform response within imputation classes. Following Skinner and Rao (2002), we propose a bias-adjusted estimator of a population mean under unweighted ratio imputation and random hot-deck imputation and derive linearization variance estimators. A small simulation study is conducted to study the performance of the methods in terms of bias and mean square error. Relative bias and relative stability of the variance estimators are also studied.Release date: 2003-07-31
- Articles and reports: 92F0138M2003002Description:
This working paper describes the preliminary 2006 census metropolitan areas and census agglomerations and is presented for user feedback. The paper briefly describes the factors that have resulted in changes to some of the census metropolitan areas and census agglomerations and includes tables and maps that list and illustrate these changes to their limits and to the component census subdivisions.Release date: 2003-07-11
- 46. Reviewing Census Metropolitan Areas (CMA) and Census Agglomerations (CA) in Canada According to Metropolitan Functionality ArchivedArticles and reports: 92F0138M2003001Description:
The goal of this working paper is to assess how well Canada's current method of delineating Census Metropolitan Areas (CMAs) and Census Agglomerations (CAs) reflects the metropolitan nature of these geographic areas according to the facilities and services they provide. The effectiveness of Canada's delineation methodology can be evaluated by applying a functional model to Statistics Canada's CMAs and CAs.
As a consequence of the research undertaken for this working paper, Statistics Canada has proposed lowering the urban core population threshold it uses to define CMAs: a CA will be promoted to a CMA if it has a total population of at least 100,000, of which 50,000 or more live in the urban core. User consultation on this proposal took place in the fall of 2002 as part of the 2006 Census content determination process.Release date: 2003-03-31
- 47. Alternative Work Practices and Quit Rates: Methodological Issues and Empirical Evidence for Canada ArchivedArticles and reports: 11F0019M2003199Geography: CanadaDescription:
Using a nationally representative sample of establishments, we have examined whether selected alternative work practices (AWPs) tend to reduce quit rates. Overall, our analysis provides strong evidence of a negative association between these AWPs and quit rates among establishments of more than 10 employees operating in high-skill services. We also found some evidence of a negative association in low-skill services. However, the magnitude of this negative association was reduced substantially when we added an indicator of whether the workplace has a formal policy of information sharing. There was very little evidence of a negative association in manufacturing. While establishments with self-directed workgroups have lower quit rates than others, none of the bundles of work practices considered yielded a negative and statistically significant effect. We surmise that key AWPs might be more successful in reducing labour turnover in technologically complex environments than in low-skill ones.Release date: 2003-03-17
- 48. A hierarchical Bayesian nonignorable nonresponse model for multinomial data from small areas ArchivedArticles and reports: 12-001-X20020026428Description:
The analysis of survey data from different geographical areas where the data from each area are polychotomous can be easily performed using hierarchical Bayesian models, even if there are small cell counts in some of these areas. However, there are difficulties when the survey data have missing information in the form of non-response, especially when the characteristics of the respondents differ from the non-respondents. We use the selection approach for estimation when there are non-respondents because it permits inference for all the parameters. Specifically, we describe a hierarchical Bayesian model to analyse multinomial non-ignorable non-response data from different geographical areas; some of them can be small. For the model, we use a Dirichlet prior density for the multinomial probabilities and a beta prior density for the response probabilities. This permits a 'borrowing of strength' of the data from larger areas to improve the reliability in the estimates of the model parameters corresponding to the smaller areas. Because the joint posterior density of all the parameters is complex, inference is sampling-based and Markov chain Monte Carlo methods are used. We apply our method to provide an analysis of body mass index (BMI) data from the third National Health and Nutrition Examination Survey (NHANES III). For simplicity, the BMI is categorized into 3 natural levels, and this is done for each of 8 age-race-sex domains and 34 counties. We assess the performance of our model using the NHANES III data and simulated examples, which show our model works reasonably well.Release date: 2003-01-29
- 49. The effect of record linkage errors on statistical inference in cohort mortality studies ArchivedArticles and reports: 11-522-X20010016277Description:
This paper discusses in detail issues dealing with the technical aspects of designing and conducting surveys. It is intended for an audience of survey methodologists.
