Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-19T09:09:48.170Z Has data issue: false hasContentIssue false
  • English
  • Français

The socio-economic patterning of survey participation and non-response error in a multilevel study of food purchasing behaviour: area- and individual-level characteristics

Published online by Cambridge University Press:  02 January 2007

Gavin Turrell ,
Carla Patterson ,
Brian Oldenburg ,
Trish Gould  and
Marie-Andree Roy
Gavin Turrell*
Affiliation:
Queensland University of Technology, School of Public Health, Victoria Park Road, Kelvin Grove, Brisbane, Queensland 4059, Australia
Carla Patterson
Affiliation:
Queensland University of Technology, School of Public Health, Victoria Park Road, Kelvin Grove, Brisbane, Queensland 4059, Australia
Brian Oldenburg
Affiliation:
Queensland University of Technology, School of Public Health, Victoria Park Road, Kelvin Grove, Brisbane, Queensland 4059, Australia
Trish Gould
Affiliation:
Queensland University of Technology, School of Public Health, Victoria Park Road, Kelvin Grove, Brisbane, Queensland 4059, Australia
Marie-Andree Roy
Affiliation:
Queensland University of Technology, School of Public Health, Victoria Park Road, Kelvin Grove, Brisbane, Queensland 4059, Australia
*
*Corresponding author: Email [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Objective:

To undertake an assessment of survey participation and non-response error in a population-based study that examined the relationship between socio-economic position and food purchasing behaviour.

Design and setting:

The study was conducted in Brisbane City (Australia) in 2000. The sample was selected using a stratified two-stage cluster design. Respondents were recruited using a range of strategies that attempted to maximise the involvement of persons from disadvantaged backgrounds: respondents were contacted by personal visit and data were collected using home-based face-to-face interviews; multiple call-backs on different days and at different times were used; and a financial gratuity was provided.

Participants:

Non-institutionalised residents of private dwellings (n = 1003), located in 50 small areas that differed in their socio-economic characteristics.

Results:

Rates of survey participation – measured by non-contacts, exclusions, dropped cases, response rates and completions – were similar across areas, suggesting that residents of socio-economically advantaged and disadvantaged areas were equally likely to be recruited. Individual-level analysis, however, showed that respondents and non-respondents differed significantly in their sociodemographic and food purchasing characteristics: non-respondents were older, less educated and exhibited different purchasing behaviours. Misclassification bias probably accounted for the inconsistent pattern of association between the area- and individual-level results. Estimates of bias due to non-response indicated that although respondents and non-respondents were qualitatively different, the magnitude of error associated with this differential was minimal.

Conclusions:

Socio-economic position measured at the individual level is a strong and consistent predictor of survey non-participation. Future studies that set out to examine the relationship between socio-economic position and diet need to adopt sampling strategies and data collection methods that maximise the likelihood of recruiting participants from all points on the socio-economic spectrum, and particularly persons from disadvantaged backgrounds. Study designs that are not sensitive to the difficulties associated with recruiting a socio-economically representative sample are likely to produce biased estimates (underestimates) of socio-economic differences in the dietary outcome being investigated.

Keywords

Survey participationNon-response errorSocio-economic positionFood purchasing
Type
Research Article
Information
Public Health Nutrition , Volume 6 , Issue 2 , April 2003 , pp. 181 - 189
Copyright
Copyright © CABI Publishing 2003

