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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.

The nonparametric classification (NPC) method has been proven to be a suitable procedure for cognitive diagnostic assessments at a classroom level. However, its nonparametric nature impedes the obtention of a model likelihood, hindering the exploration of crucial psychometric aspects, such as model fit or reliability. Reporting the reliability and validity of scores is imperative in any applied context. The present study proposes the restricted deterministic input, noisy "and" gate (R-DINA) model, a parametric cognitive diagnosis model based on the NPC method that provides the same attribute profile classifications as the nonparametric method while allowing to derive a model likelihood and, subsequently, to compute fit and reliability indices. The suitability of the new proposal is examined by means of an exhaustive simulation study and a real data illustration. The results show that the R-DINA model properly recovers the posterior probabilities of attribute mastery, thus becoming a suitable alternative for comprehensive small-scale diagnostic assessments.

Author(s): Patricia Urieta [sup.1] [sup.2] , Miguel A. Sorrel [sup.3] , Anton Aluja [sup.1] [sup.2] , Ferran Balada [sup.1] [sup.4] , Elena Lacomba [sup.1] [sup.2] , Luis F. García [sup.3] [...], Available data indicate that the frequency of Problematic Smartphone Use (PSU) has been increasing over the years. Although there is some debate as to whether or not this behavior corresponds to true addiction, comorbidity has been found with problematic outcomes typically related to traditional addictions. Thus, there is interest in better understanding which individual variables interact with this behavior. The present study uses a large sex-balanced sample covering a wide age range to shed light on this question. The main purpose of this study is to examine the relationship between personality, assessed by Zuckerman's alternative five-factor model, and PSU. The possible mediating role of individual decision-making styles as well as the effect of sociodemographic variables such as age, sex, and social position are also investigated. The results indicated a strong association between Aggressiveness, Neuroticism, and, to a lesser extent, Sensation Seeking with PSU. The decision-making styles that were most related to the problematic use of smartphones were Avoidant and Spontaneous. Neuroticism, Sensation Seeking, and Aggression explained 24% of the variance of the PSU measure. Only Avoidant showed some incremental validity for this model. However, a mediation analysis by structural equation modeling revealed generally significant indirect effects for Avoidant and Spontaneous, explaining part of the effect of personality on the factor assessed by the PSU measure. The percentage of variance explained for latent scores ranged from 20-32%. The practical implications of the study and future research directions are discussed.

Technology-facilitated sexual violence (TFSV) includes different forms of digital violence, such as online gender-based violence, online gender- and sexuality-based violence, digital sexual harassment, online sexual coercion, and nonconsensual pornography. The aim of this study was to design and validate a measure to assess the perpetration and victimization of each dimension of TFSV. The relationships between the different dimensions and differences by gender and sexual orientation were also analyzed. The participants were a sample of 2,486 adults (69% women) from Spain, aged between 16 and 79 (M = 25.95; DT = 9.809) years. The Technology-Facilitated Sexual Violence Scales were found to be valid and reliable instruments, supporting our recommendation for the use of these scales. Network analysis and solution-based exploratory factor analyses showed that the dimensions of online sexual coercion and nonconsensual pornography clustered together. All the perpetration variables were related to sexism. Finally, cis women and nonheterosexual people reported higher victimization scores overall compared to cis men and heterosexuals, respectively, while cis men reported higher perpetration scores overall than cis women. [ABSTRACT FROM AUTHOR]

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.

Expressed emotion is a construct that has so far been studied in a clinical population, although this work proposes its study in adolescent population without psychopathology. It has three aims: 1) To analyse the psychometric characteristics of the Level of Expressed Emotion Scale in the relationship of the Spanish adolescents with their father and their mother, considering different response format (dichotomic and polytomic); (2) To develop a short version of the scale; (3) To study the validity of the instrument as a measure of emotional climate of normative families. Participants were 827 adolescents from 12 to 17 years old. Data analysis included exploratory and confirmatory factor analysis and structural equation modelling. Results suggested a 4-factor structure in the short version composed by 16 items, with adequate psychometric properties. This is presented as an innovative and appropriate tool for the assessment of the emotional family climate, both for researchers and clinicians. [ABSTRACT FROM AUTHOR]

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.