The Impact of Sample Size and Other Factors When Estimating Multilevel Logistic Models

By J. Schoeneberger. 2016

71 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f458ad0d0b84aeb18a3a49ca269815032f8784dd

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. N. Sommet
2. Davide Morselli
3. Daniel M. McNeish
4. Michael T. Kalkbrenner
5. Andrew F. Hayes
6. Jacob J Coutts
7. W. Stam
8. Markku V. J. Maula

In Statistics and Probability

1. N. Sommet
2. Davide Morselli
3. Daniel M. McNeish
4. Francis L. Huang
5. Milica Miočević
6. R. van de Schoot
7. Laura M. Stapleton
8. M. Botha

Citations in the corpus

• Keep Calm and Learn Multilevel Logistic Modeling: A Simplified Three-Step Procedure Using Stata, R, Mplus, and SPSS (2017, background): “More to the point, in multilevel logistic modeling, Schoeneberger (2016) showed that a minimum of 50 level-1 units and 40 level-2 units are needed to accurately estimate small fixed effects (set at OR = 1.70) when intercept variance is small (set at var(u0j) ≈ 0.1), whereas 100 level-1 units and 80…”
• On Using Bayesian Methods to Address Small Sample Problems (2016, background): “With smaller samples, out of bounds solutions are a frequent cause for nonconvergence with ML (Depaoli & Clifton, 2015; Schoeneberger, 2015).”