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Normal Theory Two-Stage ML Estimator When Data Are Missing at the Item Level

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  • Victoria Savalei

    (University of British Columbia)

  • Mijke Rhemtulla

    (University of California, Davis)

Abstract

In many modeling contexts, the variables in the model are linear composites of the raw items measured for each participant; for instance, regression and path analysis models rely on scale scores, and structural equation models often use parcels as indicators of latent constructs. Currently, no analytic estimation method exists to appropriately handle missing data at the item level. Item-level multiple imputation (MI), however, can handle such missing data straightforwardly. In this article, we develop an analytic approach for dealing with item-level missing data—that is, one that obtains a unique set of parameter estimates directly from the incomplete data set and does not require imputations. The proposed approach is a variant of the two-stage maximum likelihood (TSML) methodology, and it is the analytic equivalent of item-level MI. We compare the new TSML approach to three existing alternatives for handling item-level missing data: scale-level full information maximum likelihood, available-case maximum likelihood, and item-level MI. We find that the TSML approach is the best analytic approach, and its performance is similar to item-level MI. We recommend its implementation in popular software and its further study.

Suggested Citation

  • Victoria Savalei & Mijke Rhemtulla, 2017. "Normal Theory Two-Stage ML Estimator When Data Are Missing at the Item Level," Journal of Educational and Behavioral Statistics, , vol. 42(4), pages 405-431, August.
    • Handle: RePEc:sae:jedbes:v:42:y:2017:i:4:p:405-431
    DOI: 10.3102/1076998617694880
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    References listed on IDEAS

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    1. Rosseel, Yves, 2012. "lavaan: An R Package for Structural Equation Modeling," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i02).
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