Search

The new methodological and technological developments of the last decade make it possible to resolve or, at least, attenuate the psychometric problems of forced-choice (FC) tests for the measurement of personality. In these tests, the person being tested is shown blocks of two or more sentences of similar social desirability, from which he or she must choose which one best represents him or her. Thus, FC tests aim to reduce response bias in self-report questionnaires. However, their use is not without risks and complications if they are not created properly. Fortunately, new psychometric models make it possible to model responses in this type of test and to optimize their construction. Moreover, they allow the construction of “on the fly” computerized adaptive FC tests (CAT-FC), in which each item is constructed on the spot, optimally matching sentences from a previously calibrated bank.

A sample suffers range restriction (RR) when its variance is reduced comparing with its population variance and, in turn, it fails representing such population. If the RR occurs over the latent factor, not directly over the observed variable, the researcher deals with an indirect RR, common when using convenience samples. This work explores how this problem affects different outputs of the factor analysis: multivariate normality (MVN), estimation process, goodness-of-fit, recovery of factor loadings, and reliability. In doing so, a Monte Carlo study was conducted. Data were generated following the linear selective sampling model, simulating tests varying their sample size (N = 200 and 500 cases), test size (J = 6, 12, 18, and 24 items), loading size (L = 0.50, 0.70, and 0.90), and restriction size (from R = 1, 0.90, 0.80, and so on till 0.10 selection ratio). Our results systematically suggest that an interaction between decreasing the loading size and increasing the restriction size affects the MVN assessment, obstructs the estimation process, and leads to an underestimation of the factor loadings and reliability. However, most of the MVN tests and most of the fit indices employed were nonsensitive to the RR problem. We provide some recommendations to applied researchers.

Multidimensional forced-choice (FC) questionnaires have been consistently found to reduce the effects of socially desirable responding and faking in noncognitive assessments. Although FC has been considered problematic for providing ipsative scores under the classical test theory, item response theory (IRT) models enable the estimation of nonipsative scores from FC responses. However, while some authors indicate that blocks composed of opposite-keyed items are necessary to retrieve normative scores, others suggest that these blocks may be less robust to faking, thus impairing the assessment validity. Accordingly, this article presents a simulation study to investigate whether it is possible to retrieve normative scores using only positively keyed items in pairwise FC computerized adaptive testing (CAT). Specifically, a simulation study addressed the effect of (a) different bank assembly (with a randomly assembled bank, an optimally assembled bank, and blocks assembled "on-the-fly" considering every possible pair of items), and (b) block selection rules (i.e., T, and Bayesian D and A-rules) over the estimate accuracy and ipsativity and overlap rates. Moreover, different questionnaire lengths (30 and 60) and trait structures (independent or positively correlated) were studied, and a nonadaptive questionnaire was included as baseline in each condition. In general, very good trait estimates were retrieved, despite using only positively keyed items. Although the best trait accuracy and lowest ipsativity were found using the Bayesian A-rule with questionnaires assembled "on-the-fly," the T-rule under this method led to the worst results. This points out to the importance of considering both aspects when designing FC CAT.

Cognitive diagnosis models (CDMs) are latent class multidimensional statistical models that help classify people accurately by using a set of discrete latent variables, commonly referred to as attributes. These models require a Q-matrix that indicates the attributes involved in each item. A potential problem is that the Q-matrix construction process, typically performed by domain experts, is subjective in nature. This might lead to the existence of Q-matrix misspecifications that can lead to inaccurate classifications. For this reason, several empirical Q-matrix validation methods have been developed in the recent years. de la Torre and Chiu proposed one of the most popular methods, based on a discrimination index. However, some questions related to the usefulness of the method with empirical data remained open due the restricted number of conditions examined, and the use of a unique cutoff point (EPS) regardless of the data conditions. This article includes two simulation studies to test this validation method under a wider range of conditions, with the purpose of providing it with a higher generalization, and to empirically determine the most suitable EPS considering the data conditions. Results show a good overall performance of the method, the relevance of the different studied factors, and that using a single indiscriminate EPS is not acceptable. Specific guidelines for selecting an appropriate EPS are provided in the discussion.

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]

Copyright of Papeles del Psicólogo is the property of Consejo General de Colegios Oficiales de Psicologos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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).)