2009 Canadian Vehicle Survey Summary Report

ANNEX A

Notes about data quality and interpretation of results

The Canadian Vehicle Survey (CVS) is a quarterly vehicle-based survey. It provides quarterly and annual estimates of the distance travelled by on-road vehicles in Canada and their fuel consumption.16 In 2009, there were 26 995 vehicles in the sample from the provinces and 16 488 in the sample from the territories. Because participation is voluntary, a percentage of these samples included non-respondents. The response rate was just above 50 percent for the provinces and 12 percent for the territories.

Although considerable effort is exerted to ensure that high standards are maintained throughout all survey operations, the resulting estimates are inevitably subject to a certain degree of error. The total survey error is defined as the difference between the survey estimate and the true value for the population. The total survey error consists of two types of errors: sampling and non-sampling.

Sampling errors occur because the CVS examines only a segment of the population, rather than the entire population. Factors such as sample size, sample design and estimation method affect the sampling error.

If the population is heterogeneous, which is the case for the CVS, a large sample size is needed to reduce sampling errors. In addition, the CVS relies on a stratified sample design to divide the population into similar groups, thereby reducing sampling errors by producing estimates for homogeneous groups. These estimates are then aggregated to produce estimates for the entire population.

Each estimate in the report is associated with a coefficient of variation (CV), which is the basis for determining an all-encompassing quality indicator. A CV measures the sampling error of the estimates and takes into account variability due to non-response and imputation.

CVs are also used to establish confidence intervals (I), which express the accuracy of an estimate in concrete terms. The I indicates the level of confidence that the true value of a characteristic occurs within certain limits. For example, an I of 95 percent, I(0.95), implies that if the sampling were repeated indefinitely, with each sample providing a different I, 95 percent of the intervals would contain the true value.17

To illustrate how all these concepts are linked, take as an example a CVS estimate stating that on-road vehicles travelled 333.3 billion vehicle-kilometres (VKM) in Canada in 2009. This is an excellent estimate because it has a CV of 0.024 and, therefore, a quality indicator of “A.” To determine the I of 95 percent attributed to this estimate, the following calculation is performed:18

I(0.95) = [333.3 billion x (1 – 1.96 x CV),
333.3 billion x (1 + 1.96 x CV)]

I(0.95) = [333.3 billion x (1 – 1.96 x 0.024),
333.3 billion x (1 + 1.96 x 0.024)]

I(0.95)19 = [317.4 billion, 349.2 billion]

Based on Figure A-1, it can be stated with a 95 percent degree of confidence that the distance travelled in Canada in 2009 was between 317.4 billion and 349.2 billion VKM. The smaller the I, the greater the chances that the survey estimate is close to the true value. Figure A-1 shows the I for the preceding example.

Figure A-1 — 95 percent confidence interval for the CVS estimate of VKM travelled in Canada, 2009.

It is important to remember the confidence interval when analysing survey results. Table A-1 is a reference for readers who want to assess the I attributed to an estimate based on the quality indicators in this report.

Table A 1 — Range of the confidence intervals attributed to CVS estimates
Quality indicator Quality of estimate Coefficient of variation Range of the confidence intervals
A Excellent Less than 5% Estimate ±0% to 9.9%
B Very good 5% – 9.9% Estimate ±10% to 19.9%
C Good 10% – 14.9% Estimate ±20% to 29.9%
D Acceptable 15% – 19.9% Estimate ±30% to 39.9%
E Use with caution 20% – 34.9% Estimate ±40% to 69.9%
F Too unreliable to be published 35% or more Estimate ±70% and over

Non-sampling errors can also contribute to the total survey error. This second type of error can occur at almost any stage of the survey. In particular, errors can arise when a respondent provides incorrect information, does not answer a question or misinterprets a question.

Non-sampling errors can also arise when data are being processed. Some of these errors will be cancelled over a large number of observations, but systematically occurring errors will contribute to a bias in the estimates. For example, if people demonstrating similar characteristics consistently tend not to respond to the survey, a bias may result in the estimates.

Some non-sampling errors are difficult to quantify and are not reflected by quality indicators. However, the CVS quality indicators take into account variance due to non-response and imputation and, consequently, account for some of the non-sampling errors. Other measures, such as survey response rate and imputation rate, can also serve as indicators for non-sampling errors.


  1. Annex B in this report provides more information on the scope and methodology of the CVS.
  2. Satin, A. and W. Shastry, Statistics Canada, Survey Sampling: A Non-mathematical Guide, 2nd edition, Cat. No. 12-602E, Ottawa, 1993, p. 14.
  3. If a normal distribution is assumed, the I of 95 percent corresponds to the estimate plus or minus approximately two times the standard error. The standard error is equal to the square root of the variance, which corresponds to the product of the estimate and the CV.
  4. Final values are calculated with full precision. Using rounded values would yield I(0.95) = [317.3 billion, 349.0 billion].