Analysis

The paper explores and assesses the approaches used by statistical offices to ensure effective methodological input into their statistical practice. The tension between independence and relevance is a common theme: generally, methodologists have to work closely with the rest of the statistical organisation for their work to be relevant; but they also need to have a degree of independence to question the use of existing methods and to lead the introduction of new ones where needed. And, of course, there is a need for an effective research program which, on the one hand, has a degree of independence needed by any research program, but which, on the other hand, is sufficiently connected so that its work is both motivated by and feeds back into the daily work of the statistical office. The paper explores alternative modalities of organisation; leadership; planning and funding; the role of project teams; career development; external advisory committees; interaction with the academic community; and research.

This article develops computational tools, called indicators, for judging the effectiveness of the auxiliary information used to control nonresponse bias in survey estimates, obtained in this article by calibration. This work is motivated by the survey environment in a number of countries, notably in northern Europe, where many potential auxiliary variables are derived from reliable administrative registers for household and individuals. Many auxiliary vectors can be composed. There is a need to compare these vectors to assess their potential for reducing bias. The indicators in this article are designed to meet that need. They are used in surveys at Statistics Sweden. General survey conditions are considered: There is probability sampling from the finite population, by an arbitrary sampling design; nonresponse occurs. The probability of inclusion in the sample is known for each population unit; the probability of response is unknown, causing bias. The study variable (the y-variable) is observed for the set of respondents only. No matter what auxiliary vector is used in a calibration estimator (or in any other estimation method), a residual bias will always remain. The choice of a "best possible" auxiliary vector is guided by the indicators proposed in the article. Their background and computational features are described in the early sections of the article. Their theoretical background is explained. The concluding sections are devoted to empirical studies. One of these illustrates the selection of auxiliary variables in a survey at Statistics Sweden. A second empirical illustration is a simulation with a constructed finite population; a number of potential auxiliary vectors are ranked in order of preference with the aid of the indicators.

We consider the problem of parameter estimation with auxiliary information, where the auxiliary information takes the form of known moments. Calibration estimation is a typical example of using the moment conditions in sample surveys. Given the parametric form of the original distribution of the sample observations, we use the estimated importance sampling of Henmi, Yoshida and Eguchi (2007) to obtain an improved estimator. If we use the normal density to compute the importance weights, the resulting estimator takes the form of the one-step exponential tilting estimator. The proposed exponential tilting estimator is shown to be asymptotically equivalent to the regression estimator, but it avoids extreme weights and has some computational advantages over the empirical likelihood estimator. Variance estimation is also discussed and results from a limited simulation study are presented.

One key to poverty alleviation or eradication in the third world is reliable information on the poor and their location, so that interventions and assistance can be effectively targeted to the neediest people. Small area estimation is one statistical technique that is used to monitor poverty and to decide on aid allocation in pursuit of the Millennium Development Goals. Elbers, Lanjouw and Lanjouw (ELL) (2003) proposed a small area estimation methodology for income-based or expenditure-based poverty measures, which is implemented by the World Bank in its poverty mapping projects via the involvement of the central statistical agencies in many third world countries, including Cambodia, Lao PDR, the Philippines, Thailand and Vietnam, and is incorporated into the World Bank software program PovMap. In this paper, the ELL methodology which consists of first modeling survey data and then applying that model to census information is presented and discussed with strong emphasis on the first phase, i.e., the fitting of regression models and on the estimated standard errors at the second phase. Other regression model fitting procedures such as the General Survey Regression (GSR) (as described in Lohr (1999) Chapter 11) and those used in existing small area estimation techniques: Pseudo-Empirical Best Linear Unbiased Prediction (Pseudo-EBLUP) approach (You and Rao 2002) and Iterative Weighted Estimating Equation (IWEE) method (You, Rao and Kovacevic 2003) are presented and compared with the ELL modeling strategy. The most significant difference between the ELL method and the other techniques is in the theoretical underpinning of the ELL model fitting procedure. An example based on the Philippines Family Income and Expenditure Survey is presented to show the differences in both the parameter estimates and their corresponding standard errors, and in the variance components generated from the different methods and the discussion is extended to the effect of these on the estimated accuracy of the final small area estimates themselves. The need for sound estimation of variance components, as well as regression estimates and estimates of their standard errors for small area estimation of poverty is emphasized.

