Treatment Of Missing Data At The Second Level Of Hierarchical Linear Models

By N. Gibson, Stephen Olejnik. 2003

61 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/cbcb91ef538b9ae55e71b1f5280acb1f3fe7dd72

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. O. Lüdtke
2. Alexander Robitzsch
3. Stephen R. Porter
4. Mike W.-L. Cheung
5. S. Grund
6. Kristopher J Preacher
7. Alexandre J. S. Morin
8. Daniel M. McNeish

In Statistics and Probability

1. Alexander Robitzsch
2. Stephen R. Porter
3. Mike W.-L. Cheung
4. O. Lüdtke
5. S. Grund
6. Paul Michel Bernard Verhaeghen
7. Timo Gnambs
8. Francis L. Huang

Citations in the corpus

• INSTITUTIONAL STRUCTURES AND STUDENT ENGAGEMENT (2006, methodology): “Recent simulations using the High School and Beyond survey indicate that listwise deletion at the group level is a better strategy than imputation for multilevel models (Gibson and Olejnik, 2003).”
• Comparison of Methods of Handling Missing Time-Invariant Covariates in Latent Growth Models Under the Assumption of Missing Completely at Random (2007, methodology): “Gibson and Olejnik (2003) compared several methods of handling level-2 missing data in the context of cross-sectional MLM.”
• Multiple Imputation of Missing Data for Multilevel Models (2018, methodology): “…the methodological literature has focused so far on ad hoc procedures, for example, separate imputation of individual- and group-level variables (Gibson & Olejnik, 2003) or “flat file” imputation using single-level MI (Cheung, 2007; for an overview, see Hox et al., 2016; van Buuren, 2011).”