Estimation of Random Coefficient Multilevel Models in the Context of Small Numbers of Level 2 Clusters
Bibliographic Data
| ID | 20286086 |
|---|---|
| Authors | Jocelyn H Bolin (Ball State University, Muncie, IN, USA, corresponding author), W Holmes Finch (0000-0003-0393-2906, Ball State University, Muncie, IN, USA), Rachel Stenger (0000-0003-0643-198X, Ball State University, Muncie, IN, USA) |
| Year | 2019 |
| Volume | 79 |
| Issue | 2 |
| Pages | 217-248 |
| Publication date | 2019-04-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational and Psychological Measurement (JOURNAL) |
| Journal identifiers | ISSN: 0013-1644 • E-ISSN: 1552-3888 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/0013164418773494 |
| PMID | 30911191 |
| OpenAlex | W2801157135 |
| Language | EN |
| Citations received | 3 |
| References cited | 24 |
Multilevel data are a reality for many disciplines. Currently, although multiple options exist for the treatment of multilevel data, most disciplines strictly adhere to one method for multilevel data regardless of the specific research design circumstances. The purpose of this Monte Carlo simulation study is to compare several methods for the treatment of multilevel data specifically when there is random coefficient variation in small samples. The methods being compared are fixed effects modeling (the industry standard in business and managerial sciences), multilevel modeling using restricted maximum likelihood (REML) estimation (the industry standard in the social and behavioral sciences), multilevel modeling using the Kenward–Rogers correction, and Bayesian estimation using Markov Chain Monte Carlo. Results indicate that multilevel modeling does have an advantage over fixed effects modeling when Level 2 slope parameter variance exists. Bayesian estimation of multilevel effects can be advantageous over traditional multilevel modeling using REML, but only when prior probabilities are correctly specified. Results are presented in terms of Type I error, power, parameter estimation bias, empirical parameter estimate standard error, and parameter 95% coverage rates, and recommendations are presented
Context (archaeology) · Econometrics · Estimation · Geography · Meta-analysis · Multilevel model · Random effects model · Statistics · Engineering · Mathematics · Medicine · Random Matrices and Applications · Statistical Methods and Bayesian Inference · Statistical Methods and Inference
A skills network approach to physicians’ competence in shared decision making
Methodological and Statistical Practices of Using Symptom Networks to Evaluate Mental Health Interventions
Motivation states to move, be physically active and sedentary vary like circadian rhythms and are associated with affect and arousal
Fixed Effects Regression Models
Multilevel Analysis
On the unnecessary ubiquity of hierarchical linear modeling.
Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)
An Approximate Distribution of Estimates of Variance Components
A comparison of Bayesian and likelihood-based methods for fitting multilevel models
Alternatives to Multilevel Modeling for the Analysis of Clustered Data
Sufficient Sample Sizes for Multilevel Modeling
Hierarchical linear models
Extensions of Mantel–Haenszel for Multilevel DIF Detection
Modeling Clustered Data with Very Few Clusters
Fixed-effect or Random-effect Models
Accurate, inaccurate, or biased teacher expectations
| Unique citing works | 3 |
|---|---|
| Citations per year | 0.5 |
| Citation span | 2020 - 2024 (5) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 3 |