The number of people recruited by firms in Local Labour Market Areas provides an important indicator of the reorganisation of the local productive processes. In Italy, this parameter can be estimated using the information collected in the Excelsior survey, although it does not provide reliable estimates for the domains of interest. In this paper we propose a multivariate small area estimation approach for count data based on the Multivariate Poisson-Log Normal distribution. This approach will be used to estimate the number of firm recruits both replacing departing employees and filling new positions. In the small area estimation framework, it is customary to assume that sampling variances and covariances are known. However, both they and the direct point estimates suffer from instability. Due to the rare nature of the phenomenon we are analysing, counts in some domains are equal to zero, and this produces estimates of sampling error covariances equal to zero. To account for the extra variability due to the estimated sampling covariance matrix, and to deal with the problem of unreasonable estimated variances and covariances in some domains, we propose an "integrated" approach where we jointly model the parameters of interest and the sampling error covariance matrices. We suggest a solution based again on the Poisson-Log Normal distribution to smooth variances and covariances. The results we obtain are encouraging: the proposed small area estimation model shows a better fit when compared to the Multivariate Normal-Normal (MNN) small area model, and it allows for a non-negligible increase in efficiency.

Alternative forms of linearization variance estimators for generalized raking estimators are defined via different choices of the weights applied (a) to residuals and (b) to the estimated regression coefficients used in calculating the residuals. Some theory is presented for three forms of generalized raking estimator, the classical raking ratio estimator, the 'maximum likelihood' raking estimator and the generalized regression estimator, and for associated linearization variance estimators. A simulation study is undertaken, based upon a labour force survey and an income and expenditure survey. Properties of the estimators are assessed with respect to both sampling and nonresponse. The study displays little difference between the properties of the alternative raking estimators for a given sampling scheme and nonresponse model. Amongst the variance estimators, the approach which weights residuals by the design weight can be severely biased in the presence of nonresponse. The approach which weights residuals by the calibrated weight tends to display much less bias. Varying the choice of the weights used to construct the regression coefficients has little impact.

Taylor linearization methods are often used to obtain variance estimators for calibration estimators of totals and nonlinear finite population (or census) parameters, such as ratios, regression and correlation coefficients, which can be expressed as smooth functions of totals. Taylor linearization is generally applicable to any sampling design, but it can lead to multiple variance estimators that are asymptotically design unbiased under repeated sampling. The choice among the variance estimators requires other considerations such as (i) approximate unbiasedness for the model variance of the estimator under an assumed model, and (ii) validity under a conditional repeated sampling framework. Demnati and Rao (2004) proposed a unified approach to deriving Taylor linearization variance estimators that leads directly to a unique variance estimator that satisfies the above considerations for general designs. When analyzing survey data, finite populations are often assumed to be generated from super-population models, and analytical inferences on model parameters are of interest. If the sampling fractions are small, then the sampling variance captures almost the entire variation generated by the design and model random processes. However, when the sampling fractions are not negligible, the model variance should be taken into account in order to construct valid inferences on model parameters under the combined process of generating the finite population from the assumed super-population model and the selection of the sample according to the specified sampling design. In this paper, we obtain an estimator of the total variance, using the Demnati-Rao approach, when the characteristics of interest are assumed to be random variables generated from a super-population model. We illustrate the method using ratio estimators and estimators defined as solutions to calibration weighted estimating equations. Simulation results on the performance of the proposed variance estimator for model parameters are also presented.

The size of the cell-phone-only population in the USA has increased rapidly in recent years and, correspondingly, researchers have begun to experiment with sampling and interviewing of cell-phone subscribers. We discuss statistical issues involved in the sampling design and estimation phases of cell-phone studies. This work is presented primarily in the context of a nonoverlapping dual-frame survey in which one frame and sample are employed for the landline population and a second frame and sample are employed for the cell-phone-only population. Additional considerations necessary for overlapping dual-frame surveys (where the cell-phone frame and sample include some of the landline population) are also discussed. We illustrate the methods using the design of the National Immunization Survey (NIS), which monitors the vaccination rates of children age 19-35 months and teens age 13-17 years. The NIS is a nationwide telephone survey, followed by a provider record check, conducted by the Centers for Disease Control and Prevention.

