The Latent Change Score Model: A More Flexible Approach to Modeling Time in Self-Regulated Learning
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This research advances understanding of empirical time modeling techniques in self-regulated learning research. We intuitively explain several such methods by situating their use in the extant literature. Further, we note key statistical and inferential assumptions of each method while making clear the inferential consequences of inattention to such assumptions.
Using a population model derived from a recent large-scale review of the training and work learning literature, we employ a Monte Carlo simulation fitting six variations of linear mixed models, seven variations of latent common factor models, and a single latent change score model to 1500 simulated datasets.
The latent change score model outperformed all six of the linear mixed models and all seven of the latent common factor models with respect to (1) estimation precision of the average learner improvement, (2) correctly rejecting a false null hypothesis about such average improvement, and (3) correctly failing to reject true null hypothesis about between-learner differences (i.e., random slopes) in average improvement.
The latent change score model is a more flexible method of modeling time in self-regulated learning research, particularly for learner processes consistent with twenty-first-century workplaces. Consequently, defaulting to linear mixed or latent common factor modeling methods may have adverse inferential consequences for better understanding self-regulated learning in twenty-first-century work.
Ours is the first study to critically, rigorously, and empirically evaluate self-regulated learning modeling methods and to provide a more flexible alternative consistent with modern self-regulated learning knowledge.
KeywordsSelf-regulated learning Longitudinal modeling Multilevel modeling Latent change score
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