One important problem in the measurement of non-cognitive characteristics such as personality traits and attitudes is that it has traditionally been made through Likert scales, which are susceptible to response biases such as social desirability (SDR) and acquiescent (ACQ) responding. Given the variability of these response styles in the population, ignoring their possible effects on the scores may compromise the fairness and the validity of the assessments. Also, response-style-induced errors of measurement can affect the reliability estimates and overestimate convergent validity by correlating higher with other Likert-scale-based measures. Conversely, it can attenuate the predictive power over non-Likert-based indicators, given that the scores contain more errors. This study compares the validity of the Big Five personality scores obtained: (1) ignoring the SDR and ACQ in graded-scale items (GSQ), (2) accounting for SDR and ACQ with a compensatory IRT model, and (3) using forced-choice blocks with a multi-unidimensional pairwise preference model (MUPP) variant for dominance items. The overall results suggest that ignoring SDR and ACQ offered the worst validity evidence, with a higher correlation between personality and SDR scores. The two remaining strategies have their own advantages and disadvantages. The results from the empirical reliability and the convergent validity analysis indicate that when modeling social desirability with graded-scale items, the SDR factor apparently captures part of the variance of the Agreeableness factor. On the other hand, the correlation between the corrected GSQ-based Openness to Experience scores, and the University Access Examination grades was higher than the one with the uncorrected GSQ-based scores, and considerably higher than that using the estimates from the forced-choice data. Conversely, the criterion-related validity of the Forced Choice Questionnaire (FCQ) scores was similar to the results found in meta-analytic studies, correlating higher with Conscientiousness. Nonetheless, the FCQ-scores had considerably lower reliabilities and would demand administering more blocks. Finally, the results are discussed, and some notes are provided for the treatment of SDR and ACQ in future studies.

Multidimensional forced-choice questionnaires are widely regarded in the personnel selection literature for their ability to control response biases. Recently developed IRT models usually rely on the assumption that item parameters remain invariant when they are paired in forced-choice blocks, without giving it much consideration. This study aims to test this assumption empirically on the MUPP-2PL model, comparing the parameter estimates of the forced-choice format to their graded-scale equivalent on a Big Five personality instrument. The assumption was found to hold reasonably well, especially for the discrimination parameters. In the cases in which it was violated, we briefly discuss the likely factors that may lead to non-invariance. We conclude discussing the practical implications of the results and providing a few guidelines for the design of forced-choice questionnaires based on the invariance assumption. Los cuestionarios de elección forzosa multidimensionales son bastante apreciados en la literatura de selección de personal por su capacidad para controlar los sesgos de respuesta. Los modelos de TRI desarrollados recientemente normalmente asumen que los parámetros de los ítems permanecen invariantes cuando se emparejan en bloques de elección forzosa, sin dedicarle mucha atención. Este estudio tiene como objetivo poner a prueba empíricamente este supuesto en el modelo MUPP-2PL, comparando las estimaciones de los parámetros del formato de elección forzosa con su equivalente en escala graduada, en un instrumento de personalidad ,Big Five,. Se encontró que el supuesto se cumplía razonablemente bien, especialmente para los parámetros de discriminación. En los casos en los que no se cumplió se discuten brevemente los posibles factores que pueden dar lugar a no invarianza. Concluimos discutiendo las implicaciones prácticas de los resultados y proponiendo algunas pautas para el diseño de cuestionarios de elección forzosa basados en el supuesto de invarianza.

The use of multidimensional forced-choice questionnaires has been proposed as a means of improving validity in the assessment of non-cognitive attributes in high-stakes scenarios. However, the reduced precision of trait estimates in this questionnaire format is an important drawback. Accordingly, this article presents an optimization procedure for assembling pairwise forced-choice questionnaires while maximizing posterior marginal reliabilities. This procedure is performed through the adaptation of a known genetic algorithm (GA) for combinatorial problems. In a simulation study, the efficiency of the proposed procedure was compared with a quasi-brute-force (BF) search. For this purpose, five-dimensional item pools were simulated to emulate the real problem of generating a forced-choice personality questionnaire under the five-factor model. Three factors were manipulated: (1) the length of the questionnaire, (2) the relative item pool size with respect to the questionnaire’s length, and (3) the true correlations between traits. The recovery of the person parameters for each assembled questionnaire was evaluated through the squared correlation between estimated and true parameters, the root mean square error between the estimated and true parameters, the average difference between the estimated and true inter-trait correlations, and the average standard error for each trait level. The proposed GA offered more accurate trait estimates than the BF search within a reasonable computation time in every simulation condition. Such improvements were especially important when measuring correlated traits and when the relative item pool sizes were higher. A user-friendly online implementation of the algorithm was made available to the users.

Cognitive diagnosis models (CDMs) are used in educational, clinical, or personnel selection settings to classify respondents with respect to discrete attributes, identifying strengths and needs, and thus allowing to provide tailored training/treatment. As in any assessment, an accurate reliability estimation is crucial for valid score interpretations. In this sense, most CDM reliability indices are based on the posterior probabilities of the estimated attribute profiles. These posteriors are traditionally computed using point estimates for the model parameters as approximations to their populational values. If the uncertainty around these parameters is unaccounted for, the posteriors may be overly peaked, deriving into overestimated reliabilities. This article presents a multiple imputation (MI) procedure to integrate out the model parameters in the estimation of the posterior distributions, thus correcting the reliability estimation. A simulation study was conducted to compare the MI procedure with the traditional reliability estimation. Five factors were manipulated: the attribute structure, the CDM model (DINA and G-DINA), test length, sample size, and item quality. Additionally, an illustration using the Examination for the Certificate of Proficiency in English data was analyzed. The effect of sample size was studied by sampling subsets of subjects from the complete data. In both studies, the traditional reliability estimation systematically provided overestimated reliabilities, whereas the MI procedure offered more accurate results. Accordingly, practitioners in small educational or clinical settings should be aware that the reliability estimation using model parameter point estimates may be positively biased. R codes for the MI procedure are made available

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.

Forced-choice (FC) questionnaires have gained increasing attention as a strategy to reduce social desirability in self-reports, supported by advancements in confirmatory models that address the ipsativity of FC test scores. However, these models assume a known dimensionality and structure, which can be overly restrictive or fail to fit the data adequately. Consequently, exploratory models can be required, with accurate dimensionality assessment as a critical first step. FC questionnaires also pose unique challenges for dimensionality assessment, due to their inherently complex multidimensional structures. Despite this, no prior studies have systematically evaluated dimensionality assessment methods for FC data. To fill this gap, the present study examines five commonly used methods: the Kaiser Criterion, Empirical Kaiser Criterion, Parallel Analysis (PA), Hull Method, and Exploratory Graph Analysis. A Monte Carlo simulation study was conducted, manipulating key design features of FC questionnaires, such as the number of dimensions, items per dimension, response formats (e.g., binary vs. graded), and block composition (e.g., inclusion of heteropolar and unidimensional blocks), as well as factor loadings, inter-factor correlations, and sample size. Results showed that the Maximal Kaiser Criterion and PA methods outperformed the others, achieving higher accuracy and lower bias. Performance improved particularly when heteropolar or unidimensional blocks were included or when the questionnaire length increased. These findings emphasize the importance of thoughtful FC test design and provide practical recommendations for improving dimensionality assessment in this format.