A Note on the Use of Missing Auxiliary Variables in Full Information Maximum Likelihood-Based Structural Equation Models

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Multiple imputation as a flexible tool for missing data handling in clinical research. (2017, background): “…as an auxiliary variable (r's with Wave 1 and Wave 3 depression exceed 0.70), but its utility may be diminished somewhat by missing data (e.g., only 16 cases with missing Wave 1 scores have Wave 2 scores to borrow from, and 57 cases with missing Wave 3 scores have data at Wave 2; Enders, 2008).”
Multiple Imputation of Missing Data for Multilevel Models (2018, unknown): “When more information can be included from auxiliary variables, then missing values can be inferred from the observed data with greater accuracy (for a discussion about the use of auxiliary variables under FIML, see Enders, 2008; Graham, 2003).”
Latent profile analysis: A review and “how to” guide of its application within vocational behavior research (2020, background): “There exist several informative articles (Enders, 2008; Lanza & Cooper, 2016; Schafer & Graham, 2002) and books (Little & Rubin, 2020) on this issue, providing different solutions.”