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Missing data methods for arbitrary missingness with small samples

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  • Daniel McNeish

Abstract

Missing data are a prevalent and widespread data analytic issue and previous studies have performed simulations to compare the performance of missing data methods in various contexts and for various models; however, one such context that has yet to receive much attention in the literature is the handling of missing data with small samples, particularly when the missingness is arbitrary. Prior studies have either compared methods for small samples with monotone missingness commonly found in longitudinal studies or have investigated the performance of a single method to handle arbitrary missingness with small samples but studies have yet to compare the relative performance of commonly implemented missing data methods for small samples with arbitrary missingness. This study conducts a simulation study to compare and assess the small sample performance of maximum likelihood, listwise deletion, joint multiple imputation, and fully conditional specification multiple imputation for a single-level regression model with a continuous outcome. Results showed that, provided assumptions are met, joint multiple imputation unanimously performed best of the methods examined in the conditions under study.

Suggested Citation

  • Daniel McNeish, 2017. "Missing data methods for arbitrary missingness with small samples," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(1), pages 24-39, January.
  • Handle: RePEc:taf:japsta:v:44:y:2017:i:1:p:24-39
    DOI: 10.1080/02664763.2016.1158246
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    References listed on IDEAS

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    1. Honaker, James & King, Gary & Blackwell, Matthew, 2011. "Amelia II: A Program for Missing Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i07).
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    3. Tan, Ming & Fang, Hong-Bin & Tian, Guo-Liang & Wei, Gang, 2005. "Testing multivariate normality in incomplete data of small sample size," Journal of Multivariate Analysis, Elsevier, vol. 93(1), pages 164-179, March.
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    Cited by:

    1. Nils Grashof, 2019. "Firm-specific cluster effects - A meta-analysis," Bremen Papers on Economics & Innovation 1906, University of Bremen, Faculty of Business Studies and Economics.
    2. Nils Grashof, 2020. "Firm‐specific cluster effects: A meta‐analysis," Papers in Regional Science, Wiley Blackwell, vol. 99(5), pages 1237-1260, October.
    3. Hazen, Katherine P. & Carlson, Matthew W. & Hatton-Bowers, Holly & Fessinger, Melanie B. & Cole-Mossman, Jennie & Bahm, Jamie & Hauptman, Kelli & Brank, Eve M. & Gilkerson, Linda, 2020. "Evaluating the Facilitating Attuned Interactions (FAN) approach: Vicarious Trauma, professional Burnout, and reflective practice," Children and Youth Services Review, Elsevier, vol. 112(C).

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