Keyword search
Filter results by
Search HelpKeyword(s)
Type
Results
All (4)
All (4) ((4 results))
- Surveys and statistical programs – Documentation: 62F0026M2001001Description:
This report describes the quality indicators produced for the 1998 Survey of Household Spending. It covers the usual quality indicators that help users interpret data, such as coefficients of variation, nonresponse rates, imputation rates and the impact of imputed data on the estimates. Added to these are various less often used indicators such as slippage rates and measures of the representativity of the sample for particular characteristics that are useful for evaluating the survey methodology.
Release date: 2001-10-15 - Surveys and statistical programs – Documentation: 62F0026M2001002Description:
This report describes the quality indicators produced for the 1999 Survey of Household Spending. It covers the usual quality indicators that help users interpret data, such as coefficients of variation, nonresponse rates, imputation rates and the impact of imputed data on the estimates. Added to these are various less often used indicators such as slippage rates and measures of the representativity of the sample for particular characteristics that are useful for evaluating the survey methodology.
Release date: 2001-10-15 - 3. An Assessment of EI and SA Reporting in SLID ArchivedArticles and reports: 11F0019M2001166Geography: CanadaDescription:
This study assesses two potential problems with respect to the reporting of Employment Insurance (EI) and Social Assistance (SA) benefits in the Survey of Labour and Income Dynamics (SLID): (a) under-reporting of the monthly number of beneficiaries; and (b) a tendency to incorrectly report receiving benefits throughout the year, while in fact benefits may have been received only in certain months, leading to artificial spikes in the January starts and December terminations of benefit spells (seam effect). The results of the analysis show the following:
(1) The rate of under-reporting of EI in SLID is about 15%. Although it varies by month (from 0% to 30%), it is fairly stable from year to year.
(2) There are significant spikes in the number of January starts and December terminations of EI benefit spells. However, the spikes in January starts appear to represent a real phenomenon, rather than a seam problem. They mirror closely the pattern of establishment of new EI claims (the latter increase significantly in January as a result of the decline in employment following the Christmas peak demand). There are no corresponding statistics for EI claim terminations to assess the nature of December spikes.
(3) The rate of under-reporting of SA in SLID is about 50%, significantly greater than for EI. The rate of under-reporting goes down to about 20% to 30%, if we assume that those who received SA, but did not report in which months they received benefits, received benefits throughout the year.
(4) There are large spikes in the number of January starts and December terminations. As in the case of EI, the SA could reflect a real phenomenon. After all, SA starts and terminations are affected by labour market conditions, in the same way EI starts and terminations are affected. However, the SA spikes are much larger than the EI spikes, which increases the probability that, at least in part, are due to a seam effect.
Release date: 2001-09-11 - 4. On the validity of Markov latent class analysis for estimating classification error in labor force data ArchivedArticles and reports: 12-001-X20000025534Description:
The primary goal of this research is to investigate the validity of Markov latent class analysis (MLCA) estimates of labor force classification error and to evaluate the efficacy of MLC analysis as an alternative to traditional methods for evaluating data quality.
Release date: 2001-02-28
Data (0)
Data (0) (0 results)
No content available at this time.
Analysis (2)
Analysis (2) ((2 results))
- 1. An Assessment of EI and SA Reporting in SLID ArchivedArticles and reports: 11F0019M2001166Geography: CanadaDescription:
This study assesses two potential problems with respect to the reporting of Employment Insurance (EI) and Social Assistance (SA) benefits in the Survey of Labour and Income Dynamics (SLID): (a) under-reporting of the monthly number of beneficiaries; and (b) a tendency to incorrectly report receiving benefits throughout the year, while in fact benefits may have been received only in certain months, leading to artificial spikes in the January starts and December terminations of benefit spells (seam effect). The results of the analysis show the following:
(1) The rate of under-reporting of EI in SLID is about 15%. Although it varies by month (from 0% to 30%), it is fairly stable from year to year.
(2) There are significant spikes in the number of January starts and December terminations of EI benefit spells. However, the spikes in January starts appear to represent a real phenomenon, rather than a seam problem. They mirror closely the pattern of establishment of new EI claims (the latter increase significantly in January as a result of the decline in employment following the Christmas peak demand). There are no corresponding statistics for EI claim terminations to assess the nature of December spikes.
(3) The rate of under-reporting of SA in SLID is about 50%, significantly greater than for EI. The rate of under-reporting goes down to about 20% to 30%, if we assume that those who received SA, but did not report in which months they received benefits, received benefits throughout the year.
(4) There are large spikes in the number of January starts and December terminations. As in the case of EI, the SA could reflect a real phenomenon. After all, SA starts and terminations are affected by labour market conditions, in the same way EI starts and terminations are affected. However, the SA spikes are much larger than the EI spikes, which increases the probability that, at least in part, are due to a seam effect.
Release date: 2001-09-11 - 2. On the validity of Markov latent class analysis for estimating classification error in labor force data ArchivedArticles and reports: 12-001-X20000025534Description:
The primary goal of this research is to investigate the validity of Markov latent class analysis (MLCA) estimates of labor force classification error and to evaluate the efficacy of MLC analysis as an alternative to traditional methods for evaluating data quality.
Release date: 2001-02-28
Reference (2)
Reference (2) ((2 results))
- Surveys and statistical programs – Documentation: 62F0026M2001001Description:
This report describes the quality indicators produced for the 1998 Survey of Household Spending. It covers the usual quality indicators that help users interpret data, such as coefficients of variation, nonresponse rates, imputation rates and the impact of imputed data on the estimates. Added to these are various less often used indicators such as slippage rates and measures of the representativity of the sample for particular characteristics that are useful for evaluating the survey methodology.
Release date: 2001-10-15 - Surveys and statistical programs – Documentation: 62F0026M2001002Description:
This report describes the quality indicators produced for the 1999 Survey of Household Spending. It covers the usual quality indicators that help users interpret data, such as coefficients of variation, nonresponse rates, imputation rates and the impact of imputed data on the estimates. Added to these are various less often used indicators such as slippage rates and measures of the representativity of the sample for particular characteristics that are useful for evaluating the survey methodology.
Release date: 2001-10-15
- Date modified: