To date, only little is known about the self-directed perception and processing of subtle gaze cues in social anxiety that might however contribute to excessive feelings of being looked at by others. Using a web-based approach, participants (n=174) were asked whether or not briefly (300 ms) presented facial expressions modulated in gaze direction (0°, 2°, 4°, 6°, 8°) and valence (angry, fearful, happy, neutral) were directed at them. The results demonstrate a positive, linear relationship between self-reported social anxiety and stronger self-directed perception of others' gaze directions, particularly for negative (angry, fearful) and neutral expressions. Furthermore, faster responding was found for gaze more clearly directed at socially anxious individuals (0°, 2°, and 4°) suggesting a tendency to avoid direct gaze. In sum, the results illustrate an altered self-directed perception of subtle gaze cues. The possibly amplifying effects of social stress on biased self-directed perception of eye gaze are discussed.
Structural equation model (SEM) trees are data-driven tools for finding variables that predict group differences in SEM parameters. SEM trees build upon the decision tree paradigm by growing tree structures that divide a data set recursively into homogeneous subsets. In past research, SEM trees have been estimated predominantly with the R package semtree. The original algorithm in the semtree package selects split variables among covariates by calculating a likelihood ratio for each possible split of each covariate. Obtaining these likelihood ratios is computationally demanding. As a remedy, we propose to guide the construction of SEM trees by a family of score-based tests that have recently been popularized in psychometrics (Merkle and Zeileis, 2013; Merkle et al., 2014). These score-based tests monitor fluctuations in case-wise derivatives of the likelihood function to detect parameter differences between groups. Compared to the likelihood-ratio approach, score-based tests are computationally efficient because they do not require refitting the model for every possible split. In this paper, we introduce score-guided SEM trees, implement them in semtree, and evaluate their performance by means of a Monte Carlo simulation.
Dynamic panel models are a popular approach to study interrelationships between repeatedly measured variables. Often, dynamic panel models are specified and estimated within a structural equation modeling (SEM) framework. An endemic problem threatening the validity of such models is unmodelled heterogeneity. Recently, individual parameter contribution (IPC) regression was proposed as a flexible method to study heterogeneity in SEM parameters as a function of observed covariates. In the present paper, we derive how IPCs can be calculated for general maximum likelihood estimates and evaluate the performance of IPC regression to estimate group differences in dynamic panel models in discrete and continuous time. We show that IPC regression can be slightly biased in samples with large group differences and present a bias correction procedure. IPC regression showed generally promising results for discrete time models. However, due to highly nonlinear parameter constraints, caution is indicated when applying IPC regression to continuous time models.
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