Data collection for poverty assessments in Africa is time consuming, expensive and can be subject to numerous constraints. In this paper we present a procedure to collect data from poor households involved in small-scale inland fisheries as well as agricultural activities. A sampling scheme has been developed that captures the heterogeneity in ecological conditions and the seasonality of livelihood options. Sampling includes a three point panel survey of 300 households. The respondents belong to four different ethnic groups randomly chosen from three strata, each representing a different ecological zone. In the first part of the paper some background information is given on the objectives of the research, the study site and survey design, which were guiding the data collection process. The second part of the paper discusses the typical constraints that are hampering empirical work in Sub-Saharan Africa, and shows how different challenges have been resolved. These lessons could guide researchers in designing appropriate socio-economic surveys in comparable settings.

The current economic downturn in the US could challenge costly strategies in survey operations. In the Behavioral Risk Factor Surveillance System (BRFSS), ending the monthly data collection at 31 days could be a less costly alternative. However, this could potentially exclude a portion of interviews completed after 31 days (late responders) whose respondent characteristics could be different in many respects from those who completed the survey within 31 days (early responders). We examined whether there are differences between the early and late responders in demographics, health-care coverage, general health status, health risk behaviors, and chronic disease conditions or illnesses. We used 2007 BRFSS data, where a representative sample of the noninstitutionalized adult U.S. population was selected using a random digit dialing method. Late responders were significantly more likely to be male; to report race/ethnicity as Hispanic; to have annual income higher than $50,000; to be younger than 45 years of age; to have less than high school education; to have health-care coverage; to be significantly more likely to report good health; and to be significantly less likely to report hypertension, diabetes, or being obese. The observed differences between early and late responders on survey estimates may hardly influence national and state-level estimates. As the proportion of late responders may increase in the future, its impact on surveillance estimates should be examined before excluding from the analysis. Analysis on late responders only should combine several years of data to produce reliable estimates.

The negative health consequences of stress include high blood pressure and immune and circulatory complications. In addition, exposure to stress can contribute to behaviours such as smoking, over consumption of alcohol, and less healthy eating habits.

The health benefits of physical activity include a reduced risk of cardiovascular disease, some types of cancer, osteoporosis, diabetes, obesity, high blood pressure, depression, stress, and anxiety.

Smoking is a risk factor for lung cancer, heart disease, stroke, chronic respiratory disease, and other conditions. Smoking is an important and preventable cause of death.

This release contains estimates of total spending on research and development (R&D) in the health field in Canada. Tables demonstrate expenditures on health R&D by both performer and funder from 2005 to 2009 preliminary estimates.

About two in five Canadian adults have a high level of total cholesterol. Older adults are more likely to have higher cholesterol levels than younger adults.

Most Canadians have vitamin D levels adequate for bone health. Women are more likely than men to have adequate vitamin D levels.

Data for fiscal year 2008/2009 comprise science and technology (S&T) expenditures and full-time equivalent personnel allocated to biotechnology research and development (R&D) and related scientific activities (RSA) in selected federal government departments and agencies.

This Social Fact Sheet provides data on demographics, health, education and religion for women and men.

This Economic Fact Sheet provides data on the labour market, hourly wages, pension coverage and registered retirement savings plan (RRSPs) for women and men.

Canada's economic competitiveness depends on scientific and technological development and also on the people responsible for this development, especially those engaged in R&D. In an earlier Science statistics bulletin, we published the gross domestic expenditures on R&D in Canada (GERD). This issue presents a supplementary measure to the GERD, the number of personnel who perform Canada's R&D activities.

