Toward best practices in analyzing datasets with missing data: Comparisons and recommendations

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BHPMF – a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography (2015, background): “Many disciplines, for example psychology (e.g. Schafer & Graham, 2002), sociology (e.g. Johnson & Young, 2011) or biogeochemistry (e.g. Moffat et al., 2007; Lasslop et al., 2010) have developed their own set of accepted techniques for predicting missing values tailored to their data structure.”
THE DEVELOPMENT OF SELF-DETERMINATION ACROSS THE LANGUAGE COURSE (2019, result): “FIML produces less biased results than listwise deletion, pairwise deletion, or mean substitution, and produces comparable results to multiple imputation (Johnson & Young, 2011).”
Multiple Imputation of missing values in exploratory factor analysis of multidimensional scales : estimating latent trait scores (2016, background): “For example, in a recent study on marital happiness, Johnson and Young (2011) observed that the percentage of missing responses was highest for questions about sexual behavior (23”