References

1Locker, D. Response and non-response bias in oral health surveys. J. Public Health Dent. 2000; 60: 72–81.CrossRefGoogle Scholar
2Andersen, R, Kasper, J, Frankel, MR, eds. Total Survey Error.San Francisco CA:, Jossey-Bass, 1979.Google Scholar
3Groves, RM, Couper, MP. Non-response in Household Interview Surveys. New York: Wiley, 1998.CrossRefGoogle Scholar
4Groves, RM. Survey Errors and Survey Costs. New York: Wiley, 1989.CrossRefGoogle Scholar
5Dillman, DA. The design and administration of mail surveys. Annu. Rev. Sociol. 1991; 17: 225–49.CrossRefGoogle Scholar
6Locker, D. Effects of non-response on estimates derived from an oral health survey of older adults. Community Dent. Oral Epidemiol. 1993; 21: 108–13.CrossRefGoogle ScholarPubMed
7Turrell, G. Income non-reporting: implications for health inequalities research. J. Epidemiol. Community Health 2000; 54: 207–14.CrossRefGoogle ScholarPubMed
8Turrell, G, Najman, JM. Collecting food-related data from low socioeconomic groups: how adequate are our research designs? Aust. J. Public Health 1995; 19: 410–6.CrossRefGoogle ScholarPubMed
9Australian Bureau of Statistics. 1996 Census of Population and Housing: Socioeconomic Indexes for Areas. Information Paper, Catalogue No. 2039.0. Canberra: Australian Bureau of Statistics, 1998.Google Scholar
10National Health and Medical Research Council. Dietary Guidelines for Australians. Canberra: Australian Government Publishing Service, 1991.Google Scholar
11Research Triangle Institute. SUDAAN User's Manual, Release 8.0. Research Triangle Park, NC: Research Triangle Institute, 2001.Google Scholar
12Hoeymans, N, Feskens, EJM, Van Den Bos, GAM, Kromhout, D. Non-response bias in a study of cardiovascular diseases, functional status and self-rated health among elderly men. Age and Ageing 1998; 27: 35–40.CrossRefGoogle Scholar
13Blalock, HM. Conceptualization and Measurement in the Social Sciences. Beverley Hills CA:, Sage, 1989.Google Scholar
14Macintyre, S, Ellaway, A. Ecological approaches: rediscovering the role of the physical and social environment. In: Berkman, LF, Kawachi, I, eds. Social Epidemiology. New York: Oxford University Press, 2000; 332–48.CrossRefGoogle Scholar
15Roberts, PJ, Roberts, C, Sibbald, B, Torgerson, DJ. The effect of a direct payment or a lottery on questionnaire response rates: a randomised controlled trial. J. Epidemiol. Community Health 2000; 54: 71–2.CrossRefGoogle ScholarPubMed
16Hill, A, Roberts, J, Ewings, P, Gunnell, D. Non-response bias in a lifestyle survey. J. Public Health Med. 1997; 19: 203–7.CrossRefGoogle Scholar
17O'Neill, TW, Marsden, D, Silman, AJ and the European Vertebral Osteoporosis Study Group. Differences in the characteristics of responders and non-responders in a prevalence survey of vertebral osteoporosis. Osteoporosis Int. 1995; 5: 327–34.CrossRefGoogle Scholar
18Van den Akker, M, Buntinx, F, Metsemakers, JFM, Knottnerus, JA. Morbidity in responders and non-responders in a register-based population survey. Fam. Pract. 1998; 15: 261–3.CrossRefGoogle Scholar
19Sheikh, K, Mattingly, S. Investigating non-response bias in mail surveys. J. Epidemiol. Community Health 1981; 35: 293–6.CrossRefGoogle ScholarPubMed
20Melton, LJ, Dyck, PJ, Karnes, JL, O'Brien, PC, Service, FJ. Non-response bias in studies of diabetic complications: the Rochester Diabetic Neuropathy Study. J. Clin. Epidemiol. 1993; 46: 341–8.CrossRefGoogle ScholarPubMed
21Hyndman, JCG, Holman, CD, Hockey, RL, Donovan, RJ, Corti, B, Rivera, J. Misclassification of social disadvantage based on geographical areas: comparison of postcode and collector's districts analyses. Int. J. Epidemiol. 1995; 24: 165–76.CrossRefGoogle ScholarPubMed
22Turrell, G. Determinants of healthy food choice in a population-based sample. Am. J. Health Behav. 1998; 22: 342–57.Google Scholar
23Patterson, BH, Block, G. Food choices and the cancer guidelines. Am. J. Public Health 1988; 78: 282–6.CrossRefGoogle ScholarPubMed
24Turrell, G. Determinants of gender differences in dietary behavior. Nutr. Res. 1997; 17: 1105–20.CrossRefGoogle Scholar
25Shimakawa, T, Sorlie, P, Carpenter, MA, Dennis, B, Tell, GS, Watson, R, et al. Dietary intake patterns and sociodemographic factors in the Atherosclerosis Risk in Communities Study. Prev. Med. 1994; 23: 769–80.CrossRefGoogle ScholarPubMed
26Worsley, A, Crawford, D. Who complies with the Australian Dietary Guidelines?. Nutr. Res. 1986; 6: 29–34.CrossRefGoogle Scholar
27Turrell, G. Compliance with the Australian Dietary Guidelines: have population-based health promotion messages been effective? Nutr. Health 1997; 11: 271–88.CrossRefGoogle Scholar