The paper explores and assesses the approaches used by statistical offices to ensure effective methodological input into their statistical practice. The tension between independence and relevance is a common theme: generally, methodologists have to work closely with the rest of the statistical organisation for their work to be relevant; but they also need to have a degree of independence to question the use of existing methods and to lead the introduction of new ones where needed. And, of course, there is a need for an effective research program which, on the one hand, has a degree of independence needed by any research program, but which, on the other hand, is sufficiently connected so that its work is both motivated by and feeds back into the daily work of the statistical office. The paper explores alternative modalities of organisation; leadership; planning and funding; the role of project teams; career development; external advisory committees; interaction with the academic community; and research.

This article develops computational tools, called indicators, for judging the effectiveness of the auxiliary information used to control nonresponse bias in survey estimates, obtained in this article by calibration. This work is motivated by the survey environment in a number of countries, notably in northern Europe, where many potential auxiliary variables are derived from reliable administrative registers for household and individuals. Many auxiliary vectors can be composed. There is a need to compare these vectors to assess their potential for reducing bias. The indicators in this article are designed to meet that need. They are used in surveys at Statistics Sweden. General survey conditions are considered: There is probability sampling from the finite population, by an arbitrary sampling design; nonresponse occurs. The probability of inclusion in the sample is known for each population unit; the probability of response is unknown, causing bias. The study variable (the y-variable) is observed for the set of respondents only. No matter what auxiliary vector is used in a calibration estimator (or in any other estimation method), a residual bias will always remain. The choice of a "best possible" auxiliary vector is guided by the indicators proposed in the article. Their background and computational features are described in the early sections of the article. Their theoretical background is explained. The concluding sections are devoted to empirical studies. One of these illustrates the selection of auxiliary variables in a survey at Statistics Sweden. A second empirical illustration is a simulation with a constructed finite population; a number of potential auxiliary vectors are ranked in order of preference with the aid of the indicators.

We consider the problem of parameter estimation with auxiliary information, where the auxiliary information takes the form of known moments. Calibration estimation is a typical example of using the moment conditions in sample surveys. Given the parametric form of the original distribution of the sample observations, we use the estimated importance sampling of Henmi, Yoshida and Eguchi (2007) to obtain an improved estimator. If we use the normal density to compute the importance weights, the resulting estimator takes the form of the one-step exponential tilting estimator. The proposed exponential tilting estimator is shown to be asymptotically equivalent to the regression estimator, but it avoids extreme weights and has some computational advantages over the empirical likelihood estimator. Variance estimation is also discussed and results from a limited simulation study are presented.

One key to poverty alleviation or eradication in the third world is reliable information on the poor and their location, so that interventions and assistance can be effectively targeted to the neediest people. Small area estimation is one statistical technique that is used to monitor poverty and to decide on aid allocation in pursuit of the Millennium Development Goals. Elbers, Lanjouw and Lanjouw (ELL) (2003) proposed a small area estimation methodology for income-based or expenditure-based poverty measures, which is implemented by the World Bank in its poverty mapping projects via the involvement of the central statistical agencies in many third world countries, including Cambodia, Lao PDR, the Philippines, Thailand and Vietnam, and is incorporated into the World Bank software program PovMap. In this paper, the ELL methodology which consists of first modeling survey data and then applying that model to census information is presented and discussed with strong emphasis on the first phase, i.e., the fitting of regression models and on the estimated standard errors at the second phase. Other regression model fitting procedures such as the General Survey Regression (GSR) (as described in Lohr (1999) Chapter 11) and those used in existing small area estimation techniques: Pseudo-Empirical Best Linear Unbiased Prediction (Pseudo-EBLUP) approach (You and Rao 2002) and Iterative Weighted Estimating Equation (IWEE) method (You, Rao and Kovacevic 2003) are presented and compared with the ELL modeling strategy. The most significant difference between the ELL method and the other techniques is in the theoretical underpinning of the ELL model fitting procedure. An example based on the Philippines Family Income and Expenditure Survey is presented to show the differences in both the parameter estimates and their corresponding standard errors, and in the variance components generated from the different methods and the discussion is extended to the effect of these on the estimated accuracy of the final small area estimates themselves. The need for sound estimation of variance components, as well as regression estimates and estimates of their standard errors for small area estimation of poverty is emphasized.

