Annual Survey of Service Industries: Newspaper Publishers

Detailed information for 2003

Status:

Active

Frequency:

Annual

Record number:

4710

The survey collects financial and operating data needed to produce statistics on the newspaper publishers industry in Canada.

Data release - May 27, 2005

Description

This annual sample survey collects the financial and operating data needed to produce statistics on the newspaper publishing industry in Canada. The survey also collects detailed information on the characteristics of the businesses, such as type of revenue and type of client.

These data are aggregated with information from other sources to produce official estimates of the national and provincial economic production of the newspaper publishing industry in Canada. The results from this survey provide data to businesses, governments, investors and associations. These data allow these groups to monitor the growth of the industry, measure performance, allow comparison across similar businesses and to better understand this industry to react to trends and patterns.

Statistical activity

The survey is administered as part of the Unified Enterprise Survey program (UES). The UES program has been designed to integrate, gradually over time, the approximately 200 separate business surveys into a single master survey program. The UES aims at collecting more industry and product detail at the provincial level than was previously possible while avoiding overlap between different survey questionnaires. The redesigned business survey questionnaires have a consistent look, structure and content. The unified approach makes reporting easier for firms operating in different industries because they can provide similar information for each branch operation. This way they avoid having to respond to questionnaires that differ for each industry in terms of format, wording and even concepts.

This survey is part of the Service Industries Program. The survey data gathered are used to compile aggregate statistics for over thirty service industry groupings. Financial data, including revenue, expense and profit statistics are available for all of the surveys in the program. In addition, many compile and disseminate industry-specific information.

Reference period: Calendar year

Collection period: January to September

Subjects

  • Business, consumer and property services
  • Business performance and ownership
  • Financial statements and performance
  • Information and culture

Data sources and methodology

Target population

The target population consists of all statistical establishments (sometimes referred to as firms or units) classified as Newspaper Publishers (NAICS 511110) according to the North American Industry Classification System (NAICS) during the reference year.

Instrument design

The survey questionnaires comprise financial characteristics such as sources of revenue, details of expenses and employment characteristics. Based on contacts with respondents and data users, some modifications have been incorporated to the questionnaires in order to reflect the nature of the industry surveyed. The changes were field tested to ensure that they were reasonable and sustainable.

Sampling

This is a sample survey with a cross-sectional design.

The frame is maintained by Statistics Canada's Business Register and is updated using administrative data and survey feedback.

Prior to the selection of a random sample, establishments are classified into homogeneous groups (i.e., groups with the same NAICS codes, same geography (province/territory). Quality requirements are targeted, and then each group is divided into sub-groups called strata: take-all, must-take, take-some, and take-none.

The take-all stratum represents the largest firms in terms of performance, based on revenue, in an industry. The must-take stratum comprises units selected on the basis of complex structure characteristics, e.g., multi-establishment, multi-legal, multi-NAICS, or multi-province enterprises. All units in the take-all and the must-take stratum are selected to the sample. Units in the take-some strata are subject to simple random sampling. Units below specified revenue thresholds are not sampled (these thresholds vary across provinces and industries). These records fall into the take-none stratum, where estimates are based solely on administrative data.

Data sources

Responding to this survey is mandatory.

Data are collected directly from survey respondents and extracted from administrative files.

Data are collected through a mail-out/mail-back process, while providing respondents with the option of telephone or electronic filing methods.

Follow-up procedures are applied when a questionnaire has not been received after a pre-specified period of time.

View the Questionnaire(s) and reporting guide(s) .

Error detection

Data are examined for inconsistencies and errors using automated edits coupled with analytical review. Where possible, data will be verified using alternate sources.

Imputation

Where information is missing, imputation is performed using a "nearest neighbour" procedure (donor imputation), using historical data where available, using averages based on responses from a set of similar establishments, or using administrative data as a proxy for reported data.

Estimation

As part of the estimation process, survey data are weighted and combined with administrative data to produce final industry estimates.

Quality evaluation

Prior to dissemination, combined survey results are analyzed for overall quality; in general, this includes a detailed review of individual responses (especially for the largest companies), an assessment of the general economic conditions portrayed by the data, historic trends, and comparisons with other data sources.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.

Data accuracy

While considerable effort is made to ensure high standards throughout all stages of collection and processing, the resulting estimates are inevitably subject to a certain degree of error. These errors can be broken down into two major types: non-sampling and sampling.

Non-sampling error is not related to sampling and may occur for many reasons. For example, non-response is an important source of non-sampling error. Population coverage, differences in the interpretation of questions, incorrect information from respondents, and mistakes in recording, coding and processing data are other examples of non-sampling errors.

Sampling error occurs because population estimates are derived from a sample of the population rather than the entire population. Sampling error depends on factors such as sample size, sampling design, and the method of estimation. An important property of probability sampling is that sampling error can be computed from the sample itself by using a statistical measure called the coefficient of variation (CV). The assumption is that over repeated surveys, the relative difference between a sample estimate and the estimate that would have been obtained from an enumeration of all units in the universe would be less than twice the CV, 95 times out of 100. The range of acceptable data values yielded by a sample is called a confidence interval. Confidence intervals can be constructed around the estimate using the CV. First, we calculate the standard error by multiplying the sample estimate by the CV. The sample estimate plus or minus twice the standard error is then referred to as a 95% confidence interval.

For the Newspaper Publishers Survey, CVs were calculated for each estimate. Generally, the more commonly reported variables obtained excellent CVs (5% or less), while the less commonly reported variables were associated with higher but still very good CVs (under 10%). The CVs are available upon request.

Report a problem on this page

Is something not working? Is there information outdated? Can't find what you're looking for?

Please contact us and let us know how we can help you.

Privacy notice

Date modified: