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This guide will be of assistance in understanding the concepts, methodology and data quality of the surveys conducted as well as the data analysed by the Pensions and Wealth Surveys Section of the Income Statistics Division. It covers the following surveys/programs:- Pension Plans in Canada;- Trusteed Pension Funds (Census and Quarterly);- Survey of Financial Security; and- Pension adjustment/registered retirement savings plans data file provided by Canada Customs and Revenue Agency.

The analysis of survey data from different geographical areas where the data from each area are polychotomous can be easily performed using hierarchical Bayesian models, even if there are small cell counts in some of these areas. However, there are difficulties when the survey data have missing information in the form of non-response, especially when the characteristics of the respondents differ from the non-respondents. We use the selection approach for estimation when there are non-respondents because it permits inference for all the parameters. Specifically, we describe a hierarchical Bayesian model to analyse multinomial non-ignorable non-response data from different geographical areas; some of them can be small. For the model, we use a Dirichlet prior density for the multinomial probabilities and a beta prior density for the response probabilities. This permits a 'borrowing of strength' of the data from larger areas to improve the reliability in the estimates of the model parameters corresponding to the smaller areas. Because the joint posterior density of all the parameters is complex, inference is sampling-based and Markov chain Monte Carlo methods are used. We apply our method to provide an analysis of body mass index (BMI) data from the third National Health and Nutrition Examination Survey (NHANES III). For simplicity, the BMI is categorized into 3 natural levels, and this is done for each of 8 age-race-sex domains and 34 counties. We assess the performance of our model using the NHANES III data and simulated examples, which show our model works reasonably well.

In most telephone time-use surveys, respondents are called on one day and asked to report on their activities during the previous day. Given that most respondents are not available on their initial calling day, this feature of telephone time-use surveys introduces the possibility that the probability of interviewing the respondent about a given reference day is correlated with the activities on that reference day. Furthermore, non-contact bias is a more important consideration for time-use surveys than for other surveys, because time-use surveys cannot accept proxy responses. Therefore, it is essential that telephone time-use surveys have a strategy for making subsequent attempts to contact respondents. A contact strategy specifies the contact schedule and the field period. Previous literature has identified two schedules for making subsequent attempts: a convenient-day schedule and a designated-day schedule. Most of these articles recommend the designated-day schedule, but there is little evidence to support this viewpoint. In this paper, we use computer simulations to examine the bias associated with the convenient-day schedule and three variations of the designated-day schedule. The results support using a designated-day schedule, and validate the recommendations of the previous literature. The convenient-day schedule introduces systematic bias: time spent in activities done away from home tends to be overestimated. More importantly, estimates generated using the convenient-day schedule are sensitive to the variance of the contact probability. In contrast, a designated-day-with-postponement schedule generates very little bias, and is robust to a wide range of assumptions about the pattern of activities across days of the week.

This table is part of the topic "Ethnocultural Portrait of Canada," which shows 2001 Census data on ethnic groups in Canada, such as their size, geographic location and demographic characteristics. Similar information is available for Canada's visible minority population.

Data on the socio-economic characteristics of these populations will be available at a later date. As well, data on religions in Canada will be available in May 2003.

Additional information on ethnocultural diversity will be available from the Ethnic Diversity Survey in the summer of 2003.

This table can be found in the Topic Bundle: Ethnocultural Portrait of Canada, 2001 Census, Catalogue No. 97F0010XCB2001000.

It is also possible to subscribe to all the day-of-release bundles. For more information, refer to Catalogue No. 97F0023XCB.

This table is available FREE on the Internet, Catalogue No. 97F0010XIE2001001.

This table is part of the topic "Ethnocultural Portrait of Canada," which shows 2001 Census data on ethnic groups in Canada, such as their size, geographic location and demographic characteristics. Similar information is available for Canada's visible minority population.

Data on the socio-economic characteristics of these populations will be available at a later date. As well, data on religions in Canada will be available in May 2003.

Additional information on ethnocultural diversity will be available from the Ethnic Diversity Survey in the summer of 2003.

This table can be found in the Topic Bundle: Ethnocultural Portrait of Canada, 2001 Census, Catalogue No. 97F0010XCB2001000.

It is also possible to subscribe to all the day-of-release bundles. For more information, refer to Catalogue No. 97F0023XCB.

This table is available FREE on the Internet, Catalogue No. 97F0010XIE2001001.

The analysis of survey data from different geographical areas where the data from each area are polychotomous can be easily performed using hierarchical Bayesian models, even if there are small cell counts in some of these areas. However, there are difficulties when the survey data have missing information in the form of non-response, especially when the characteristics of the respondents differ from the non-respondents. We use the selection approach for estimation when there are non-respondents because it permits inference for all the parameters. Specifically, we describe a hierarchical Bayesian model to analyse multinomial non-ignorable non-response data from different geographical areas; some of them can be small. For the model, we use a Dirichlet prior density for the multinomial probabilities and a beta prior density for the response probabilities. This permits a 'borrowing of strength' of the data from larger areas to improve the reliability in the estimates of the model parameters corresponding to the smaller areas. Because the joint posterior density of all the parameters is complex, inference is sampling-based and Markov chain Monte Carlo methods are used. We apply our method to provide an analysis of body mass index (BMI) data from the third National Health and Nutrition Examination Survey (NHANES III). For simplicity, the BMI is categorized into 3 natural levels, and this is done for each of 8 age-race-sex domains and 34 counties. We assess the performance of our model using the NHANES III data and simulated examples, which show our model works reasonably well.

In most telephone time-use surveys, respondents are called on one day and asked to report on their activities during the previous day. Given that most respondents are not available on their initial calling day, this feature of telephone time-use surveys introduces the possibility that the probability of interviewing the respondent about a given reference day is correlated with the activities on that reference day. Furthermore, non-contact bias is a more important consideration for time-use surveys than for other surveys, because time-use surveys cannot accept proxy responses. Therefore, it is essential that telephone time-use surveys have a strategy for making subsequent attempts to contact respondents. A contact strategy specifies the contact schedule and the field period. Previous literature has identified two schedules for making subsequent attempts: a convenient-day schedule and a designated-day schedule. Most of these articles recommend the designated-day schedule, but there is little evidence to support this viewpoint. In this paper, we use computer simulations to examine the bias associated with the convenient-day schedule and three variations of the designated-day schedule. The results support using a designated-day schedule, and validate the recommendations of the previous literature. The convenient-day schedule introduces systematic bias: time spent in activities done away from home tends to be overestimated. More importantly, estimates generated using the convenient-day schedule are sensitive to the variance of the contact probability. In contrast, a designated-day-with-postponement schedule generates very little bias, and is robust to a wide range of assumptions about the pattern of activities across days of the week.

This guide will be of assistance in understanding the concepts, methodology and data quality of the surveys conducted as well as the data analysed by the Pensions and Wealth Surveys Section of the Income Statistics Division. It covers the following surveys/programs:- Pension Plans in Canada;- Trusteed Pension Funds (Census and Quarterly);- Survey of Financial Security; and- Pension adjustment/registered retirement savings plans data file provided by Canada Customs and Revenue Agency.