TY  - JOUR
AB  - In educational psychology, observational units are oftentimes nested within superordinate groups. Researchers need to account for hierarchy in the data by means of multilevel modeling, but especially in three-level longitudinal models, it is often unclear which sample size is necessary for reliable parameter estimation. To address this question, we generated a population dataset based on a study in the field of educational psychology, consisting of 3000 classrooms (level-3) with 55000 students (level-2) measured at 5 occasions (level-1), including predictors on each level and interaction effects. Drawing from this data, we realized 1000 random samples each for various sample and missing value conditions and compared analysis results with the true population parameters. We found that sampling at least 15 level-2 units each in 35 level-3 units results in unbiased fixed effects estimates, whereas higher-level random effects variance estimates require larger samples. Overall, increasing the level-2 sample size most strongly improves estimation soundness. We further discuss how data characteristics influence parameter estimation and provide specific sample size recommendations.
DA  - 2019
DO  - 10.3389/fpsyg.2019.01067
KW  - random effects model
KW  - sample size
KW  - power analysis
KW  - three-level model
KW  - parameter estimation
LA  - eng
PY  - 2019
SN  - 1664-1078
T2  - Frontiers in Psychology
TI  - The Influence of Sample Size on Parameter Estimates in Three-Level Random-Effects Models
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29360200
Y2  - 2024-11-22T02:06:29
ER  -