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All (92) (50 to 60 of 92 results)

51. Comparing a rate in a subpopulation to the rate in the full population: How it may be done when using survey data, and available software tools Archived
Articles and reports: 12-002-X20050018030
Description:
People often wish to use survey micro-data to study whether the rate of occurrence of a particular condition in a subpopulation is the same as the rate of occurrence in the full population. This paper describes some alternatives for making inferences about such a rate difference and shows whether and how these alternatives may be implemented in three different survey software packages. The software packages illustrated - SUDAAN, WesVar and Bootvar - all can make use of bootstrap weights provided by the analyst to carry out variance estimation.
Release date: 2005-06-23
52. Penalized spline nonparametric mixed models for inference about a finite population mean from two-stage samples Archived
Articles and reports: 12-001-X20040027753
Description:
Samplers often distrust model-based approaches to survey inference because of concerns about misspecification when models are applied to large samples from complex populations. We suggest that the model-based paradigm can work very successfully in survey settings, provided models are chosen that take into account the sample design and avoid strong parametric assumptions. The Horvitz-Thompson (HT) estimator is a simple design-unbiased estimator of the finite population total. From a modeling perspective, the HT estimator performs well when the ratios of the outcome values and the inclusion probabilities are exchangeable. When this assumption is not met, the HT estimator can be very inefficient. In Zheng and Little (2003, 2004) we used penalized splines (p-splines) to model smoothly - varying relationships between the outcome and the inclusion probabilities in one-stage probability proportional to size (PPS) samples. We showed that p spline model-based estimators are in general more efficient than the HT estimator, and can provide narrower confidence intervals with close to nominal confidence coverage. In this article, we extend this approach to two-stage sampling designs. We use a p-spline based mixed model that fits a nonparametric relationship between the primary sampling unit (PSU) means and a measure of PSU size, and incorporates random effects to model clustering. For variance estimation we consider the empirical Bayes model-based variance, the jackknife and balanced repeated replication (BRR) methods. Simulation studies on simulated data and samples drawn from public use microdata in the 1990 census demonstrate gains for the model-based p-spline estimator over the HT estimator and linear model-assisted estimators. Simulations also show the variance estimation methods yield confidence intervals with satisfactory confidence coverage. Interestingly, these gains can be seen for a common equal-probability design, where the first stage selection is PPS and the second stage selection probabilities are proportional to the inverse of the first stage inclusion probabilities, and the HT estimator leads to the unweighted mean. In situations that most favor the HT estimator, the model-based estimators have comparable efficiency.
Release date: 2005-02-03
53. Use of sparse-effect models to characterize bias-efficiency trade-offs in the analysis of complex survey data Archived
Articles and reports: 11-522-X20030017700
Description:
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
54. Inference for totals in cluster sampling under mean imputation for missing data Archived
Articles and reports: 11-522-X20030017722
Description:
This paper shows how to adapt design-based and model-based frameworks to the case of two-stage sampling.
Release date: 2005-01-26
55. Comparison of design-based and model-based methods in analyzing complex health survey data: A case study Archived
Articles and reports: 11-522-X20020016708
Description:
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
56. Area-level models using data from multiple surveys Archived
Articles and reports: 11-522-X20020016717
Description:
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
57. A comparison of approaches to modelling health and environment Archived
Articles and reports: 11-522-X20020016719
Description:
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
58. An investigation into the development and testing of a methodology for updating census indicators Archived
Articles and reports: 11-522-X20020016727
Description:
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
59. Covariance structure modelling with complex survey data Archived
Articles and reports: 11-522-X20020016730
Description:
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
60. Using IRT and factor scores in regression and other analyses: A review Archived
Articles and reports: 11-522-X20020016731
Description:
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

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Analysis (92)

Analysis (92) (70 to 80 of 92 results)

