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- Articles 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
- 43. An investigation into the development and testing of a methodology for updating census indicatorsArchivedArticles 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
- 46. Inferences for finite populations using multiple data sources with different reference timesArchivedArticles 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
- Articles 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
- 49. Use of generalized variance function models in inference from social and economic survey dataArchivedArticles 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
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Analysis (69) (0 to 10 of 69 results)
- Articles and reports: 12-001-X201800254956Description:
In Italy, the Labor Force Survey (LFS) is conducted quarterly by the National Statistical Institute (ISTAT) to produce estimates of the labor force status of the population at different geographical levels. In particular, ISTAT provides LFS estimates of employed and unemployed counts for local Labor Market Areas (LMAs). LMAs are 611 sub-regional clusters of municipalities and are unplanned domains for which direct estimates have overly large sampling errors. This implies the need of Small Area Estimation (SAE) methods. In this paper, we develop a new area level SAE method that uses a Latent Markov Model (LMM) as linking model. In LMMs, the characteristic of interest, and its evolution in time, is represented by a latent process that follows a Markov chain, usually of first order. Therefore, areas are allowed to change their latent state across time. The proposed model is applied to quarterly data from the LFS for the period 2004 to 2014 and fitted within a hierarchical Bayesian framework using a data augmentation Gibbs sampler. Estimates are compared with those obtained by the classical Fay-Herriot model, by a time-series area level SAE model, and on the basis of data coming from the 2011 Population Census.Release date: 2018-12-20
- 2. Investigating alternative estimators for the prevalence of serious mental illness based on a two-phase sampleArticles and reports: 12-001-X201800154928Description:
A two-phase process was used by the Substance Abuse and Mental Health Services Administration to estimate the proportion of US adults with serious mental illness (SMI). The first phase was the annual National Survey on Drug Use and Health (NSDUH), while the second phase was a random subsample of adult respondents to the NSDUH. Respondents to the second phase of sampling were clinically evaluated for serious mental illness. A logistic prediction model was fit to this subsample with the SMI status (yes or no) determined by the second-phase instrument treated as the dependent variable and related variables collected on the NSDUH from all adults as the model’s explanatory variables. Estimates were then computed for SMI prevalence among all adults and within adult subpopulations by assigning an SMI status to each NSDUH respondent based on comparing his (her) estimated probability of having SMI to a chosen cut point on the distribution of the predicted probabilities. We investigate alternatives to this standard cut point estimator such as the probability estimator. The latter assigns an estimated probability of having SMI to each NSDUH respondent. The estimated prevalence of SMI is the weighted mean of those estimated probabilities. Using data from NSDUH and its subsample, we show that, although the probability estimator has a smaller mean squared error when estimating SMI prevalence among all adults, it has a greater tendency to be biased at the subpopulation level than the standard cut point estimator.Release date: 2018-06-21
- Articles and reports: 12-001-X201700254872Description:
This note discusses the theoretical foundations for the extension of the Wilson two-sided coverage interval to an estimated proportion computed from complex survey data. The interval is shown to be asymptotically equivalent to an interval derived from a logistic transformation. A mildly better version is discussed, but users may prefer constructing a one-sided interval already in the literature.Release date: 2017-12-21
- 4. Bayesian predictive inference of a proportion under a two-fold small area model with heterogeneous correlationsArticles and reports: 12-001-X201700114822Description:
We use a Bayesian method to infer about a finite population proportion when binary data are collected using a two-fold sample design from small areas. The two-fold sample design has a two-stage cluster sample design within each area. A former hierarchical Bayesian model assumes that for each area the first stage binary responses are independent Bernoulli distributions, and the probabilities have beta distributions which are parameterized by a mean and a correlation coefficient. The means vary with areas but the correlation is the same over areas. However, to gain some flexibility we have now extended this model to accommodate different correlations. The means and the correlations have independent beta distributions. We call the former model a homogeneous model and the new model a heterogeneous model. All hyperparameters have proper noninformative priors. An additional complexity is that some of the parameters are weakly identified making it difficult to use a standard Gibbs sampler for computation. So we have used unimodal constraints for the beta prior distributions and a blocked Gibbs sampler to perform the computation. We have compared the heterogeneous and homogeneous models using an illustrative example and simulation study. As expected, the two-fold model with heterogeneous correlations is preferred.Release date: 2017-06-22
- Articles and reports: 12-001-X201600214662Description:
Two-phase sampling designs are often used in surveys when the sampling frame contains little or no auxiliary information. In this note, we shed some light on the concept of invariance, which is often mentioned in the context of two-phase sampling designs. We define two types of invariant two-phase designs: strongly invariant and weakly invariant two-phase designs. Some examples are given. Finally, we describe the implications of strong and weak invariance from an inference point of view.Release date: 2016-12-20
- Articles and reports: 12-001-X201600114545Description:
The estimation of quantiles is an important topic not only in the regression framework, but also in sampling theory. A natural alternative or addition to quantiles are expectiles. Expectiles as a generalization of the mean have become popular during the last years as they not only give a more detailed picture of the data than the ordinary mean, but also can serve as a basis to calculate quantiles by using their close relationship. We show, how to estimate expectiles under sampling with unequal probabilities and how expectiles can be used to estimate the distribution function. The resulting fitted distribution function estimator can be inverted leading to quantile estimates. We run a simulation study to investigate and compare the efficiency of the expectile based estimator.Release date: 2016-06-22
- 7. Methodological Challenges in Official Statistics ArchivedArticles and reports: 11-522-X201700014704Description:
