Testing for Cognitive Models Stability to Specific Context of Application

Dear Colleagues,

Checking for cognitive model stability means to explore the extent to which the theoretically- based causal relationships, correctly specified, can go above and beyond some values while imposing constraints on specific path coefficients. These constraints might significantly alter the other unconstrained path coefficients in the cognitive model and thus provide concrete conclusions on the boundaries of the underlying theory both in terms of involved variables and path coefficients within certain contexts of application. This possible result can be considered like model properties rather than potential misspecifications. These model properties (i.e., correct specifications and variables inclusion under specific constraints and thus under an alteration process) are seen like the capacity of the tested model to react to, and thus control for, possible restrictions. As a consequence, modeling the so-called alteration to specific constraints means to test for this model stability. In this respect, the alteration was conceptually defined as the cognitive strength that may be admissible to overcome constraint-based causal paths (Vassallo, 2020).

This Special Issue welcomes manuscripts on modeling path stability in cognitive path models of applied psychology. Alternative ways (to the ones proposed by (Vassallo, 2020).) of testing model connections’ stability to context specificity and alternative methods (again to the ones proposed by (Vassallo, 2020).) of model constraint estimations (together with quantifying the consequences to the other unconstrained coefficients) are both preferred. Systematic reviews on path models misspecifications issues might be considered, but should be focused on constraints processes along with proposing new directions of controlling for these constraints.

Before submitting a manuscript, I strongly suggest authors read the following works very carefully:

1. Lee, S.Y. Estimation of covariance structure models with parameters subject to functional restraints. Psychom. 1980, 45, 309–324, doi:10.1007/bf02293906.
2. Pearl, J. On the testability of causal models with latent and instrumental variables. P. Besnard, P.; Hanks, S., Eds. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI). Morgan Kaufmann: San Francisco, CA, USA. 1995; pp. 435–443.
3. Vassallo, M. Exploring boundaries of correct specifications in cognitive causal models: Phantom path analysis may help. Testing, Psychometrics. Methodol. Appl. Psychol. 2020, 27, 21–42, doi: 10.4473/TPM27.1.2.

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• path models stability
• models constraints
• path alterations
• alteration concepts
• psychometric tests