Abstract
We discuss three broader implications of state-trace analysis. First, we briefly outline signed difference analysis—the generalization of STA to more than one latent variable. State-trace analysis asks if the effects of one or more independent variables on two or more dependent variables are mediated by one or more than one latent variables. SDA asks if the effects are mediated by a model consisting of two, three, or potentially any number of latent variables. Second, we discuss the question of characterizing the latent variables revealed by STA or SDA. Third, we discuss the question of model complexity—where low-dimensional models are necessarily nested in higher-dimensional models.
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Although in SDA, a two-dimensional model may be rejected in favor of a three-dimensional model and so on.
References
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Dunn, J.C., Kalish, M.L. (2018). Final Comments. In: State-Trace Analysis. Computational Approaches to Cognition and Perception. Springer, Cham. https://doi.org/10.1007/978-3-319-73129-2_9
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DOI: https://doi.org/10.1007/978-3-319-73129-2_9
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