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- 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 - 3. A generalization of the Lavallée and Hidiroglou algorithm for stratification in business Surveys ArchivedArticles and reports: 12-001-X20020026432Description:
This paper suggests stratification algorithms that account for a discrepancy between the stratification variable and the study variable when planning a stratified survey design. Two models are proposed for the change between these two variables. One is a log-linear regression model; the other postulates that the study variable and the stratification variable coincide for most units, and that large discrepancies occur for some units. Then, the Lavallée and Hidiroglou (1988) stratification algorithm is modified to incorporate these models in the determination of the optimal sample sizes and of the optimal stratum boundaries for a stratified sampling design. An example illustrates the performance of the new stratification algorithm. A discussion of the numerical implementation of this algorithm is also presented.
Release date: 2003-01-29 - 4. Differences in Innovator and Non-innovator Profiles: Small Establishments in Business Services ArchivedArticles and reports: 11F0019M2000143Geography: CanadaDescription:
This paper explores differences between innovative and non-innovative establishments in business service industries. It focuses on small establishments that supply core technical inputs to other firms: establishments in computer and related services, engineering, and other scientific and technical services.
The analysis begins by examining the incidence of innovation within the small firm population. Forty percent of small businesses report introducing new or improved products, processes or organizational forms. Among these businesses, product innovation dominates over process or organizational change. A majority of these establishments reveal an ongoing commitment to innovation programs by introducing innovations on a regular basis. By contrast, businesses that do not introduce new or improved products, processes or organizational methods reveal little supporting evidence of innovation activity.
The paper then investigates differences in strategic intensity between innovative and non-innovative businesses. Innovators attach greater importance to financial management and capital acquisition. Innovators also place more emphasis on recruiting skilled labour and on promoting incentive compensation. These distinctions are sensible - among small firms in R&D-intensive industries, financing and human resource competencies play a critical role in the innovation process.
A final section examines whether the obstacles to innovation differ between innovators and non-innovators. Innovators are more likely to report difficulties related to market success, imitation, and skill restrictions. Evidence of learning-by-doing is more apparent within a multivariate framework. The probability of encountering risk-related obstacles and input restrictions is higher among establishments that engage in R&D and use intellectual property rights, both key elements of the innovation process. Many obstacles to innovation are also more apparent for businesses that stress financing, marketing, production or human resource strategies.
Release date: 2000-01-25
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- 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 - 3. A generalization of the Lavallée and Hidiroglou algorithm for stratification in business Surveys ArchivedArticles and reports: 12-001-X20020026432Description:
This paper suggests stratification algorithms that account for a discrepancy between the stratification variable and the study variable when planning a stratified survey design. Two models are proposed for the change between these two variables. One is a log-linear regression model; the other postulates that the study variable and the stratification variable coincide for most units, and that large discrepancies occur for some units. Then, the Lavallée and Hidiroglou (1988) stratification algorithm is modified to incorporate these models in the determination of the optimal sample sizes and of the optimal stratum boundaries for a stratified sampling design. An example illustrates the performance of the new stratification algorithm. A discussion of the numerical implementation of this algorithm is also presented.
Release date: 2003-01-29 - 4. Differences in Innovator and Non-innovator Profiles: Small Establishments in Business Services ArchivedArticles and reports: 11F0019M2000143Geography: CanadaDescription:
This paper explores differences between innovative and non-innovative establishments in business service industries. It focuses on small establishments that supply core technical inputs to other firms: establishments in computer and related services, engineering, and other scientific and technical services.
The analysis begins by examining the incidence of innovation within the small firm population. Forty percent of small businesses report introducing new or improved products, processes or organizational forms. Among these businesses, product innovation dominates over process or organizational change. A majority of these establishments reveal an ongoing commitment to innovation programs by introducing innovations on a regular basis. By contrast, businesses that do not introduce new or improved products, processes or organizational methods reveal little supporting evidence of innovation activity.
The paper then investigates differences in strategic intensity between innovative and non-innovative businesses. Innovators attach greater importance to financial management and capital acquisition. Innovators also place more emphasis on recruiting skilled labour and on promoting incentive compensation. These distinctions are sensible - among small firms in R&D-intensive industries, financing and human resource competencies play a critical role in the innovation process.
A final section examines whether the obstacles to innovation differ between innovators and non-innovators. Innovators are more likely to report difficulties related to market success, imitation, and skill restrictions. Evidence of learning-by-doing is more apparent within a multivariate framework. The probability of encountering risk-related obstacles and input restrictions is higher among establishments that engage in R&D and use intellectual property rights, both key elements of the innovation process. Many obstacles to innovation are also more apparent for businesses that stress financing, marketing, production or human resource strategies.
Release date: 2000-01-25
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