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A Multimethod Latent State-Trait Model for Structurally Different And Interchangeable Methods

Author

Listed:
  • Tobias Koch

    (Leuphana Universität Lüneburg)

  • Martin Schultze

    (Freie Universität Berlin)

  • Jana Holtmann

    (Freie Universität Berlin)

  • Christian Geiser

    (Utah State University)

  • Michael Eid

    (Freie Universität Berlin)

Abstract

A new multiple indicator multilevel latent state-trait (LST) model for the analysis of multitrait–multimethod–multioccasion (MTMM-MO) data is proposed. The LST-COM model combines current CFA-MTMM modeling approaches of interchangeable and structurally different methods and LST modeling approaches. The model enables researchers to specify construct and method factors on the level of time-stable (trait) as well as time-variable (occasion-specific) latent variables and analyze the convergent and discriminant validity among different rater groups across time. The statistical performance of the model is scrutinized by a simulation study and guidelines for empirical applications are provided.

Suggested Citation

  • Tobias Koch & Martin Schultze & Jana Holtmann & Christian Geiser & Michael Eid, 2017. "A Multimethod Latent State-Trait Model for Structurally Different And Interchangeable Methods," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 17-47, March.
    • Handle: RePEc:spr:psycho:v:82:y:2017:i:1:d:10.1007_s11336-016-9541-x
    DOI: 10.1007/s11336-016-9541-x
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    References listed on IDEAS

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    4. Steele, Fiona, 2008. "Multilevel models for longitudinal data," LSE Research Online Documents on Economics 52203, London School of Economics and Political Science, LSE Library.
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    6. Michael Eid, 2000. "A multitrait-multimethod model with minimal assumptions," Psychometrika, Springer;The Psychometric Society, vol. 65(2), pages 241-261, June.
    7. Tobias Koch & Martin Schultze & Jeremy Burrus & Richard D. Roberts & Michael Eid, 2015. "A Multilevel CFA-MTMM Model for Nested Structurally Different Methods," Journal of Educational and Behavioral Statistics, , vol. 40(5), pages 477-510, October.
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