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McNeish, Daniel – Journal of Experimental Education, 2018
Small samples are common in growth models due to financial and logistical difficulties of following people longitudinally. For similar reasons, longitudinal studies often contain missing data. Though full information maximum likelihood (FIML) is popular to accommodate missing data, the limited number of studies in this area have found that FIML…
Descriptors: Growth Models, Sampling, Sample Size, Hierarchical Linear Modeling
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Kim, Minjung; Kwok, Oi-Man; Yoon, Myeongsun; Willson, Victor; Lai, Mark H. C. – Journal of Experimental Education, 2016
This study investigated the optimal strategy for model specification search under the latent growth modeling (LGM) framework, specifically on searching for the correct polynomial mean or average growth model when there is no a priori hypothesized model in the absence of theory. In this simulation study, the effectiveness of different starting…
Descriptors: Statistical Analysis, Growth Models, Simulation, Structural Equation Models