The number of people recruited by firms in Local Labour Market Areas provides an important indicator of the reorganisation of the local productive processes. In Italy, this parameter can be estimated using the information collected in the Excelsior survey, although it does not provide reliable estimates for the domains of interest. In this paper we propose a multivariate small area estimation approach for count data based on the Multivariate Poisson-Log Normal distribution. This approach will be used to estimate the number of firm recruits both replacing departing employees and filling new positions. In the small area estimation framework, it is customary to assume that sampling variances and covariances are known. However, both they and the direct point estimates suffer from instability. Due to the rare nature of the phenomenon we are analysing, counts in some domains are equal to zero, and this produces estimates of sampling error covariances equal to zero. To account for the extra variability due to the estimated sampling covariance matrix, and to deal with the problem of unreasonable estimated variances and covariances in some domains, we propose an "integrated" approach where we jointly model the parameters of interest and the sampling error covariance matrices. We suggest a solution based again on the Poisson-Log Normal distribution to smooth variances and covariances. The results we obtain are encouraging: the proposed small area estimation model shows a better fit when compared to the Multivariate Normal-Normal (MNN) small area model, and it allows for a non-negligible increase in efficiency.

Alternative forms of linearization variance estimators for generalized raking estimators are defined via different choices of the weights applied (a) to residuals and (b) to the estimated regression coefficients used in calculating the residuals. Some theory is presented for three forms of generalized raking estimator, the classical raking ratio estimator, the 'maximum likelihood' raking estimator and the generalized regression estimator, and for associated linearization variance estimators. A simulation study is undertaken, based upon a labour force survey and an income and expenditure survey. Properties of the estimators are assessed with respect to both sampling and nonresponse. The study displays little difference between the properties of the alternative raking estimators for a given sampling scheme and nonresponse model. Amongst the variance estimators, the approach which weights residuals by the design weight can be severely biased in the presence of nonresponse. The approach which weights residuals by the calibrated weight tends to display much less bias. Varying the choice of the weights used to construct the regression coefficients has little impact.

Taylor linearization methods are often used to obtain variance estimators for calibration estimators of totals and nonlinear finite population (or census) parameters, such as ratios, regression and correlation coefficients, which can be expressed as smooth functions of totals. Taylor linearization is generally applicable to any sampling design, but it can lead to multiple variance estimators that are asymptotically design unbiased under repeated sampling. The choice among the variance estimators requires other considerations such as (i) approximate unbiasedness for the model variance of the estimator under an assumed model, and (ii) validity under a conditional repeated sampling framework. Demnati and Rao (2004) proposed a unified approach to deriving Taylor linearization variance estimators that leads directly to a unique variance estimator that satisfies the above considerations for general designs. When analyzing survey data, finite populations are often assumed to be generated from super-population models, and analytical inferences on model parameters are of interest. If the sampling fractions are small, then the sampling variance captures almost the entire variation generated by the design and model random processes. However, when the sampling fractions are not negligible, the model variance should be taken into account in order to construct valid inferences on model parameters under the combined process of generating the finite population from the assumed super-population model and the selection of the sample according to the specified sampling design. In this paper, we obtain an estimator of the total variance, using the Demnati-Rao approach, when the characteristics of interest are assumed to be random variables generated from a super-population model. We illustrate the method using ratio estimators and estimators defined as solutions to calibration weighted estimating equations. Simulation results on the performance of the proposed variance estimator for model parameters are also presented.

The size of the cell-phone-only population in the USA has increased rapidly in recent years and, correspondingly, researchers have begun to experiment with sampling and interviewing of cell-phone subscribers. We discuss statistical issues involved in the sampling design and estimation phases of cell-phone studies. This work is presented primarily in the context of a nonoverlapping dual-frame survey in which one frame and sample are employed for the landline population and a second frame and sample are employed for the cell-phone-only population. Additional considerations necessary for overlapping dual-frame surveys (where the cell-phone frame and sample include some of the landline population) are also discussed. We illustrate the methods using the design of the National Immunization Survey (NIS), which monitors the vaccination rates of children age 19-35 months and teens age 13-17 years. The NIS is a nationwide telephone survey, followed by a provider record check, conducted by the Centers for Disease Control and Prevention.

