Designing Visual-Arts Education Programs for Transfer Effects: Development and Experimental Evaluation of (Digital) Drawing Courses in the Art Museum Designed to Promote Adolescents' Socio-Emotional Skills.

An active engagement with arts in general and visual arts in particular has been hypothesized to yield beneficial effects beyond arts itself. So-called cognitive and socio-emotional "transfer" effects into other domains have been claimed. However, the empirical basis of these hopes is limited. This is partly due to a lack of experimental comparisons, theory-based designs, and objective measurements in the literature on transfer effects of arts education. Therefore, the aim of the present study was to design and experimentally investigate a theory-based visual-arts education program for adolescents aged between 12 and 19 years (M age = 15.02, SD age = 1.75). The program was delivered in a museum context in three sessions and was expected to yield specific and objectively measurable transfer effects. To conduct a randomized field trial, three strictly parallelized and standardized art courses were developed, all of which addressed the topic of portrait drawing. The courses mainly differed regarding their instructional focus, which was either on periods of art history, on the facial expression of emotions, or on the self-perception of a person in the context of different social roles. In the first and more "traditional" course portrait drawing was used to better understand how portraits looked like in former centuries. The two other courses were designed in a way that the artistic engagement in portrait drawing was interwoven with practicing socio-emotional skills, namely empathy and emotion recognition in one course and understanding complex self-concept structures in the other. We expected positive socio-emotional transfer effects in the two "psychological" courses. We used an animated morph task to measure emotion recognition performance and a self-concept task to measure the self-complexity of participants before and after all three courses. Results indicate that an instructional focus on drawing the facial expressions of emotions yields specific improvements in emotion recognition, whereas drawing persons in different social roles yields a higher level of self-complexity in the self-concept task. In contrast, no significant effects on socio-emotional skills were found in the course focussing on art history. Therefore, our study provides causal evidence that visual-arts programs situated in an art-museum context can advance socio-emotional skills, when designed properly.

Comparing estimators for latent interaction models under structural and distributional misspecifications.

Estimation methods for structural equation models with interactions of latent variables were compared in several studies. Yet none of these studies examined models that were structurally misspecified. Here, the model-implied instrumental variable 2-stage least square estimator (MIIV-2SLS; Bollen, 1995; Bollen & Paxton, 1998), the 2-stage method of moments estimator (2SMM; Wall & Amemiya, 2003), the nonlinear structural equation mixture model approach (NSEMM; Kelava, Nagengast, & Brandt, 2014), and the unconstrained product indicator approach (UPI; Marsh, Wen, & Hau, 2004) were compared in a Monte Carlo simulation. The design included structural misspecifications in the measurement model involving the scaling indicator or not, the size of the misspecification, normal and nonnormal data, the indicators' reliability, and sample size. For the structural misspecifications that did not involve the scaling indicator, we found that MIIV-2SLS' parameter estimates were less biased compared with 2SMM, NSEMM, and UPI. If the reliability was high, the RMSE for all approaches was very similar; for low reliability, MIIV-2SLS' RMSE became larger compared with the other approaches. If the structural misspecification involved the scaling indicator, all estimators were seriously biased, with the largest bias for MIIV-2SLS. In most scenarios, this bias was more severe for the linear effects than for the interaction effect. The RMSE for conditions with misspecified scaling indicators was smallest for 2SMM, especially for low reliability scenarios, but the overall magnitude of bias was such that we cannot recommend any of the estimators in this situation. Our article showed the damage done when researchers omit cross-loadings of the scaling indicator and the importance of giving more attention to these indicators particularly if the indicators' reliability is low. It also showed that no one estimator is superior to the others across all conditions. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The Standardization of Linear and Nonlinear Effects in Direct and Indirect Applications of Structural Equation Mixture Models for Normal and Nonnormal Data.

The application of mixture models to flexibly estimate linear and nonlinear effects in the SEM framework has received increasing attention (e.g., Jedidi et al., 1997b; Bauer, 2005; Muthén and Asparouhov, 2009; Wall et al., 2012; Kelava and Brandt, 2014; Muthén and Asparouhov, 2014). The advantage of mixture models is that unobserved subgroups with class-specific relationships can be extracted (direct application), or that the mixtures can be used as a statistical tool to approximate nonnormal (latent) distributions (indirect application). Here, we provide a general standardization procedure for linear and nonlinear interaction and quadratic effects in mixture models. The procedure can also be applied to multiple group models or to single class models with nonlinear effects like LMS (Klein and Moosbrugger, 2000). We show that it is necessary to take nonnormality of the data into account for a correct standardization. We present an empirical example from education science applying the proposed procedure.