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A comparison of mail, telephone, and home interview strategies for household health surveys.

Siemiatycki, J·1979·American Journal of Public Health

Abstract

The method of data collection in household health surveys can be a major determinant of cost and data quality. A survey strategy can comprise mail, telephone, or home interview methods, individually or in combination to follow up non-respondents. The purpose of this study in Montreal was to compare cost and data quality of various strategies. Strategies which began with mail or telephone contact, followed by the two other methods, provided response rates as high as a home interview strategy (all between 80 and 90 per cent), for one-half the cost of home interviews when used as the sole method. The telephone response rate was higher than the mail response rate. Comparing different follow-up approaches to strategies beginning with mail or telephone, it proved less costly, and equally effective, to use home interviewing as a last resort for persistent non-respondents. Validity of response (comparing individual responses with records of a government health insurance data bank) and willingness to answer sensitive questions were greatest in mail strategy.

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Metadata

Authors
Siemiatycki, J.
Venue
American Journal of Public Health
Published
1979-03
Volume
69
Issue
3
Pages
238-245
ISSN
  • 0090-0036 (ISSN-L)
  • 1541-0048
OpenAlex
W2098043510
Cited by
402
References
0
FWCI
51.05
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A comparison of mail, telephone, and home interview strategies for household health surveys. https://app.cochat.ai/papers/10.2105/ajph.69.3.238
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Siemiatycki, J. (1979). A comparison of mail, telephone, and home interview strategies for household health surveys. American Journal of Public Health. https://doi.org/10.2105/ajph.69.3.238

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.