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General Social Survey (GSS) on Victimization
In 2004, Statistics Canada conducted the victimization cycle of the GSS for the fourth time. Previous cycles were conducted in 1988, 1993 and 1999. The objectives of the survey are to provide estimates of the extent to which people experience incidences of eight offence types (see text box 1); to examine risk factors associated with victimization; to examine rates of reporting victimization to police; and to measure fear of crime and public perceptions of crime and the criminal justice system.
Households in the 10 provinces were selected using Random Digit Dialling. Once a household was chosen, an individual 15 years or older was selected randomly to respond to the survey. Households without telephones, households with only cellular phone service as well as individuals living in institutions were excluded. These groups combined represented 4% of the target population. This figure is not large enough to significantly change the survey results.
The sample size in 2004 was about 24,000 households, similar to the sample size in 1999 (26,000) and considerably higher than the sample in 1993 and 1988 (10,000 each). Of the 31,895 households that were selected for the 2004 GSS sample, 23,766 useable responses were obtained.
The data that appear in this profile are based on estimates from a sample of the Canadian population and are therefore subject to sampling error. Sampling error refers to the difference between an estimate derived from the sample and the one that would have been obtained from collecting data from every person in the population.
This profile uses the coefficient of variation (CV) as a measure of the sampling error. Any estimate that has a high CV (over 33.3%) has not been published because the estimate is too unreliable. An estimate that has a CV between 16.6 and 33.3 should be used with caution. The symbol 'E' is used to identify these estimates.
When comparing estimates for significant differences, the hypothesis that the difference between two estimates is zero is tested. A 95% confidence interval is then constructed around this difference and if this interval contains zero, then we conclude that the difference is not significant. If, however, this confidence interval does not contain zero, one can conclude that there is a significant difference between the two estimates.
In addition to sampling error, non-sampling errors may have been introduced. Types of non-sampling errors include the refusal by a respondent to report, a respondent's inability to remember or report events accurately, or errors in coding and processing of the data. In addition, individuals who could not speak English or French well enough to complete the survey were not included.
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