Keyword search
Filter results by
Search HelpKeyword(s)
Subject
Survey or statistical program
Results
All (5)
All (5) ((5 results))
- Articles and reports: 11-522-X200800010957Description:
Business surveys differ from surveys of populations of individual persons or households in many respects. Two of the most important differences are (a) that respondents in business surveys do not answer questions about characteristics of themselves (such as their experiences, behaviours, attitudes and feelings) but about characteristics of organizations (such as their size, revenues, policies, and strategies) and (b) that they answer these questions as an informant for that organization. Academic business surveys differ from other business surveys, such as of national statistical agencies, in many respects as well. The one most important difference is that academic business surveys usually do not aim at generating descriptive statistics but at testing hypotheses, i.e. relations between variables. Response rates in academic business surveys are very low, which implies a huge risk of non-response bias. Usually no attempt is made to assess the extent of non-response bias and published survey results might, therefore, not be a correct reflection of actual relations within the population, which in return increases the likelihood that the reported test result is not correct.
This paper provides an analysis of how (the risk of) non-response bias is discussed in research papers published in top management journals. It demonstrates that non-response bias is not assessed to a sufficient degree and that, if attempted at all, correction of non-response bias is difficult or very costly in practice. Three approaches to dealing with this problem are presented and discussed:(a) obtaining data by other means than questionnaires;(b) conducting surveys of very small populations; and(c) conducting surveys of very small samples.
It will be discussed why these approaches are appropriate means of testing hypotheses in populations. Trade-offs regarding the selection of an approach will be discussed as well.
Release date: 2009-12-03 - Articles and reports: 11-522-X200800010959Description:
The Unified Enterprise Survey (UES) at Statistics Canada is an annual business survey that unifies more than 60 surveys from different industries. Two types of collection follow-up score functions are currently used in the UES data collection. The objective of using a score function is to maximize the economically weighted response rates of the survey in terms of the primary variables of interest, under the constraint of a limited follow-up budget. Since the two types of score functions are based on different methodologies, they could have different impacts on the final estimates.
This study generally compares the two types of score functions based on the collection data obtained from the two recent years. For comparison purposes, this study applies each score function method to the same data respectively and computes various estimates of the published financial and commodity variables, their deviation from the true pseudo value and their mean square deviation, based on each method. These estimates of deviation and mean square deviation based on each method are then used to measure the impact of each score function on the final estimates of the financial and commodity variables.
Release date: 2009-12-03 - Articles and reports: 12-001-X200800210760Description:
The design of a stratified simple random sample without replacement from a finite population deals with two main issues: the definition of a rule to partition the population into strata, and the allocation of sampling units in the selected strata. This article examines a tree-based strategy which plans to approach jointly these issues when the survey is multipurpose and multivariate information, quantitative or qualitative, is available. Strata are formed through a hierarchical divisive algorithm that selects finer and finer partitions by minimizing, at each step, the sample allocation required to achieve the precision levels set for each surveyed variable. In this way, large numbers of constraints can be satisfied without drastically increasing the sample size, and also without discarding variables selected for stratification or diminishing the number of their class intervals. Furthermore, the algorithm tends not to define empty or almost empty strata, thus avoiding the need for strata collapsing aggregations. The procedure was applied to redesign the Italian Farm Structure Survey. The results indicate that the gain in efficiency held using our strategy is nontrivial. For a given sample size, this procedure achieves the required precision by exploiting a number of strata which is usually a very small fraction of the number of strata available when combining all possible classes from any of the covariates.
Release date: 2008-12-23 - 4. Trucking in Canada ArchivedTable: 53-222-XDescription:
This publication presents a comprehensive overview of the Canadian trucking industry, both for-hire and private (own account). Principal information includes statistics on revenues and expenses, equipment operated, investment, employment, and commodities transported from point of origin to point of destination. Also included are special studies, a glossary and an explanation of data quality measures and methodology.
Release date: 2007-06-22 - Table: 50-002-X20010015780Description:
Section 1 described results for small for-hire carriers whose operating revenues were between $30,000 and $1,000,000. Section 2 contains data for all owner operators included in the Annual Motor Carriers of Freight Survey of Small For-hire Carriers and Owner Operators including some firms whose operating revenues exceeded $1,000,000. Section 3 provides a general discussion of the Annual Motor Carriers of Freight Survey of Small For-hire Carriers and Owner Operators methodology and data quality.