The advent of computerized record-linkage methodology has facilitated the conduct of cohort mortality studies in which exposure data in one database are electronically linked with mortality data from another database. In this article, the impact of linkage errors on estimates of epidemiological indicators of risk, such as standardized mortality ratios and relative risk regression model parameters, is explored. It is shown that these indicators can be subject to bias and additional variability in the presence of linkage errors, with false links and non-links leading to positive and negative bias, respectively, in estimates of the standardized mortality ratio. Although linkage errors always increase the uncertainty in the estimates, bias can be effectively eliminated in the special case in which the false positive rate equals the false negative rate within homogeneous states defined by cross-classification of the covariates of interest.Release date: 2002-09-12
- 50. Schooling, Literacy and Individual Earnings ArchivedArticles and reports: 89-552-M2000007Geography: CanadaDescription:
This paper addresses the problem of statistical inference with ordinal variates and examines the robustness to alternative literacy measurement and scaling choices of rankings of average literacy and of estimates of the impact of literacy on individual earnings.Release date: 2000-06-02
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Analysis (69) (30 to 40 of 69 results)
- 31. Use of sparse-effect models to characterize bias-efficiency trade-offs in the analysis of complex survey data ArchivedArticles and reports: 11-522-X20030017700Description:
This paper suggests a useful framework for exploring the effects of moderate deviations from idealized conditions. It offers evaluation criteria for point estimators and interval estimators.Release date: 2005-01-26
- Articles and reports: 11-522-X20030017722Description:
This paper shows how to adapt design-based and model-based frameworks to the case of two-stage sampling.Release date: 2005-01-26
- 33. Comparison of design-based and model-based methods in analyzing complex health survey data: A case study ArchivedArticles and reports: 11-522-X20020016708Description:
In this paper, we discuss the analysis of complex health survey data by using multivariate modelling techniques. Main interests are in various design-based and model-based methods that aim at accounting for the design complexities, including clustering, stratification and weighting. Methods covered include generalized linear modelling based on pseudo-likelihood and generalized estimating equations, linear mixed models estimated by restricted maximum likelihood, and hierarchical Bayes techniques using Markov Chain Monte Carlo (MCMC) methods. The methods will be compared empirically, using data from an extensive health interview and examination survey conducted in Finland in 2000 (Health 2000 Study).
The data of the Health 2000 Study were collected using personal interviews, questionnaires and clinical examinations. A stratified two-stage cluster sampling design was used in the survey. The sampling design involved positive intra-cluster correlation for many study variables. For a closer investigation, we selected a small number of study variables from the health interview and health examination phases. In many cases, the different methods produced similar numerical results and supported similar statistical conclusions. Methods that failed to account for the design complexities sometimes led to conflicting conclusions. We also discuss the application of the methods in this paper by using standard statistical software products.Release date: 2004-09-13
- 34. Area-level models using data from multiple surveys ArchivedArticles and reports: 11-522-X20020016717Description:
In the United States, the National Health and Nutrition Examination Survey (NHANES) is linked to the National Health Interview Survey (NHIS) at the primary sampling unit level (the same counties, but not necessarily the same persons, are in both surveys). The NHANES examines about 5,000 persons per year, while the NHIS samples about 100,000 persons per year. In this paper, we present and develop properties of models that allow NHIS and administrative data to be used as auxiliary information for estimating quantities of interest in the NHANES. The methodology, related to Fay-Herriot (1979) small-area models and to calibration estimators in Deville and Sarndal (1992), accounts for the survey designs in the error structure.Release date: 2004-09-13
- Articles and reports: 11-522-X20020016719Description:
This study takes a look at the modelling methods used for public health data. Public health has a renewed interest in the impact of the environment on health. Ecological or contextual studies ideally investigate these relationships using public health data augmented with environmental characteristics in multilevel or hierarchical models. In these models, individual respondents in health data are the first level and community data are the second level. Most public health data use complex sample survey designs, which require analyses accounting for the clustering, nonresponse, and poststratification to obtain representative estimates of prevalence of health risk behaviours.
This study uses the Behavioral Risk Factor Surveillance System (BRFSS), a state-specific US health risk factor surveillance system conducted by the Center for Disease Control and Prevention, which assesses health risk factors in over 200,000 adults annually. BRFSS data are now available at the metropolitan statistical area (MSA) level and provide quality health information for studies of environmental effects. MSA-level analyses combining health and environmental data are further complicated by joint requirements of the survey sample design and the multilevel analyses.
We compare three modelling methods in a study of physical activity and selected environmental factors using BRFSS 2000 data. Each of the methods described here is a valid way to analyse complex sample survey data augmented with environmental information, although each accounts for the survey design and multilevel data structure in a different manner and is thus appropriate for slightly different research questions.Release date: 2004-09-13
- 36. An investigation into the development and testing of a methodology for updating census indicators ArchivedArticles and reports: 11-522-X20020016727Description:
The census data are widely used in the distribution and targeting of resources at national, regional and local levels. In the United Kingdom (UK), a population census is conducted every 10 years. As time elapses, the census data become outdated and less relevant, thus making the distribution of resources less equitable. This paper examines alternative methods in rectifying this.
