The Misinformation Susceptibility Test (MIST): A psychometrically validated measure of news veracity discernment.

Interest in the psychology of misinformation has exploded in recent years. Despite ample research, to date there is no validated framework to measure misinformation susceptibility. Therefore, we introduce Verification done, a nuanced interpretation schema and assessment tool that simultaneously considers Veracity discernment, and its distinct, measurable abilities (real/fake news detection), and biases (distrust/naïvité-negative/positive judgment bias). We then conduct three studies with seven independent samples (Ntotal = 8504) to show how to develop, validate, and apply the Misinformation Susceptibility Test (MIST). In Study 1 (N = 409) we use a neural network language model to generate items, and use three psychometric methods-factor analysis, item response theory, and exploratory graph analysis-to create the MIST-20 (20 items; completion time < 2 minutes), the MIST-16 (16 items; < 2 minutes), and the MIST-8 (8 items; < 1 minute). In Study 2 (N = 7674) we confirm the internal and predictive validity of the MIST in five national quota samples (US, UK), across 2 years, from three different sampling platforms-Respondi, CloudResearch, and Prolific. We also explore the MIST's nomological net and generate age-, region-, and country-specific norm tables. In Study 3 (N = 421) we demonstrate how the MIST-in conjunction with Verification done-can provide novel insights on existing psychological interventions, thereby advancing theory development. Finally, we outline the versatile implementations of the MIST as a screening tool, covariate, and intervention evaluation framework. As all methods are transparently reported and detailed, this work will allow other researchers to create similar scales or adapt them for any population of interest.

Creating an objective measurement for the Enem: an analysis using the Rasch Model / Criando uma medida verdadeira para o Enem: uma análise pelo Modelo Rasch / Creando una verdadera medida para Enem: un análisis por el Modelo Rasch

In the 1930s, a group of scientists argued that the empirical concatenation of observable elements was not possible in the human and social sciences and was, thus, not feasible to obtain objective measurements similar to those found in physics. To address this issue, mathematical theories that do not require concatenation were proposed in the 1960s, including the Additive Conjoint Measurement Theory (ACMT). In the same decade, George Rasch developed the simple logistic model for dichotomous data as a probabilistic operationalization of the ACMT. This study investigates the possibility of developing a fundamental measure for the National Exam of Upper Secondary Education (ENEM) that applies Rasch's model to students' performance on the 2011 ENEM exam. The results indicate an adequate model fit, demonstrating the viability of a fundamental measure using ENEM data. Implications are discussed.

Nos anos 1930, um grupo de cientistas argumentou que a concatenação empírica de elementos observáveis não seria possível nas Ciências Humanas e Sociais e por isso era inviável obter medidas verdadeiras nesses campos do conhecimento científico. Para lidar com este problema, foram propostas teorias matemáticas nas quais a concatenação empírica não seria necessária, como a Teoria de Medidas Aditivas Conjuntas (TMAC). No mesmo período, George Rasch desenvolveu o modelo logístico simples para dados dicotômicos, uma operacionalização probabilística da TMAC que viabiliza a análise empírica de pressupostos da medida verdadeira. Em nosso estudo, investigamos o desenvolvimento de uma medida verdadeira para o Exame Nacional do Ensino Médio (ENEM), aplicando o modelo logístico simples em dados referentes à performance dos participantes da edição de 2011 do ENEM. Os resultados indicaram um ajuste adequado do modelo, apontando para a viabilidade da construção de uma medida verdadeira para o ENEM. Implicações são discutidas.

En los años 1930, un grupo de científicos argumentó que la concatenación empírica de elementos observables no sería posible en Ciencias Humanas y Sociales y por consiguiente sería inviable obtener medidas verdaderas similares a las de Física. Para abordar este problema, a partir de los años 1960 se proponían teorías en las cuales la concatenación empírica no es necesaria, como la Teoría de Medidas Aditivas Conjuntas (TMAC). Al mismo período, George Rasch desarrolló el modelo logístico simple para datos dicotómicos, una operacionalización probabilista de la TMAC. Este estudio investigó la posibilidad de desarrollar una medida verdadera para el Examen Nacional de la Secundaria Superior (ENEM), aplicando el modelo logístico simple en los datos referentes a la performance de los participantes en la prueba de 2011 del ENEM. Los resultados indicaron adecuado ajuste del modelo, asi como la viabilidad de una medida verdadera para el ENEM. Implicaciones son discutidas.

Genetic, epigenetic, and environmental factors controlling oxytocin receptor gene expression.

