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Interpretational Confounding of Unobserved Variables in Structural Equation Models

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  • Ronald S. Burt

    (University of California, Berkeley)

Abstract

The problem of assigning empirical meaning to unobserved variables in structural equation models is discussed. Interpretational confounding is discussed as the assignment of the other than a priori assigned empirical meaning of an unobserved variable. Hypotheses conceming the possibility of interpretational confounding as a concomitant of a lack of point variability in unobserved variables are specified, and corresponding chi-square statistics are given. Numerical illustration is provided

Suggested Citation

  • Ronald S. Burt, 1976. "Interpretational Confounding of Unobserved Variables in Structural Equation Models," Sociological Methods & Research, , vol. 5(1), pages 3-52, August.
    • Handle: RePEc:sae:somere:v:5:y:1976:i:1:p:3-52
    DOI: 10.1177/004912417600500101
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    References listed on IDEAS

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    1. R. Bock & Rolf Bargmann, 1966. "Analysis of covariance structures," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 507-534, December.
    2. Griliches, Zvi, 1974. "Errors in Variables and Other Unobservables," Econometrica, Econometric Society, vol. 42(6), pages 971-998, November.
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    Cited by:

    1. Derrick Neufeld & Yulin Fang & Zeying Wan, 2013. "Community of Practice Behaviors and Individual Learning Outcomes," Group Decision and Negotiation, Springer, vol. 22(4), pages 617-639, July.
    2. Roy D. Howell, 2013. "Conceptual clarity in measurement—Constructs, composites, and causes: a commentary on Lee, Cadogan and Chamberlain," AMS Review, Springer;Academy of Marketing Science, vol. 3(1), pages 18-23, March.
    3. Zsuzsa Bakk & Jouni Kuha, 2018. "Two-Step Estimation of Models Between Latent Classes and External Variables," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 871-892, December.
    4. Nick Lee & John W. Cadogan & Laura Chamberlain, 2014. "Material and efficient cause interpretations of the formative model: resolving misunderstandings and clarifying conceptual language," AMS Review, Springer;Academy of Marketing Science, vol. 4(1), pages 32-43, June.
    5. Nick Lee & John W. Cadogan & Laura Chamberlain, 2013. "The MIMIC model and formative variables: problems and solutions," AMS Review, Springer;Academy of Marketing Science, vol. 3(1), pages 3-17, March.
    6. Bakk, Zsuzsa & Kuha, Jouni, 2018. "Two-step estimation of models between latent classes and external variables," LSE Research Online Documents on Economics 85161, London School of Economics and Political Science, LSE Library.

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