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Latent Variable Models Under Misspecification: Two-Stage Least Squares (2SLS) and Maximum Likelihood (ML) Estimators

Author

Listed:
  • Kenneth A. Bollen

    (The University of North Carolina at Chapel Hill, bollen@unc.edu)

  • James B. Kirby

    (Agency for Healthcare Research and Quality, Rockville, MD)

  • Patrick J. Curran

    (The University of North Carolina at Chapel Hill)

  • Pamela M. Paxton

    (The Ohio State University, Columbus)

  • Feinian Chen

    (North Carolina State University, Raleigh)

Abstract

This article compares maximum likelihood (ML) estimation to three variants of two-stage least squares (2SLS) estimation in structural equation models. The authors use models that are both correctly and incorrectly specified. Simulated data are used to assess bias, efficiency, and accuracy of hypothesis tests. Generally, 2SLS with reduced sets of instrumental variables performs similarly to ML when models are correctly specified. Under correct specification, both estimators have little bias except at the smallest sample sizes and are approximately equally efficient. As predicted, when models are incorrectly specified, 2SLS generally performs better, with less bias and more accurate hypothesis tests. Unless a researcher has tremendous confidence in the correctness of his or her model, these results suggest that a 2SLS estimator should be considered.

Suggested Citation

  • Kenneth A. Bollen & James B. Kirby & Patrick J. Curran & Pamela M. Paxton & Feinian Chen, 2007. "Latent Variable Models Under Misspecification: Two-Stage Least Squares (2SLS) and Maximum Likelihood (ML) Estimators," Sociological Methods & Research, , vol. 36(1), pages 48-86, August.
    • Handle: RePEc:sae:somere:v:36:y:2007:i:1:p:48-86
    DOI: 10.1177/0049124107301947
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    References listed on IDEAS

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