BACKGROUND: The neuropeptide oxytocin regulates mammalian social behavior. Disruptions in oxytocin signaling are a feature of many psychopathologies. One commonly studied biomarker for oxytocin involvement in psychiatric diseases is DNA methylation at the oxytocin receptor gene (OXTR). Such studies focus on DNA methylation in two regions of OXTR, exon 3 and a region termed MT2 which overlaps exon 1 and intron 1. However, the relative contribution of exon 3 and MT2 in regulating OXTR gene expression in the brain is currently unknown. RESULTS: Here, we use the prairie vole as a translational animal model to investigate genetic, epigenetic, and environmental factors affecting Oxtr gene expression in a region of the brain that has been shown to drive Oxtr related behavior in the vole, the nucleus accumbens. We show that the genetic structure of Oxtr in prairie voles resembles human OXTR. We then studied the effects of early life experience on DNA methylation in two regions of a CpG island surrounding the Oxtr promoter: MT2 and exon 3. We show that early nurture in the form of parental care results in DNA hypomethylation of Oxtr in both MT2 and exon 3, but only DNA methylation in MT2 is associated with Oxtr gene expression. Network analyses indicate that CpG sites in the 3' portion of MT2 are most highly associated with Oxtr gene expression. We also identify two novel SNPs in exon 3 of Oxtr in prairie voles and a novel alternative transcript originating from the third intron of the gene. Expression of the novel alternative transcript is associated with genotype at SNP KLW2. CONCLUSIONS: These results identify putative regulatory features of Oxtr in prairie voles which inform future studies examining OXTR in human social behaviors and disorders. These studies indicate that in prairie voles, DNA methylation in MT2, particularly in the 3' portion, is more predictive of Oxtr gene expression than DNA methylation in exon 3. Similarly, in human temporal cortex, we find that DNA methylation in the 3' portion of MT2 is associated with OXTR expression. Together, these results suggest that among the CpG sites studied, DNA methylation of MT2 may be the most reliable indicator of OXTR gene expression. We also identify novel features of prairie vole Oxtr, including SNPs and an alternative transcript, which further develop the prairie vole as a translational model for studies of OXTR.

Is Small Still Beautiful for the Strengths and Difficulties Questionnaire? Novel Findings Using Exploratory Structural Equation Modeling.

During the present decade a large body of research has employed confirmatory factor analysis (CFA) to evaluate the factor structure of the Strengths and Difficulties Questionnaire (SDQ) across multiple languages and cultures. However, because CFA can produce strongly biased estimations when the population cross-loadings differ meaningfully from zero, it may not be the most appropriate framework to model the SDQ responses. With this in mind, the current study sought to assess the factorial structure of the SDQ using the more flexible exploratory structural equation modeling approach. Using a large-scale Spanish sample composed of 67,253 youths aged between 10 and 18 years (M = 14.16, SD = 1.07), the results showed that CFA provided a severely biased and overly optimistic assessment of the underlying structure of the SDQ. In contrast, exploratory structural equation modeling revealed a generally weak factorial structure, including questionable indicators with large cross-loadings, multiple error correlations, and significant wording variance. A subsequent Monte Carlo study showed that sample sizes greater than 4,000 would be needed to adequately recover the SDQ loading structure. The findings from this study prevent recommending the SDQ as a screening tool and suggest caution when interpreting previous results in the literature based on CFA modeling.

Exploratory Graph Analysis of the Multidimensional Schizotypy Scale.

The present study examined the dimensional structure underlying the Multidimensional Schizotypy Scale (MSS) and its brief version (MSS-B). We used Exploratory Graph Analysis (EGA) to evaluate their dimensional structure in two large, independent samples (n = 6265 and n = 1000). We then used Confirmatory Factor Analysis (CFA) to compare the fit of the theoretical dimensions with the EGA dimensions. For the MSS, EGA identified four dimensions: positive schizotypy, two dimensions of negative schizotypy (affective and social anhedonia), and disorganized schizotypy. For the MSS-B, EGA identified three dimensions, which corresponded to the theorized positive, negative, and disorganized dimensions. Based on the MSS's EGA dimensions, we also estimated a four-factor model for the MSS-B. The CFA comparison found that the four-factor model fit significantly better than the theoretical three-factor model for both the MSS and MSS-B, providing support for the theoretical model and offering a more nuanced interpretation of the negative schizotypy factor. In addition, EGA also revealed that the positive and negative schizotypy dimensions of the MSS and MSS-B might be mediated by the disorganized dimension. Our findings offer new implications for future research on the MSS and MSS-B dimensions that may provide differential associations with interview and questionnaire measures.

Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research.

The estimation of the correct number of dimensions is a long-standing problem in psychometrics. Several methods have been proposed, such as parallel analysis (PA), Kaiser-Guttman's eigenvalue-greater-than-one rule, multiple average partial procedure (MAP), the maximum-likelihood approaches that use fit indexes as BIC and EBIC and the less used and studied approach called very simple structure (VSS). In the present paper a new approach to estimate the number of dimensions will be introduced and compared via simulation to the traditional techniques pointed above. The approach proposed in the current paper is called exploratory graph analysis (EGA), since it is based on the graphical lasso with the regularization parameter specified using EBIC. The number of dimensions is verified using the walktrap, a random walk algorithm used to identify communities in networks. In total, 32,000 data sets were simulated to fit known factor structures, with the data sets varying across different criteria: number of factors (2 and 4), number of items (5 and 10), sample size (100, 500, 1000 and 5000) and correlation between factors (orthogonal, .20, .50 and .70), resulting in 64 different conditions. For each condition, 500 data sets were simulated using lavaan. The result shows that the EGA performs comparable to parallel analysis, EBIC, eBIC and to Kaiser-Guttman rule in a number of situations, especially when the number of factors was two. However, EGA was the only technique able to correctly estimate the number of dimensions in the four-factor structure when the correlation between factors were .7, showing an accuracy of 100% for a sample size of 5,000 observations. Finally, the EGA was used to estimate the number of factors in a real dataset, in order to compare its performance with the other six techniques tested in the simulation study.