The goal of the current investigation was to analyze ability emotional intelligence (EI) in a large cross-sectional sample of Spanish adults (N = 12,198; males, 56.56%) aged from 17 to 76 years (M = 37.71, SD = 12.66). Using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT), which measures ability EI according to the 4 branches of the Mayer and Salovey EI model. The authors examined effects of gender on ability EI, as well as the linear and quadratic effects of age. Results suggest that gender affects the total ability EI score as well as scores on the 4 EI branches. Ability EI was greater in women than men. Ability EI varied with age according to an inverted-U curve: Younger and older adults scored lower on ability EI than middle-aged adults, except for the branch of understanding emotions. These findings strongly support the idea that both gender and age significantly influence ability EI during aging. (PsycINFO Database Record

Conventional methods for assessing the validity and reliability of situational judgment test (SJT) scores have proven to be inadequate. For example, factor analysis techniques typically lead to nonsensical solutions, and assumptions underlying Cronbach’s alpha coefficient are violated due to the multidimensional nature of SJTs. In the current article, we describe how cognitive diagnosis models (CDMs) provide a new approach that not only overcomes these limitations but that also offers extra advantages for scoring and better understanding SJTs. The analysis of the Q-matrix specification, model fit, and model parameter estimates provide a greater wealth of information than traditional procedures do. Our proposal is illustrated using data taken from a 23-item SJT that presents situations about student-related issues. Results show that CDMs are useful tools for scoring tests, like SJTs, in which multiple knowledge, skills, abilities, and other characteristics are required to correctly answer the items. SJT classifications were reliable and significantly related to theoretically relevant variables. We conclude that CDM might help toward the exploration of the nature of the constructs underlying SJT, one of the principal challenges in SJT research.

This is the pre-peer reviewed version of a work that was accepted for publication in Journal of Environmental Psychology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Environmental Psychology, 44 (2015) doi: 10.1016/j.jenvp.2015.09.006Little is known about the developmental course of young children's acquisition of pro-environmental practices. This study takes a closer look at the relationship between age on 6e12 year-olds (N ¼ 107) and pro-environmental behaviors (EB). Using a well-validated game format tool based on the Rasch model to collect the data, children's EB were registered, together with their ecological beliefs, Emotional Affinity toward Nature (EAN) and parental EB. The results of three structural equation models showed that age was statistically significant and negatively associated with children's EB. Children's ecological beliefs and EAN were positively related to EB, but these variables did not mediate the relationship between age and EB. Parental EB was negatively linked to children's EB. Exploratory analyses revealed that the relationship between ecological beliefs, EAN and EB seems to be stronger for older childrenFinancial support came from the Spanish Ministry of Economy and Competitiveness (PSI-2013-44939

Research related to the fit evaluation at the item level involving cognitive diagnosis models (CDMs) has been scarce. According to the parsimony principle, balancing goodness of fit against model complexity is necessary. General CDMs require a larger sample size to be estimated reliably, and can lead to worse attribute classification accuracy than the appropriate reduced models when the sample size is small and the item quality is poor, which is typically the case in many empirical applications. The main purpose of this study was to systematically examine the statistical properties of four inferential item-fit statistics: $S-X^2$ , the likelihood ratio (LR) test, the Wald (W) test, and the Lagrange multiplier (LM) test. To evaluate the performance of the statistics, a comprehensive set of factors, namely, sample size, correlational structure, test length, item quality, and generating model, is systematically manipulated using Monte Carlo methods. Results show that the $S-X^2$ statistic has unacceptable power. Type I error and power comparisons favor LR and W tests over the LM test. However, all the statistics are highly affected by the item quality. With a few exceptions, their performance is only acceptable when the item quality is high. In some cases, this effect can be ameliorated by an increase in sample size and test length. This implies that using the above statistics to assess item fit in practical settings when the item quality is low remains a challenge.