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Baek, Eunkyeng; Luo, Wen; Henri, Maria – Journal of Experimental Education, 2022
It is common to include multiple dependent variables (DVs) in single-case experimental design (SCED) meta-analyses. However, statistical issues associated with multiple DVs in the multilevel modeling approach (i.e., possible dependency of error, heterogeneous treatment effects, and heterogeneous error structures) have not been fully investigated.…
Descriptors: Meta Analysis, Hierarchical Linear Modeling, Comparative Analysis, Statistical Inference
Fan Pan – ProQuest LLC, 2021
This dissertation informed researchers about the performance of different level-specific and target-specific model fit indices in Multilevel Latent Growth Model (MLGM) using unbalanced design and different trajectories. As the use of MLGMs is a relatively new field, this study helped further the field by informing researchers interested in using…
Descriptors: Goodness of Fit, Item Response Theory, Growth Models, Monte Carlo Methods
Arens, A. Katrin; Morin, Alexandre J. S. – American Educational Research Journal, 2017
This study illustrates an integrative psychometric framework to investigate two sources of construct-relevant multidimensionality in answers to the Self-Perception Profile for Children (SPPC). Using a sample of 2,353 German students attending Grades 3 to 6, we contrasted: (a) first-order versus hierarchical and bifactor models to investigate…
Descriptors: Self Concept, Structural Equation Models, Factor Analysis, Error of Measurement
Liu, Siwei; Rovine, Michael J.; Molenaar, Peter C. M. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
This study investigated the performance of fit indexes in selecting a covariance structure for longitudinal data. Data were simulated to follow a compound symmetry, first-order autoregressive, first-order moving average, or random-coefficients covariance structure. We examined the ability of the likelihood ratio test (LRT), root mean square error…
Descriptors: Structural Equation Models, Goodness of Fit, Longitudinal Studies, Data Analysis