Missing Data Imputation versus Full Information Maximum Likelihood with Second-Level Dependencies

By Ross Larsen. 2011

248 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9a73103bd677443c55c67abd6b10b0667e9d14a1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Alexandre J. S. Morin
2. Benjamin Nagengast
3. A. Arens
4. Herbert W. Marsh
5. Francesca Scalas
6. John P. Meyer
7. Laryssa Topolnytsky
8. C. Kam

In Statistics and Probability

1. Alexandre J. S. Morin
2. Benjamin Nagengast
3. Laryssa Topolnytsky
4. Francesca Scalas
5. A. Arens
6. Herbert W. Marsh
7. C. Kam
8. John P. Meyer

Citations in the corpus

• Relations between Teachers' Emotional Exhaustion and Students' Educational Outcomes. (2016, methodology): “Moreover, FIML has been found to perform generally better than more computationally intensive multiple imputation procedures in the context of multilevel analyses (e.g., Larsen, 2011).”
• Are Commitment Profiles Stable and Predictable? A Latent Transition Analysis (2016, background): “…of missing data (e.g., 50%), in the context of longitudinal studies with missing time points, under missing at random assumptions, and even to violations of these assumptions in some cases (e.g., Enders, 2001, 2010; Enders & Bandalos, 2001; Graham, 2009; Larsen, 2011; Shin, Davidson, & Long, 2009).”
• Doubly Latent Multilevel Analyses of Classroom Climate: An Illustration (2014, background): “%) under missing-at-random assumptions, and even in some cases to violations of this assumption (e.g., Enders, 2001, 2010; Enders & Bandalos, 2001; Graham, 2009; Larsen, 2011; Shin, Davidson, & Long, 2009).”