Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2025-01-05T02:22:21.462Z Has data issue: false hasContentIssue false
  • English
  • Français

Self-Selection Biases in Correlational Studies Based on Questionnaires

Published online by Cambridge University Press:  01 January 2025

Roger J. Bowden
Get access Rights & Permissions [Opens in a new window]

Abstract

It is commonly held that even where questionnaire response is poor, correlational studies are affected only by loss of degrees of freedom or precision. We show that this supposition is not true. If the decision to respond is correlated with a substantive variable of interest, then regression or analysis of variance methods based upon the questionnaire results may be adversely affected by self-selection bias. Moreover such bias may arise even where response is 100%. The problem in both cases arises where selection information is passed to the score indirectly via the disturbance or individual effects, rather than entirely via the observable explanatory variables. We suggest tests for the ensuing self-selection bias and possible ways of handling the ensuing problems of inference.

Keywords

questionnairessurveysselection biascategorizationscorrelationanalysis of varianceinstrumental variables
Type
Original Paper
Information
Psychometrika , Volume 51 , Issue 2 , June 1986 , pp. 313 - 325
Copyright
Copyright © 1986 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

The author would like to thank members of the Psychology Department at UWA for references on the illustrative example, namely the effects of sex-stereotyping on achievements in mathematics; thanks are also due to the anonymous reviewers whose comments resulted in material improvements to the paper.

References

Aitken, A. C. (1934). Note on selection from a multivariate normal population. Proceedings of the Edinburgh Mathematical Society, 4, 106–110.CrossRefGoogle Scholar
Bock, R. D., Jones, L. V. (1968). The Measurement and prediction of judgement and choice, San Francisco: Holden-Day.Google Scholar
Bowden, R. J., Turkington, D. A. (1984). Instrumental variables, New York: Cambridge University Press.Google Scholar
Cox, D. R., Hinkley, D. V. (1974). Theoretical statistics, London: Chapman and Hall.CrossRefGoogle Scholar
Cramèr, H. (1961). Mathematical methods of statistics, Princeton, NJ: Princeton University Press.Google Scholar
Deem, R. (1978). Women and schooling, Norfolk: Routledge and Kegan Paul.Google Scholar
Gronau, R. (1974). Wage comparisons—A selectivity bias. Journal of Political Economy, 82, 1119–1143.CrossRefGoogle Scholar
Gulliksen, H. (1950). Theory of mental tests, New York: J. Wiley.CrossRefGoogle Scholar
Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46, 1251–1270.CrossRefGoogle Scholar
Heckman, J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153–161.CrossRefGoogle Scholar
Lawley, D. N. (1943). A note on Karl Pearson's selection formulae. Proceedings of the Royal Society of Edinburgh: Section A, 62, 28–30.Google Scholar
Meredith, W. (1964). Notes on factorial invariance. Psychometrika, 29, 177–185.CrossRefGoogle Scholar
Skolnick, J. A., Langbort, L., Day, A. T. (1982). How to encourage girls in maths and science, New Jersey: Prentice-Hall.Google Scholar
Tobias, S. (1978). Overcoming maths anxiety, Boston: Houghton, Mifflin and Co..Google Scholar