Release date: 2001-06-29
Data (2)
Data (2) ((2 results))
- 1. Trucking in Canada ArchivedTable: 53-222-XDescription:
This publication presents a comprehensive overview of the Canadian trucking industry, both for-hire and private (own account). Principal information includes statistics on revenues and expenses, equipment operated, investment, employment, and commodities transported from point of origin to point of destination. Also included are special studies, a glossary and an explanation of data quality measures and methodology.
Release date: 2007-06-22 - Table: 50-002-X20010015780Description:
Section 1 described results for small for-hire carriers whose operating revenues were between $30,000 and $1,000,000. Section 2 contains data for all owner operators included in the Annual Motor Carriers of Freight Survey of Small For-hire Carriers and Owner Operators including some firms whose operating revenues exceeded $1,000,000. Section 3 provides a general discussion of the Annual Motor Carriers of Freight Survey of Small For-hire Carriers and Owner Operators methodology and data quality.
Release date: 2001-06-29
Analysis (3)
Analysis (3) ((3 results))
- Articles and reports: 11-522-X200800010957Description:
Business surveys differ from surveys of populations of individual persons or households in many respects. Two of the most important differences are (a) that respondents in business surveys do not answer questions about characteristics of themselves (such as their experiences, behaviours, attitudes and feelings) but about characteristics of organizations (such as their size, revenues, policies, and strategies) and (b) that they answer these questions as an informant for that organization. Academic business surveys differ from other business surveys, such as of national statistical agencies, in many respects as well. The one most important difference is that academic business surveys usually do not aim at generating descriptive statistics but at testing hypotheses, i.e. relations between variables. Response rates in academic business surveys are very low, which implies a huge risk of non-response bias. Usually no attempt is made to assess the extent of non-response bias and published survey results might, therefore, not be a correct reflection of actual relations within the population, which in return increases the likelihood that the reported test result is not correct.
This paper provides an analysis of how (the risk of) non-response bias is discussed in research papers published in top management journals. It demonstrates that non-response bias is not assessed to a sufficient degree and that, if attempted at all, correction of non-response bias is difficult or very costly in practice. Three approaches to dealing with this problem are presented and discussed:(a) obtaining data by other means than questionnaires;(b) conducting surveys of very small populations; and(c) conducting surveys of very small samples.
It will be discussed why these approaches are appropriate means of testing hypotheses in populations. Trade-offs regarding the selection of an approach will be discussed as well.
Release date: 2009-12-03 - Articles and reports: 11-522-X200800010959Description:
The Unified Enterprise Survey (UES) at Statistics Canada is an annual business survey that unifies more than 60 surveys from different industries. Two types of collection follow-up score functions are currently used in the UES data collection. The objective of using a score function is to maximize the economically weighted response rates of the survey in terms of the primary variables of interest, under the constraint of a limited follow-up budget. Since the two types of score functions are based on different methodologies, they could have different impacts on the final estimates.
This study generally compares the two types of score functions based on the collection data obtained from the two recent years. For comparison purposes, this study applies each score function method to the same data respectively and computes various estimates of the published financial and commodity variables, their deviation from the true pseudo value and their mean square deviation, based on each method. These estimates of deviation and mean square deviation based on each method are then used to measure the impact of each score function on the final estimates of the financial and commodity variables.
Release date: 2009-12-03 - Articles and reports: 12-001-X200800210760Description:
The design of a stratified simple random sample without replacement from a finite population deals with two main issues: the definition of a rule to partition the population into strata, and the allocation of sampling units in the selected strata. This article examines a tree-based strategy which plans to approach jointly these issues when the survey is multipurpose and multivariate information, quantitative or qualitative, is available. Strata are formed through a hierarchical divisive algorithm that selects finer and finer partitions by minimizing, at each step, the sample allocation required to achieve the precision levels set for each surveyed variable. In this way, large numbers of constraints can be satisfied without drastically increasing the sample size, and also without discarding variables selected for stratification or diminishing the number of their class intervals. Furthermore, the algorithm tends not to define empty or almost empty strata, thus avoiding the need for strata collapsing aggregations. The procedure was applied to redesign the Italian Farm Structure Survey. The results indicate that the gain in efficiency held using our strategy is nontrivial. For a given sample size, this procedure achieves the required precision by exploiting a number of strata which is usually a very small fraction of the number of strata available when combining all possible classes from any of the covariates.
Release date: 2008-12-23
Reference (0)
Reference (0) (0 results)
No content available at this time.
- Date modified: