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The current research asks whether children's judgments of harmful actions toward animals depend on animals' perceived attractiveness. In Study 1, primary school children (N = 359) rated the perceived attractiveness of six animals and judged how severe it is to hurt them, as compared to moral transgressions, social-conventional transgressions, and personal choices. Hurting attractive animals was perceived as severe as hurting another child, while hurting unattractive animals was evaluated as less serious than social-conventional transgressions. In Study 2, we experimentally tested whether the attractiveness of animals rated as unattractive in Study 1 could be influenced by an environmental education intervention. After the intervention, children in the experimental group (N = 21) rated unattractive animals as more attractive than before the intervention, and this led to judging harming these animals more severely than before the intervention. No changes were found in the control group (N = 20). [ABSTRACT FROM AUTHOR]

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The Q‐matrix identifies the subset of attributes measured by each item in the cognitive diagnosis modelling framework. Usually constructed by domain experts, the Q‐matrix might contain some misspecifications, disrupting classification accuracy. Empirical Q‐matrix validation methods such as the general discrimination index (GDI) and Wald have shown promising results in addressing this problem. However, a cut‐off point is used in both methods, which might be suboptimal. To address this limitation, the Hull method is proposed and evaluated in the present study. This method aims to find the optimal balance between fit and parsimony, and it is flexible enough to be used either with a measure of item discrimination (the proportion of variance accounted for, PVAF) or a coefficient of determination (pseudo‐R2). Results from a simulation study showed that the Hull method consistently showed the best performance and shortest computation time, especially when used with the PVAF. The Wald method also performed very well overall, while the GDI method obtained poor results when the number of attributes was high. The absence of a cut‐off point provides greater flexibility to the Hull method, and it places it as a comprehensive solution to the Q‐matrix specification problem in applied settings. This proposal is illustrated using real data. [ABSTRACT FROM AUTHOR]

Decisions on how to calibrate an item bank might have major implications in the subsequent performance of the adaptive algorithms. One of these decisions is model selection, which can become problematic in the context of cognitive diagnosis computerized adaptive testing, given the wide range of models available. This article aims to determine whether model selection indices can be used to improve the performance of adaptive tests. Three factors were considered in a simulation study, that is, calibration sample size, Q-matrix complexity, and item bank length. Results based on the true item parameters, and general and single reduced model estimates were compared to those of the combination of appropriate models. The results indicate that fitting a single reduced model or a general model will not generally provide optimal results. Results based on the combination of models selected by the fit index were always closer to those obtained with the true item parameters. The implications for practical settings include an improvement in terms of classification accuracy and, consequently, testing time, and a more balanced use of the item bank. An R package was developed, named cdcatR, to facilitate adaptive applications in this context. [ABSTRACT FROM AUTHOR]

Cognitive diagnosis models (CDMs) are latent class models that hold great promise for providing diagnostic information about student knowledge profiles. The increasing use of computers in classrooms enhances the advantages of CDMs for more efficient diagnostic testing by using adaptive algorithms, referred to as cognitive diagnosis computerized adaptive testing (CD-CAT). When multiple-choice items are involved, CD-CAT can be further improved by using polytomous scoring (i.e., considering the specific options students choose), instead of dichotomous scoring (i.e., marking answers as either right or wrong). In this study, the authors propose and evaluate the performance of the Jensen–Shannon divergence (JSD) index as an item selection method for the multiple-choice deterministic inputs, noisy "and" gate (MC-DINA) model. Attribute classification accuracy and item usage are evaluated under different conditions of item quality and test termination rule. The proposed approach is compared with the random selection method and an approximate approach based on dichotomized responses. The results show that under the MC-DINA model, JSD improves the attribute classification accuracy significantly by considering the information from distractors, even with a very short test length. This result has important implications in practical classroom settings as it can allow for dramatically reduced testing times, thus resulting in more targeted learning opportunities. [ABSTRACT FROM AUTHOR]

Contemporary models of personality assume a hierarchical structure in which broader traits contain narrower traits. Individual differences in response styles also constitute a source of score variance. In this study, the bifactor model is applied to separate these sources of variance for personality subscores. The procedure is illustrated using data for two personality inventories—NEO Personality Inventory–Revised and Zuckerman–Kuhlman–Aluja Personality Questionnaire. The inclusion of the acquiescence method factor generally improved the fit to acceptable levels for the Zuckerman–Kuhlman–Aluja Personality Questionnaire, but not for the NEO Personality Inventory–Revised. This effect was higher in subscales where the number of direct and reverse items is not balanced. Loadings on the specific factors were usually smaller than the loadings on the general factor. In some cases, part of the variance was due to domains being different from the main one. This information is of particular interest to researchers as they can identify which subscale scores have more potential to increase predictive validity. [ABSTRACT FROM AUTHOR]

