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Lorah, Julie Ann – AERA Online Paper Repository, 2018
The Bayesian information criterion (BIC) can be useful for model selection within multilevel modeling studies. However, the formula for BIC requires a value for N, which is unclear in multilevel models, since N is observed in at least two levels. The present study uses simulated data to evaluate the rate of false positives and power when using a…
Descriptors: Bayesian Statistics, Hierarchical Linear Modeling, Computation, Statistical Analysis
Uanhoro, James Ohisei; O'Connell, Ann A. – AERA Online Paper Repository, 2018
There have been increasing calls for applied researchers to see and utilize effect sizes as the primary outcomes of their research. However, this sometimes places a methodological burden on researchers whose primary interests are substantive. Motivated by a desire to help applied researchers better report effect sizes and their confidence…
Descriptors: Effect Size, Computation, Statistical Analysis, Hierarchical Linear Modeling
Shen, Zuchao; Kelcey, Benjamin; Cox, Kyle T.; Zhang, Jiaqi – AERA Online Paper Repository, 2017
Recent studies show cluster randomized trials may be well powered to detect mediation or indirect effects in multilevel settings. However, literature has rarely provided guidance on designing cluster-randomized trials aim to assess indirect effects. In this study, we developed closed-form expression to estimate the variance of and the statistical…
Descriptors: Randomized Controlled Trials, Research Design, Context Effect, Statistical Analysis
Joo, Seang-hwane; Wang, Yan; Ferron, John M. – AERA Online Paper Repository, 2017
Multiple-baseline studies provide meta-analysts the opportunity to compute effect sizes based on either within-series comparisons of treatment phase to baseline phase observations, or time specific between-series comparisons of observations from those that have started treatment to observations of those that are still in baseline. The advantage of…
Descriptors: Meta Analysis, Effect Size, Hierarchical Linear Modeling, Computation
Zigler, Christina K.; Ye, Feifei – AERA Online Paper Repository, 2016
Mediation in multi-level data can be examined using conflated multilevel modeling (CMM), unconflated multilevel modeling (UMM), or multilevel structural equation modeling (MSEM). A Monte Carlo study was performed to compare the three methods on bias, type I error, and power in a 1-1-1 model with random slopes. The three methods showed no…
Descriptors: Hierarchical Linear Modeling, Structural Equation Models, Monte Carlo Methods, Statistical Bias
Li, Chen; Jiao, Hong – AERA Online Paper Repository, 2016
Growth modeling has been of interest in many assessment programs, including both highstakes and low-states tests. Growth could be modeled using different approaches. This study models growth with an Item Response Theory (IRT) based approach that utilizes item response data. It investigates the impact of complex student clustering structure where…
Descriptors: Item Response Theory, Hierarchical Linear Modeling, Growth Models, Multivariate Analysis
Patten, Sarah Linda – AERA Online Paper Repository, 2016
This original research examined the intersection of school leadership and teacher to parent communication. The analysis used hierarchical linear modeling (HLM) to determine how principal actions and school level factors were related to teacher actions and the focus of communication. Secondary educators in four district school boards were surveyed…
Descriptors: Hierarchical Linear Modeling, Correlation, Parent Teacher Cooperation, Public Schools
Hooper, Alison – AERA Online Paper Repository, 2016
This study considers whether children who have kindergarten and first grade teachers with certification in early childhood education experience greater gains in reading and math achievement compared to children whose teachers have only elementary education certification. Using data from the Early Childhood Longitudinal Study (ECLS-K), the study…
Descriptors: Preschool Teachers, Elementary School Teachers, Teacher Certification, Early Childhood Education