We identify several research areas and topics for methodological research in official statistics. We argue why these are important, and why these are the most important ones for official statistics. We describe the main topics in these research areas and sketch what seems to be the most promising ways to address them. Here we focus on: (i) Quality of National accounts, in particular the rate of growth of GNI (ii) Big data, in particular how to create representative estimates and how to make the most of big data when this is difficult or impossible. We also touch upon: (i) Increasing timeliness of preliminary and final statistical estimates (ii) Statistical analysis, in particular of complex and coherent phenomena. These topics are elements in the present Strategic Methodological Research Program that has recently been adopted at Statistics NetherlandsRelease date: 2016-03-24
- 8. Big Data: A Survey Research PerspectiveArchivedArticles and reports: 11-522-X201700014713Description:
Big data is a term that means different things to different people. To some, it means datasets so large that our traditional processing and analytic systems can no longer accommodate them. To others, it simply means taking advantage of existing datasets of all sizes and finding ways to merge them with the goal of generating new insights. The former view poses a number of important challenges to traditional market, opinion, and social research. In either case, there are implications for the future of surveys that are only beginning to be explored.Release date: 2016-03-24
- Articles and reports: 11-522-X201700014727Description:
"Probability samples of near-universal frames of households and persons, administered standardized measures, yielding long multivariate data records, and analyzed with statistical procedures reflecting the design – these have been the cornerstones of the empirical social sciences for 75 years. That measurement structure have given the developed world almost all of what we know about our societies and their economies. The stored survey data form a unique historical record. We live now in a different data world than that in which the leadership of statistical agencies and the social sciences were raised. High-dimensional data are ubiquitously being produced from Internet search activities, mobile Internet devices, social media, sensors, retail store scanners, and other devices. Some estimate that these data sources are increasing in size at the rate of 40% per year. Together their sizes swamp that of the probability-based sample surveys. Further, the state of sample surveys in the developed world is not healthy. Falling rates of survey participation are linked with ever-inflated costs of data collection. Despite growing needs for information, the creation of new survey vehicles is hampered by strained budgets for official statistical agencies and social science funders. These combined observations are unprecedented challenges for the basic paradigm of inference in the social and economic sciences. This paper discusses alternative ways forward at this moment in history. "Release date: 2016-03-24
- Articles and reports: 11-522-X201700014738Description:
In the standard design approach to missing observations, the construction of weight classes and calibration are used to adjust the design weights for the respondents in the sample. Here we use these adjusted weights to define a Dirichlet distribution which can be used to make inferences about the population. Examples show that the resulting procedures have better performance properties than the standard methods when the population is skewed.Release date: 2016-03-24
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Reference (16) (10 to 20 of 16 results)
- Surveys and statistical programs – Documentation: 11-522-X19990015650Description:
The U.S. Manufacturing Plant Ownership Change Database (OCD) was constructed using plant-level data taken from the Census Bureau's Longitudinal Research Database (LRD). It contains data on all manufacturing plants that have experienced ownership change at least once during the period 1963-92. This paper reports the status of the OCD and discuss its research possibilities. For an empirical demonstration, data taken from the database are used to study the effects of ownership changes on plant closure.Release date: 2000-03-02
- Surveys and statistical programs – Documentation: 11-522-X19990015658Description:
Radon, a naturally occurring gas found at some level in most homes, is an established risk factor for human lung cancer. The U.S. National Research Council (1999) has recently completed a comprehensive evaluation of the health risks of residential exposure to radon, and developed models for projecting radon lung cancer risks in the general population. This analysis suggests that radon may play a role in the etiology of 10-15% of all lung cancer cases in the United States, although these estimates are subject to considerable uncertainty. In this article, we present a partial analysis of uncertainty and variability in estimates of lung cancer risk due to residential exposure to radon in the United States using a general framework for the analysis of uncertainty and variability that we have developed previously. Specifically, we focus on estimates of the age-specific excess relative risk (ERR) and lifetime relative risk (LRR), both of which vary substantially among individuals.Release date: 2000-03-02
- Geographic files and documentation: 92F0138M1993001Geography: CanadaDescription:
The Geography Divisions of Statistics Canada and the U.S. Bureau of the Census have commenced a cooperative research program in order to foster an improved and expanded perspective on geographic areas and their relevance. One of the major objectives is to determine a common geographic area to form a geostatistical basis for cross-border research, analysis and mapping.
This report, which represents the first stage of the research, provides a list of comparable pairs of Canadian and U.S. standard geographic areas based on current definitions. Statistics Canada and the U.S. Bureau of the Census have two basic types of standard geographic entities: legislative/administrative areas (called "legal" entities in the U.S.) and statistical areas.
The preliminary pairing of geographic areas are based on face-value definitions only. The definitions are based on the June 4, 1991 Census of Population and Housing for Canada and the April 1, 1990 Census of Population and Housing for the U.S.A. The important aspect is the overall conceptual comparability, not the precise numerical thresholds used for delineating the areas.
Data users should use this report as a general guide to compare the census geographic areas of Canada and the United States, and should be aware that differences in settlement patterns and population levels preclude a precise one-to-one relationship between conceptually similar areas. The geographic areas compared in this report provide a framework for further empirical research and analysis.Release date: 1999-03-05
- 14. Inverse sampling design algorithmsArchivedSurveys 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
- 16. A transformation method for finite population sampling calibrated with empirical likelihoodArchivedSurveys 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