71. The effect of record linkage errors on statistical inference in cohort mortality studies Archived
Articles and reports: 11-522-X20010016277
Description:
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
72. Schooling, Literacy and Individual Earnings Archived
Articles and reports: 89-552-M2000007
Geography: Canada
Description:
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
73. Meta analysis of bioassay data from the U.S. national toxicology program Archived
Articles and reports: 11-522-X19990015654
Description:
A meta analysis was performed to estimate the proportion of liver carcinogens, the proportion of chemicals carcinogenic at any site, and the corresponding proportion of anticarcinogens among chemicals tested in 397 long-term cancer bioassays conducted by the U.S. National Toxicology Program. Although the estimator used was negatively biased, the study provided persuasive evidence for a larger proportion of liver carcinogens (0.43,90%CI: 0.35,0.51) than was identified by the NTP (0.28). A larger proportion of chemicals carcinogenic at any site was also estimated (0.59,90%CI: 0.49,0.69) than was identified by the NTP (0.51), although this excess was not statistically significant. A larger proportion of anticarcinogens (0.66) was estimated than carcinogens (0.59). Despite the negative bias, it was estimated that 85% of the chemicals were either carcinogenic or anticarcinogenic at some site in some sex-species group. This suggests that most chemicals tested at high enough doses will cause some sort of perturbation in tumor rates.
Release date: 2000-03-02
74. Delineation of Canada's North: An Examination of the North-South Relationship in Canada Archived
Articles and reports: 92F0138M2000003
Description:
Statistics Canada's interest in a common delineation of the north for statistical analysis purposes evolved from research to devise a classification to further differentiate the largely rural and remote areas that make up 96% of Canada's land area. That research led to the establishment of the census metropolitan area and census agglomeration influenced zone (MIZ) concept. When applied to census subdivisions, the MIZ categories did not work as well in northern areas as in the south. Therefore, the Geography Division set out to determine a north-south divide that would differentiate the north from the south independent of any standard geographic area boundaries.
This working paper describes the methodology used to define a continuous line across Canada to separate the north from the south, as well as lines marking transition zones on both sides of the north-south line. It also describes the indicators selected to derive the north-south line and makes comparisons to alternative definitions of the north. The resulting classification of the north complements the MIZ classification. Together, census metropolitan areas, census agglomerations, MIZ and the North form a new Statistical Area Classification (SAC) for Canada.
Two related Geography working papers (catalogue no. 92F0138MPE) provide further details about the MIZ classification. Working paper no. 2000-1 (92F0138MPE00001) briefly describes MIZ and includes tables of selected socio-economic characteristics from the 1991 Census tabulated by the MIZ categories, and working paper no. 2000-2 (92F0138MPE00002) describes the methodology used to define the MIZ classification.
Release date: 2000-02-03
75. Comparative Study of Analytical Consumer Price Indexes for Different Subgroups of the Reference Population Archived
Articles and reports: 62F0014M1998013
Geography: Canada
Description:
The reference population for the Consumer Price Index (CPI) has been represented, since the 1992 updating of the basket of goods and services, by families and unattached individuals living in private urban or rural households. The official CPI is a measure of the average percentage change over time in the cost of a fixed basket of goods and services purchased by Canadian consumers.
Because of the broadly defined target population of the CPI, the measure has been criticised for failing to reflect the inflationary experiences of certain socio-economic groups. This study examines this question for three sub-groups of the reference population of the CPI. It is an extension of earlier studies on the subject done at Statistics Canada.
In this document, analytical consumer price indexes sub-group indexes are compared to the analytical index for the whole population calculated at the national geographic level.
The findings tend to point to those of earlier Statistics Canada studies on sub-groups in the CPI reference population. Those studies have consistently concluded that a consumer price index established for a given sub-group does not differ substantially from the index for the whole reference population.
Release date: 1999-05-13
76. Inverse sampling design algorithms Archived
Articles and reports: 12-001-X19970013101
Description:
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
77. Variable selection for regression estimation in finite populations Archived
Articles and reports: 12-001-X19970013102
Description:
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
78. A transformation method for finite population sampling calibrated with empirical likelihood Archived
Articles and reports: 12-001-X19960022980
Description:
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
79. Fertility Projections for Canada, Provinces and Territories, 1993-2016 Archived
Articles and reports: 91F0015M1996001
Geography: Canada
Description:
This paper describes the methodology for fertility projections used in the 1993-based population projections by age and sex for Canada, provinces and territories, 1993-2016. A new version of the parametric model known as the Pearsonian Type III curve was applied for projecting fertility age pattern. The Pearsonian Type III model is considered as an improvement over the Type I used in the past projections. This is because the Type III curve better portrays both the distribution of the age-specific fertility rates and the estimates of births. Since the 1993-based population projections are the first official projections to incorporate the net census undercoverage in the population base, it has been necessary to recalculate fertility rates based on the adjusted population estimates. This recalculation resulted in lowering the historical series of age-specific and total fertility rates, 1971-1993. The three sets of fertility assumptions and projections were developed with these adjusted annual fertility rates.
It is hoped that this paper will provide valuable information about the technical and analytical aspects of the current fertility projection model. Discussions on the current and future levels and age pattern of fertility in Canada, provinces and territories are also presented in the paper.
Release date: 1996-08-02
80. Multiple sample estimation of population and census undercount in the presence of matching errors Archived
Articles and reports: 12-001-X199600114385
Description:
The multiple capture-recapture census is reconsidered by relaxing the traditional perfect matching assumption. We propose matching error models to characterize error-prone matching mechanisms. The observed data take the form of an incomplete 2^k contingency table with one missing cell and follow a multinomial distribution. We develop a procedure for the estimation of the population size. Our approach applies to both standard log-linear models for contingency tables and log-linear models for heterogeneity of catchability. We illustrate the method and estimation using a 1988 dress rehearsal study for the 1990 census conducted by the U.S. Bureau of the Census.
Release date: 1996-06-14

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Date modified:: 2024-06-08