Data collection for poverty assessments in Africa is time consuming, expensive and can be subject to numerous constraints. In this paper we present a procedure to collect data from poor households involved in small-scale inland fisheries as well as agricultural activities. A sampling scheme has been developed that captures the heterogeneity in ecological conditions and the seasonality of livelihood options. Sampling includes a three point panel survey of 300 households. The respondents belong to four different ethnic groups randomly chosen from three strata, each representing a different ecological zone. In the first part of the paper some background information is given on the objectives of the research, the study site and survey design, which were guiding the data collection process. The second part of the paper discusses the typical constraints that are hampering empirical work in Sub-Saharan Africa, and shows how different challenges have been resolved. These lessons could guide researchers in designing appropriate socio-economic surveys in comparable settings.

The current economic downturn in the US could challenge costly strategies in survey operations. In the Behavioral Risk Factor Surveillance System (BRFSS), ending the monthly data collection at 31 days could be a less costly alternative. However, this could potentially exclude a portion of interviews completed after 31 days (late responders) whose respondent characteristics could be different in many respects from those who completed the survey within 31 days (early responders). We examined whether there are differences between the early and late responders in demographics, health-care coverage, general health status, health risk behaviors, and chronic disease conditions or illnesses. We used 2007 BRFSS data, where a representative sample of the noninstitutionalized adult U.S. population was selected using a random digit dialing method. Late responders were significantly more likely to be male; to report race/ethnicity as Hispanic; to have annual income higher than $50,000; to be younger than 45 years of age; to have less than high school education; to have health-care coverage; to be significantly more likely to report good health; and to be significantly less likely to report hypertension, diabetes, or being obese. The observed differences between early and late responders on survey estimates may hardly influence national and state-level estimates. As the proportion of late responders may increase in the future, its impact on surveillance estimates should be examined before excluding from the analysis. Analysis on late responders only should combine several years of data to produce reliable estimates.

The Program for International Student Assessment (PISA) is designed to assess, on a regular basis, the achievement of 15-year-olds in reading, mathematical and scientific literacy through a common international test.

Information gathered through PISA enables a thorough comparative analysis of the skill level of students near the end of their compulsory education. PISA also permits exploration of the ways that skills vary across different social and economic groups and the factors that influence the level and distribution of skills within and between countries.

PISA is a collaborative effort among member countries of the Organization for Economic Co-operation and Development (OECD). In Canada, PISA is administered through a partnership of the Council of Ministers of Education, Canada, Human Resources and Skills Development Canada and Statistics Canada.

PISA will be repeated every three years. The first PISA cycle was conducted in 2000 and focused on reading, with mathematics and science as minor domains. The focus shifts to mathematics in PISA 2003, to science in 2006, and back to reading in 2009.

These reports provide results of the PISA assessments of student performance at the provincial level, and compare the achievement of Canadian students to that of students internationally.

The Survey of Older Workers is sponsored by the Labour Market Policy branch of Human Resources and Skills Development Canada (HRSDC). The survey is designed to assess the labour market intentions and transitions of older Canadians. The subject matter is intended to identify "factors" that influence the decision to retire or remain working. In this context pensions, general finances, the role of dependents, the nature of work, health considerations etc., will be of primary concern in trying to understand workers' intentions and motivations.

This publication shows readers how to design and conduct a census or sample survey. It explains basic survey concepts and provides information on how to create efficient and high quality surveys. It is aimed at those involved in planning, conducting or managing a survey and at students of survey design courses.

This book contains the following information:

-how to plan and manage a survey;-how to formulate the survey objectives and design a questionnaire; -things to consider when determining a sample design (choosing between a sample or a census, defining the survey population, choosing a survey frame, identifying possible sources of survey error); -choosing a method of collection (self-enumeration, personal interviews or telephone interviews; computer-assisted versus paper-based questionnaires); -organizing and conducting data collection operations;-determining the sample size, allocating the sample across strata and selecting the sample; -methods of point estimation and variance estimation, and data analysis; -the use of administrative data, particularly during the design and estimation phases-how to process the data (which consists of all data handling activities between collection and estimation) and use quality control and quality assurance measures to minimize and control errors during various survey steps; and-disclosure control and data dissemination.

