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On the Asymptotic Relative Efficiency of Planned Missingness Designs

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  • Mijke Rhemtulla
  • Victoria Savalei
  • Todd Little

Abstract

In planned missingness (PM) designs, certain data are set a priori to be missing. PM designs can increase validity and reduce cost; however, little is known about the loss of efficiency that accompanies these designs. The present paper compares PM designs to reduced sample (RN) designs that have the same total number of data points concentrated in fewer participants. In 4 studies, we consider models for both observed and latent variables, designs that do or do not include an “X set” of variables with complete data, and a full range of between- and within-set correlation values. All results are obtained using asymptotic relative efficiency formulas, and thus no data are generated; this novel approach allows us to examine whether PM designs have theoretical advantages over RN designs removing the impact of sampling error. Our primary findings are that (a) in manifest variable regression models, estimates of regression coefficients have much lower relative efficiency in PM designs as compared to RN designs, (b) relative efficiency of factor correlation or latent regression coefficient estimates is maximized when the indicators of each latent variable come from different sets, and (c) the addition of an X set improves efficiency in manifest variable regression models only for the parameters that directly involve the X-set variables, but it substantially improves efficiency of most parameters in latent variable models. We conclude that PM designs can be beneficial when the model of interest is a latent variable model; recommendations are made for how to optimize such a design. Copyright The Psychometric Society 2016

Suggested Citation

  • Mijke Rhemtulla & Victoria Savalei & Todd Little, 2016. "On the Asymptotic Relative Efficiency of Planned Missingness Designs," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 60-89, March.
    • Handle: RePEc:spr:psycho:v:81:y:2016:i:1:p:60-89
    DOI: 10.1007/s11336-014-9422-0
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    References listed on IDEAS

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    1. A. Mooijaart & P.M. Bentler, 1991. "Robustness of normal theory statistics in structural equation models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 45(2), pages 159-171, June.
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    Cited by:

    1. Josephine Wood & Gregory J. Matthews & Jennifer Pellowski & Ofer Harel, 2019. "Comparing Different Planned Missingness Designs in Longitudinal Studies," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(2), pages 226-250, December.

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