Model Selection and Model Averaging for Mixed-Effects Models with Crossed Random Effects for Subjects and Items.

A good deal of experimental research is characterized by the presence of random effects on subjects and items. A standard modeling approach that includes such sources of variability is the mixed-effects models (MEMs) with crossed random effects. However, under-parameterizing or over-parameterizing the random structure of MEMs bias the estimations of the Standard Errors (SEs) of fixed effects. In this simulation study, we examined two different but complementary perspectives: model selection with likelihood-ratio tests, AIC, and BIC; and model averaging with Akaike weights. Results showed that true model selection was constant across the different strategies examined (including ML and REML estimators). However, sample size and variance of random slopes were found to explain true model selection and SE bias of fixed effects. No relevant differences in SE bias were found for model selection and model averaging. Sample size and variance of random slopes interacted with the estimator to explain SE bias. Only the within-subjects effect showed significant underestimation of SEs with smaller number of items and larger item random slopes. SE bias was higher for ML than REML, but the variability of SE bias was the opposite. Such variability can be translated into high rates of unacceptable bias in many replications.

Older People's Personal Strengths During the First Wave of the COVID-19 Pandemic.

BACKGROUND: Although several biopsychosocial variables could play an important role as risk and protective factors of mental health, COVID-19 outbreak studies among older people have seldom focused on protective factors. The purpose of this study was to analyze how older adults' personal strengths predict their well-being and emotional distress. METHOD: 783 Spanish people aged 60 and over completed a survey that included sociodemographic characteristics, perceived health, direct or indirect infection by COVID-19, resilience, gratitude, experiential avoidance, family functioning, emotional distress and well-being. Structural Equation Modelling (SEM) was performed. SEM invariance was also used to analyze whether there were differences between older people affected by COVID-19 and those not affected. RESULTS: The best model supports the mediation effect of resilience, gratitude and experiential avoidance on older people's well-being and emotional distress. Whether participants or relatives had been infected by the virus or not did not affect the results. CONCLUSIONS: Variables used as criteria in older adults are related to well-being and emotional distress, but only indirectly and mediated by resilience, gratitude and experiential avoidance. This confirms the importance of considering psychological strengths in older people's well-being. Interventions focused on these personal resources should be considered.

Emotional Valence Precedes Semantic Maturation of Words: A Longitudinal Computational Study of Early Verbal Emotional Anchoring.

We present a longitudinal computational study on the connection between emotional and amodal word representations from a developmental perspective. In this study, children's and adult word representations were generated using the latent semantic analysis (LSA) vector space model and Word Maturity methodology. Some children's word representations were used to set a mapping function between amodal and emotional word representations with a neural network model using ratings from 9-year-old children. The neural network was trained and validated in the child semantic space. Then, the resulting neural network was tested with adult word representations using ratings from an adult data set. Samples of 1210 and 5315 words were used in the child and the adult semantic spaces, respectively. Results suggested that the emotional valence of words can be predicted from amodal vector representations even at the child stage, and accurate emotional propagation was found in the adult word vector representations. In this way, different propagative processes were observed in the adult semantic space. These findings highlight a potential mechanism for early verbal emotional anchoring. Moreover, different multiple linear regression and mixed-effect models revealed moderation effects for the performance of the longitudinal computational model. First, words with early maturation and subsequent semantic definition promoted emotional propagation. Second, an interaction effect between age of acquisition and abstractness was found to explain model performance. The theoretical and methodological implications are discussed.

Redundancy, isomorphism, and propagative mechanisms between emotional and amodal representations of words: A computational study.

Some proposals claim that language acts as a link to propagate emotional and other modal information. Thus, there is an eminently amodal path of emotional propagation in the mental lexicon. Following these proposals, we present a computational model that emulates a linking mechanism (mapping function) between emotional and amodal representations of words using vector space models, emotional feature-based models, and neural networks. We analyzed three central concepts within the embodiment debate (redundancy, isomorphism, and propagative mechanisms) comparing two alternative hypotheses: semantic neighborhood hypothesis versus specific dimensionality hypothesis. Univariate and multivariate neural networks were trained for dimensional (N = 11,357) and discrete emotions (N = 2,266), and later we analyzed its predictions in a test set (N = 4,167 and N = 875, respectively). We showed how this computational model could propagate emotional responses to words without a direct emotional experience via amodal propagation, but no direct relations were found between emotional rates and amodal distances. Thereby, we found that there were clear redundancy and propagative mechanisms, but no isomorphism should be assumed. Results suggested that it was necessary to establish complex links to go beyond amodal distances of vector spaces. In this way, although the emotional rates of semantic neighborhoods could predict the emotional rates of target words, the mapping function of specific amodal features seemed to simulate emotional responses better. Thus, both hypotheses would not be mutually exclusive. We also showed that discrete emotions could have simpler relations between modal and amodal representations than dimensional emotions. All these results and their theoretical implications are discussed.

Metacomprehension skills depend on the type of text: An analysis from Differential Item Functioning.

BACKGROUND: Metacomprehension skills determine an individual reader's ability to judge their degree of learning and text comprehension and have considerable importance in their ability to learn from reading. Given that many comprehension processes are influenced by text characteristics, the aim of the present study was to analyze whether different types of text have significant impact on metacomprehension skills at two different points in primary education. METHOD: A total of 823 students (4th and 6th years of primary school, 9 to 11 years old) read three different texts (narrative, expository and discontinuous texts) taken from ECOM-PLEC.Pri, a standardized Spanish test for reading comprehension (León, Escudero, & Olmos, 2012). Students were classified by their metacomprehension skills. A Differential Item Functioning (DIF) analysis was conducted in order to analyze whether the underlying reading comprehension and metacomprehension processes differed across text types. RESULTS: Results showed a considerable divergence of performance for reading narrative texts as opposed to expository and discontinuous texts. These differences were related to academic level. CONCLUSION: Text characteristics such as the type of text can have a great impact on metacomprehension skills and, consequently, on learning.