Related Experiment Video
Updated: Aug 7, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Comparing random effects models, ordinary least squares, or fixed effects with cluster robust standard errors for
Young Ri Lee1, James E Pustejovsky2
1Department of Educational Psychology, University of Texas at Austin.
Cross-classified random effects modeling (CCREM) is common, but ordinary least squares regression with cluster robust variance estimators (OLS-CRVE) or fixed effects regression with CRVE (FE-CRVE) may be better. FE-CRVE is recommended when CCREM assumptions are uncertain.
Area of Science:
- Statistics
- Psychology
- Education Research
Background:
- Cross-classified random effects modeling (CCREM) is widely used for complex data structures.
- Alternative methods like OLS-CRVE and FE-CRVE offer potential advantages due to weaker assumptions.
Purpose of the Study:
- To compare the performance of CCREM, OLS-CRVE, and FE-CRVE.
- To evaluate these methods under various assumption violations, including homoscedasticity, exogeneity, and unmodeled random slopes.
Main Methods:
- A Monte Carlo simulation study was employed.
- The study systematically varied conditions related to homoscedasticity, exogeneity, and random slopes.
Main Results:
- CCREM performed best when all its assumptions were met.
- OLS-CRVE and FE-CRVE showed comparable or superior performance when homoscedasticity was violated.
- FE-CRVE demonstrated adequate performance when exogeneity was violated, and both OLS-CRVE and FE-CRVE yielded more accurate inferences with unmodeled random slopes.
Conclusions:
- Two-way FE-CRVE is a robust alternative to CCREM.
- FE-CRVE is particularly recommended when the assumptions of CCREM (homoscedasticity, exogeneity) may not hold.
More Related Videos
12:27Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
14:14The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
Published on: May 13, 2022
Related Concept Videos
Mechanistic Models: Compartment Models in Individual and Population Analysis
Randomized Experiments
Simple randomization
Simple...
Comparing the Survival Analysis of Two or More Groups
Cluster Sampling Method
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Friedman Two-way Analysis of Variance by Ranks
One-Way ANOVA: Equal Sample Sizes
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...