The concept of emotional intelligence (EI) has drawn a great amount of scholarly interest in recent years; however, attempts to measure individual differences in this ability remain controversial. Although the Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT) remains the flagship test of EI, no study has comprehensively examined the full interpretive argument tying variation in observed test performance to variation in the underlying ability. Employing a modern perspective on validation, this article reviews and synthesizes available evidence and discusses sources of concern at (...) every level of the interpretive argument. It is argued that a focus on causal explanation of observed variation in test performance would significantly improve the validity of the MSCEIT as a measure of EI. (shrink)

MacCann, Matthews, and Roberts (2012) and Mayer, Salovey, and Caruso (2012) have offered responses to my evaluation of the validity of the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) as a measure of emotional intelligence. MacCann et al. argue that my standards for validity evidence are unrealistically high, but their argument mistakenly supposes that the concept of measurement is somehow relative, rather than absolute. Mayer et al. offer valuable clarifications regarding their emotional intelligence (EI) model, and some new evidence of its correlates. (...) However, the new evidence is not persuasive, and I think that they have largely missed the point of my call for a stronger focus on causal explanations of test performance. (shrink)

The internal structure of ability emotional intelligence tests at item level has been hardly studied, and if studied often the predicted structure did not show. In the present study, an a priori model for responses to EI ability items using Likert response scales with a Situational Judgement Test format is investigated with confirmatory factor analysis. The model consists of a target EI ability factor, an acquiescence factor, which is a method factor induced by the Likert response scales, and design-based error (...) covariances, which are induced by the SJT format. It is investigated whether this a priori model can account for the observed associations between the raw item responses of the Components of Emotion Understanding Test-24. The CEUT-24 is a new test developed to assess emotion understanding, a key aspect of the EI ability construct, based on the componential emotion framework. The sample consisted of 1184 participants from four European countries speaking four different languages. Findings showed that the a priori model fitted the data well in all four languages. Furthermore, measurement invariance testing gave evidence for a well-fitting configural, metric, and partial scalar invariance model. The conclusion is that within a regular CFA framework using raw observed items responses, method factors can be disentangled from the targeted EI ability factor. (shrink)

Today, all important educational achievement studies, particularly large scale assessments, use item response theory (IRT) as the standard method for their analyses. An important and very basic assumption of IRT is on the given dimensionality of a test: In order to be interpreted unidimensional a test has to be unidimensional and hence cannot be interpreted multidimensional. In reality though, this basic assumption is very often neglected. The Programme for International Student Assessment (PISA), for example, applies a unidimensional IRT-Model for the (...) analysis of the mathematics achievement and at the same time applies a multidimensional IRT-model for the analysis of the subscales Change and Relationships, Quantity, Space and Shape, and Uncertainty and Data. This contradiction to one of the basic assumptions of IRT is also included in other well known large scale assessments. Strangely enough, non of these studies discuss the issue. This work discusses the advantage and disadvantages of the currently used approaches for a concurrent uni- and multidimensional analysis, and a new approach is presented, which bases on an IRT model, the generalized subdimension model (GSM), which allows the calculation of a weighted mean score within the IRT framework. Besides the demonstration of different applications for the GSM and its predecessor, the subdimension model, the model’s characteristics are compared to those of other models, such as hierarchical models. Beyond the comparison of the model fit, that is, the reliability of the results, the discussion particularly focuses on the difference in the interpretation, that is, on the validity of the results. (shrink)