A number of small area methods have been developed for producing postcensal estimates, including the Structural Preserving Estimation technique as a result of Purcell and Kish (1980). This paper develops an alternative approach that is based on a linear mixed modelling approach to producing postcensal estimates. The validity of the methodology is tested on simulated data from the Finnish population register and the technique is applied to producing updated estimates for a number of the 1991 UK census variables.Release date: 2004-09-13
- Articles and reports: 11-522-X20020016730Description:
A wide class of models of interest in social and economic research can be represented by specifying a parametric structure for the covariances of observed variables. The availability of software, such as LISREL (Jöreskog and Sörbom 1988) and EQS (Bentler 1995), has enabled these models to be fitted to survey data in many applications. In this paper, we consider approaches to inference about such models using survey data derived by complex sampling schemes. We consider evidence of finite sample biases in parameter estimation and ways to reduce such biases (Altonji and Segal 1996) and associated issues of efficiency of estimation, standard error estimation and testing. We use longitudinal data from the British Household Panel Survey for illustration. As these data are subject to attrition, we also consider the issue of how to use nonresponse weights in the modelling.Release date: 2004-09-13
- Articles and reports: 11-522-X20020016731Description:
Behavioural researchers use a variety of techniques to predict respondent scores on constructs that are not directly observable. Examples of such constructs include job satisfaction, work stress, aptitude for graduate study, children's mathematical ability, etc. The techniques commonly used for modelling and predicting scores on such constructs include factor analysis, classical psychometric scaling and item response theory (IRT), and for each technique there are often several different strategies that can be used to generate individual scores. However, researchers are seldom satisfied with simply measuring these constructs. They typically use the derived scores in multiple regression, analysis of variance and numerous multivariate procedures. Though using predicted scores in this way can result in biased estimates of model parameters, not all researchers are aware of this difficulty. The paper will review the literature on this issue, with particular emphasis on IRT methods. Problems will be illustrated, some remedies suggested, and areas for further research will be identified.Release date: 2004-09-13
- 39. Inferences for finite populations using multiple data sources with different reference times ArchivedArticles and reports: 11-522-X20020016733Description:
While censuses and surveys are often said to measure populations as they are, most reflect information about individuals as they were at the time of measurement, or even at some prior time point. Inferences from such data therefore should take into account change over time at both the population and individual levels. In this paper, we provide a unifying framework for such inference problems, illustrating it through a diverse series of examples including: (1) estimating residency status on Census Day using multiple administrative records, (2) combining administrative records for estimating the size of the US population, (3) using rolling averages from the American Community Survey, and (4) estimating the prevalence of human rights abuses.
Specifically, at the population level, the estimands of interest, such as the size or mean characteristics of a population, might be changing. At the same time, individual subjects might be moving in and out of the frame of the study or changing their characteristics. Such changes over time can affect statistical studies of government data that combine information from multiple data sources, including censuses, surveys and administrative records, an increasingly common practice. Inferences from the resulting merged databases often depend heavily on specific choices made in combining, editing and analysing the data that reflect assumptions about how populations of interest change or remain stable over time.Release date: 2004-09-13
- Articles and reports: 11-522-X20020016743Description:
There is much interest in using data from longitudinal surveys to help understand life history processes such as education, employment, fertility, health and marriage. The analysis of data on the durations of spells or sojourns that individuals spend in certain states (e.g., employment, marriage) is a primary tool in studying such processes. This paper examines methods for analysing duration data that address important features associated with longitudinal surveys: the use of complex survey designs in heterogeneous populations; missing or inaccurate information about the timing of events; and the possibility of non-ignorable dropout or censoring mechanisms. Parametric and non-parametric techniques for estimation and for model checking are considered. Both new and existing methodology are proposed and applied to duration data from Canada's Survey of Labour and Income Dynamics (SLID).Release date: 2004-09-13
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Reference (3) ((3 results))
- 1. Inverse sampling design algorithms ArchivedSurveys and statistical programs – Documentation: 12-001-X19970013101Description:
In the main body of statistics, sampling is often disposed of by assuming a sampling process that selects random variables such that they are independent and identically distributed (IID). Important techniques, like regression and contingency table analysis, were developed largely in the IID world; hence, adjustments are needed to use them in complex survey settings. Rather than adjust the analysis, however, what is new in the present formulation is to draw a second sample from the original sample. In this second sample, the first set of selections are inverted, so as to yield at the end a simple random sample. Of course, to employ this two-step process to draw a single simple random sample from the usually much larger complex survey would be inefficient, so multiple simple random samples are drawn and a way to base inferences on them developed. Not all original samples can be inverted; but many practical special cases are discussed which cover a wide range of practices.Release date: 1997-08-18
- Surveys and statistical programs – Documentation: 12-001-X19970013102Description:
The selection of auxiliary variables is considered for regression estimation in finite populations under a simple random sampling design. This problem is a basic one for model-based and model-assisted survey sampling approaches and is of practical importance when the number of variables available is large. An approach is developed in which a mean squared error estimator is minimised. This approach is compared to alternative approaches using a fixed set of auxiliary variables, a conventional significance test criterion, a condition number reduction approach and a ridge regression approach. The proposed approach is found to perform well in terms of efficiency. It is noted that the variable selection approach affects the properties of standard variance estimators and thus leads to a problem of variance estimation.Release date: 1997-08-18
- 3. A transformation method for finite population sampling calibrated with empirical likelihood ArchivedSurveys and statistical programs – Documentation: 12-001-X19960022980Description:
In this paper, we study a confidence interval estimation method for a finite population average when some auxiliairy information is available. As demonstrated by Royall and Cumberland in a series of empirical studies, naive use of existing methods to construct confidence intervals for population averages may result in very poor conditional coverage probabilities, conditional on the sample mean of the covariate. When this happens, we propose to transform the data to improve the precision of the normal approximation. The transformed data are then used to make inference on the original population average, and the auxiliary information is incorporated into the inference directly, or by calibration with empirical likelihood. Our approach is design-based. We apply our approach to six real populations and find that when transformation is needed, our approach performs well compared to the usual regression method.Release date: 1997-01-30