The field of Affective Computing AC expects to narrow the communicative gap between the highly emotional human and the emotionally challenged computer by developing computational systems that recognize and respond to the affective states of the user. Affect-sensitive interfaces are being developed in number of domains, including gaming, mental health, and learning technologies. Emotions are part of human life. Recently, interest has been growing among researchers to find ways of detecting subjective information used in blogs and other online social media.
A more flexible representation of substantive theory. Psychological Methods, 17, Click ""download paper"" below for the latest version of October 21, Download the 2nd version dated April 14, Click here to view the seven web tables referred to in the paper and here to view Mplus inputs, data, and outputs used in this version of paper.
Download the 1st version dated September 29, containing a MIMIC section and more tables, and the corresponding Mplus inputs, data, and outputs here.
The seven web tables correspond to tables 8, 10, 17, 18, 19, 20, and 21 of the first version. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, small-variance priors. It is argued that this produces an analysis that better rejects substantive theories.
The proposed Bayesian approach is particularly beneficial in applications where parameters are added to a conventional model such that a non-identified model is obtained if maximum-likelihood estimation is applied.
This approach is useful for measurement aspects of latent variable modeling such as with CFA and the measurement part of SEM. Two application areas are studied, cross-loadings and residual correlations in CFA. The approach encompasses three elements: Model testing, model estimation, and model modi cation.
Monte Carlo simulations and real data are analyzed using Mplus. Mastering a new method. Psychological Methods, Vol 17 3Sep Responses to specific comments cover how to handle sign switching, nonconvergence and nonidentification, and prior choices in latent variable models.
Two new applications are included.
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