This publication also includes a case study that illustrates the steps in developing a household survey, using the methods and principles presented in the book. This publication was previously only available in print format and originally published in 2003.

This annual publication is based on the Survey of Intellectual Property Commercialization in the Higher Education Sector which tracks the progress of innovation in this area.

The objective of the survey is to assure the availability of pertinent information to monitor science and technology related activities and to support the development of science and technology policy. The topic studied is intellectual property management at universities and affiliated teaching hospitals. The data are used to determine how to maximize the benefits resulting from public sector research. Data users include the federal and provincial governments and university administrators and researchers.

This publication presents a series of research articles based on longitudinal data from the National Population Health Survey Household Component and addresses important health issues. It also provides links to tables, other research articles and information about the survey.

By following the same individuals every two years since 1994/1995, this survey has become a key source of health information on how lifestyle, family and economic factors affect the population's health over time.

The longitudinal nature of the articles and health data that are presented is what makes this publication original.

This document of fact sheets provides an Aboriginal language profile of Métis, Inuit, and off-reserve First Nations children under the age of six in Canada. The 2006 Aboriginal Children's Survey is used to provide broad indicators of young Aboriginal children's experiences with Aboriginal languages. Data include their ability to speak and understand an Aboriginal language, and their exposure to Aboriginal languages at home and in the community. Family characteristics associated with Aboriginal language knowledge are also presented. Finally, the hopes and expectations of parents regarding their children's acquisition of an Aboriginal language are described.

This document of fact sheets provides an early learning profile of Métis, Inuit, and off-reserve First Nations children under the age of six in Canada. The 2006 Aboriginal Children's Survey is used to provide broad indicators of young Aboriginal children's experiences with learning. Data include how they learn about words and traditional activities and who helps them learn. Family characteristics associated with participation in early learning activities are also presented.

This series contains short analytical articles providing statistical insights on emerging issues in the economy such as productivity, innovation and technology use. These articles briefly describe the issues and the results examined by these research papers.

The articles describe issues on a wide range of topics, including - the amount of dynamic competition taking place as a result of the entry of new firms and the exit of closed firms; - the amount of merger activity taking place; - the difference between multinational and domestic firms; - the productivity growth in Canada; - the changes in the geographic location of industry; - the problems in small-firm financing; - the changing industrial structure of different regions; - how the economy interacts with the environment; - the changes in trade patterns; - Canada/United States price differences; - the innovation process in Canada; - the differences between small and large producers; - the changing patterns of advanced technology use and its effect on firm performance; - the type of strategies that differentiate more-successful from less-successful firms.

This series of profiles provides analysis on a variety of topics and issues concerning victimization, offending and public perceptions of crime and the justice system. The profiles primarily draw on results from the General Social Survey on victimization. Where applicable, they also incorporate information from other data sources, such as the Census of the Population and the Incident-based Uniform Crime Reporting Survey.

Examples of the topics explored through this series include: Victimization and offending in Canada's territories, Canadians use of crime prevention measures and victimization of older Canadians. This is a unique periodical, of great interest to those who have to plan, establish, administer and evaluate justice programs and projects, or anyone who has an interest in Canada's justice system.

The papers in this series are based on the Survey of Financial Security which is a study of what families own (assets) and what they owe (debts). Various topics are covered by this survey, such as the value of family assets (home; other property; vehicles; bank accounts; term deposits; life insurance; and investments in registered savings plans, bonds, mutual funds, stocks, etc.), the amount of family debts (amount owed on mortgages, car loans, credit cards, other charge accounts, student loans, etc.), major on-going expenses for housing and child care, and any employer pensions plans that members of the family belong to. Information is also available on the demographic, employment, income and educational characteristics of family members. This research paper series covers various topics relating to survey content, concepts and operations.