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Reducing Bias in Estimates of Linear Models by Remeasurement of a Random Subsample

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  • PAUL D. ALLISON

    (University of Pennsylvania)

  • ROBERT M. HAUSER

    (University of Wisconsin)

Abstract

Measurement error in independent variables produces biased estimates of the coefficients in linear models. These biases can be reduced by obtaining repeated measurements of the variables and then estimating structural equation models with multiple indicators of latent variables. Remeasurement is usually costly, however, raising the question of whether the same benefits can be obtained by remeasuring only a fraction of the sample. Although this strategy has been tried previously, there were no appropriate statistical methods for combining the data in the remeasured subsample and the single measurement subsample. We demonstrate here how recently developed methods for incomplete data provide an attractive solution to this estimation problem. The methodology is illustrated by a reanalysis of Bielby, Hauser, and Featherman's (1977a) study of the OCG-II data.

Suggested Citation

  • Paul D. Allison & Robert M. Hauser, 1991. "Reducing Bias in Estimates of Linear Models by Remeasurement of a Random Subsample," Sociological Methods & Research, , vol. 19(4), pages 466-492, May.
    • Handle: RePEc:sae:somere:v:19:y:1991:i:4:p:466-492
    DOI: 10.1177/0049124191019004002
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    References listed on IDEAS

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    1. Bengt Muthén & David Kaplan & Michael Hollis, 1987. "On structural equation modeling with data that are not missing completely at random," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 431-462, September.
    2. P. Bentler & David Weeks, 1980. "Linear structural equations with latent variables," Psychometrika, Springer;The Psychometric Society, vol. 45(3), pages 289-308, September.
    3. Bowles, Samuel, 1972. "Schooling and Inequality from Generation to Generation," Journal of Political Economy, University of Chicago Press, vol. 80(3), pages 219-251, Part II, .
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