Survey Methodology
Sampling for business surveys at Statistics Canada

by M.A. Hidiroglou

  • Release date: December 23, 2025

Abstract

This article examines the methodological complexities associated with the design of business surveys, with particular emphasis on sampling strategies implemented by National Statistical Offices (NSOs). It addresses the inherent challenges posed by the dynamic nature of the business population, which necessitates continual updates to the sampling frame to ensure representativeness and relevance. Critical design considerations include the determination of optimal sample sizes, stratification across key dimensions such as industry, geographic region, and enterprise size, as well as the treatment of business births and the exclusion of inactive (or “dead”) units. The article applies Bankier’s (1988) power allocation method to a two-way stratification scheme defined by industry and geography, evaluating its performance by comparing the resulting coefficients of variation with those obtained via a raking algorithm applied to the marginal coefficients. Furthermore, the approach is extended to a multivariate context to accommodate multiple estimation domains. The discussion also encompasses practical issues related to sample rotation and coordination, which are critical for maintaining data quality and minimizing respondent burden over time.

Key Words:   Bankier method; Business register; Dead unit removal; Microstrata; Neyman allocation; Permanent random numbers; Power allocation; Rotation; Sample coordination.

Table of contents

How to cite

Hidiroglou, M.A. (2025). Sampling for business surveys at Statistics Canada. Survey Methodology, 51(2), 341-379. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2025002/article/00013-eng.pdf.

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