Omitted Variables in Multilevel Models

By Jee-Seon Kim, Edward W. Frees. 2006

79 citations recorded by the source corpus.

Subfields: Education, Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/020e6c6c962665d472fc2249c8b977257aea9c1b

How researchers see this paper

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

In Education

1. Francis L. Huang
2. Jee-Seon Kim
3. Edward W. Frees
4. Tom A. B. Snijders
5. Carla Rampichini
6. Leonardo Grilli

In Sociology and Political Science

1. Francis L. Huang
2. Jee-Seon Kim
3. Edward W. Frees
4. Tom A. B. Snijders
5. B. Lancee
6. Christian E. G. Steglich

In Statistics and Probability

1. Francis L. Huang
2. Jee-Seon Kim
3. Edward W. Frees
4. Tom A. B. Snijders
5. Johannes Berkhof
6. Sandip Sinharay
7. Carla Rampichini
8. Leonardo Grilli

Citations in the corpus

• Multilevel Modeling and Ordinary Least Squares Regression: How Comparable Are They? (2018, background): “As a result, standard MLM point estimates can be biased (Castellano, Rabe-Hesketh, & Skrondal, 2014; Kim & Frees, 2006).”
• Multilevel Modeling with Correlated Effects (2007, methodology): “ Kim and Frees (2006) conducted two omitted variable tests using two fixed effects estimators and a random effects estimator.”
• Diagnostic Checks for Multilevel Models (2008, background): “This robustness property is elaborated with a lot of detailed matrix calculus in Kim and Frees [ 32 ].”