AbstractForced-choice (FC) questionnaires have gained scientific interest over the last decades. However, the inclusion of unequally keyed item pairs in FC questionnaires remains a subject of debate, as there is evidence supporting both their usage and avoidance. Designing unequally keyed pairs may be more difficult when considering social desirability, as they might allow the identification of ideal responses. Nevertheless, they may enhance the reliability and the potential for normative interpretation of scores. To empirically investigate this topic, data were collected from 1,125 undergraduate Psychology students who completed a personality item pool measuring the Big Five personality traits in Likert-type format and two FC questionnaires (with and without unequally keyed pairs). These questionnaires were compared in terms of reliability, convergent and criterion validity, and ipsativity of the scores, along with insights on the construction process. While constructing questionnaires with unequally keyed blocks presented challenges in matching items on their social desirability, the differences observed in terms of reliability, validity, or ipsativity were sporadic and lacked systematic patterns. This suggests that neither questionnaire format exhibited a clear superiority. Given these results, it is recommended using only equally keyed blocks to minimize potential validity issues associated with response biases. [ABSTRACT FROM AUTHOR]

Background: Ensuring the validity of assessments requires a thorough examination of the test content. Subject matter experts (SMEs) are commonly employed to evaluate the relevance, representativeness, and appropriateness of the items. This article proposes incorporating item response theory (IRT) into model assessments conducted by SMEs. Using IRT allows for the estimation of discrimination and threshold parameters for each SME, providing evidence of their performance in differentiating relevant from irrelevant items, thus facilitating the detection of suboptimal SME performance while improving item relevance scores. Method: Use of IRT was compared to traditional validity indices (content validity index and Aiken's V) in the evaluation of conscientiousness items. The aim was to assess the SMEs' accuracy in identifying whether items were designed to measure conscientiousness or not, and predicting their factor loadings. Results: The IRT-based scores effectively identified conscientiousness items (R² = 0.57) and accurately predicted their factor loadings (R² = 0.45). These scores demonstrated incremental validity, explaining 11% more variance than Aiken's V and up to 17% more than the content validity index. Conclusions: Modeling SME assessments with IRT improves item alignment and provides better predictions of factor loadings, enabling improvement of the content validity of measurement instruments. [ABSTRACT FROM AUTHOR]

The number of available factor analytic techniques has been increasing in the last decades. However, the lack of clear guidelines and exhaustive comparison studies between the techniques might hinder that these valuable methodological advances make their way to applied research. The present paper evaluates the performance of confirmatory factor analysis (CFA), CFA with sequential model modification using modification indices and the Saris procedure, exploratory factor analysis (EFA) with different rotation procedures (Geomin, target, and objectively refined target matrix), Bayesian structural equation modeling (BSEM), and a new set of procedures that, after fitting an unrestrictive model (i.e., EFA, BSEM), identify and retain only the relevant loadings to provide a parsimonious CFA solution (ECFA, BCFA). By means of an exhaustive Monte Carlo simulation study and a real data illustration, it is shown that CFA and BSEM are overly stiff and, consequently, do not appropriately recover the structure of slightly misspecified models. EFA usually provides the most accurate parameter estimates, although the rotation procedure choice is of major importance, especially depending on whether the latent factors are correlated or not. Finally, ECFA might be a sound option whenever an a priori structure cannot be hypothesized and the latent factors are correlated. Moreover, it is shown that the pattern of the results of a factor analytic technique can be somehow predicted based on its positioning in the confirmatory-exploratory continuum. Applied recommendations are given for the selection of the most appropriate technique under different representative scenarios by means of a detailed flowchart. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Research on unconscious processing has been a valuable source of evidence in psycholinguistics for shedding light on the cognitive architecture of language. The automaticity of syntactic processing, in particular, has long been debated. One strategy to establish this automaticity involves detecting significant syntactic priming effects in tasks that limit conscious awareness of the stimuli. Criteria for assessing unconscious priming include the visibility ( d ') of masked words not differing significantly from zero and no positive correlation between visibility and priming. However, such outcomes could also arise for strictly methodological reasons, such as low statistical power in visibility tests or low reliability of dependent measures. In this study, we aimed to address these potential limitations. Through meta-analysis and Bayesian re-analysis, we find evidence of low statistical power and of participants having above-chance awareness of 'subliminal' words. Moreover, we conducted reliability analyses on a dataset from Berkovitch and Dehaene (2019), finding that low reliability in both syntactic priming and visibility tasks may better explain the absence of a significant correlation. Overall, these findings cast doubt on the validity of previous conclusions regarding the automaticity of syntactic processing based on masked priming effects. The results underscore the importance of revisiting the methods employed when exploring unconscious processing in future psycholinguistic research., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2025 The Author(s).)

The inclusion of the borderline pattern in the International Classification of Diseases, 11th Revision (ICD-11) dimensional classification of personality disorders (PDs) has caused controversy. Unease about leaving out these clinically challenging patients seems to conflict with the need of an evidence-based and credible diagnostic system. However, the accommodation of borderline within the new diagnostic system has not yet been studied in depth. To this end, we examine in a sample of 1799 general population and clinical subjects the joint structure of the five initial ICD-11 domains and the borderline pattern. Regression and item-level factor analyses reveal that borderline criteria do not form a separate construct and are indissociable from negative affectivity. Furthermore, borderline adds nothing to the remaining domains when it comes to predict PD severity. The borderline pattern appears as largely superfluous and even misguiding, unless their criteria are properly integrated within the structure of personality pathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).