Reducing bias due to systematic attrition in longitudinal studies: The benefits of multiple imputation

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How to handle missing data: A comparison of different approaches (2015, unknown): “Previous research has shown that predictive mean matching is able to handle many types of data and does not directly depend on any distributional assumptions (Vink, Frank, Pannekoek, & Van Buuren, 2014) and can be used to prevent bias due to attrition in longitudinal designs (Asendorpf et al., 2014)”
Does employees’ spirituality enhance job performance? The mediating roles of intrinsic motivation and job crafting (2020, methodology): “FIML estimation is superior to other imputation techniques, including listwise deletion, previous studies having found that the exclusion of missing cases (i.e., listwise deletion) can lead to biased results (Asendorpf et al. 2014).”
A practical guide to the analysis of non-response and attrition in longitudinal research using a real data example (2018, methodology): “…baseline participation 2. participation in wave 5 3. drop-out in the wave 1–3 period 4. drop-out in the wave 5–7 period We analyzed parent and child participation separately given the previous evidence that patterns of participation may differ across these groups (Asendorpf et al., 2014).”
The Relationship of Coworker Incivility to Job Performance and the Moderating Role of Self-Efficacy and Compassion at Work: The Job Demands-Resources (JD-R) Approach (2017, background): “, listwise deletion) can lead to biased results (Asendorpf et al. 2014).”