Power Computations for Polynomial Change Models in Block-Randomized Designs

Y

21

Research Design; Statistical Analysis; Sample Size; Effect Size; Computation; Measurement; Growth Models; Educational Experiments; Hierarchical Linear Modeling

10.1080/00220973.2018.1496057

0022-0973

Education experiments frequently assign students to treatment or control conditions within schools. Longitudinal components added in these studies (e.g., students followed over time) allow researchers to assess treatment effects in average rates of change (e.g., linear or quadratic). We provide methods for a priori power analysis in three-level polynomial change models for block-randomized designs. We discuss unconditional models and models with covariates at the second and third level. We illustrate how power is influenced by the number of measurement occasions, the sample sizes at the second and third levels, and the covariates at the second and third levels.

As Provided

2019

EJ1222726