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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]

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. [ABSTRACT FROM AUTHOR]

The authors analyze and compare the factor convergence and predictive power of the Revised NEO Personality Inventory (NEO-PI-R) and the Zuckerman-Kuhlman-Aluja Personality Questionnaire (ZKA-PQ/SF) with respect to the Five-Factor Personality Inventory for ICD-11 (FFiCD). A total of 803 White Spanish subjects were analyzed. All the personality domains had significant predictive power with regard to the FFiCD except NEO Openness. The explained variance of the personality domains with respect to FFiCD Negative Affectivity (71% and 77%) and Detachment (56% and 56%) were similar for NEO-PI-R and ZKA-PQ/SF, respectively, but the NEO-PI-R accounted for greater variance for FFiCD Anankastia, Dissociality, and Disinhibition. The FFiCD facets of Rashness, Thrill- Seeking (Disinhibition), and Unassertiveness (Detachment) were located in factors other than those theoretically expected. The authors conclude that normal personality measured by the NEO-PI-R and the ZKA-PQ/SF contribute, in a differential but complementary way, to knowledge of the maladaptive personality measured by the FFiCD. [ABSTRACT FROM AUTHOR]

At present, there are two models of pathological personality represented in two measurement instruments, the Personality Inventory for DSM-5 (PID-5) and the Personality Inventory for ICD-11 (PiCD). Although both instruments have shown high convergence, the PID-5 has the advantage of including facets that might offer greater predictive capacity. An alternative to the PiCD has recently been proposed to overcome this drawback, namely the Five-Factor Personality Inventory for ICD-11 (FFiCD). The present study aims to explore its internal structure and to offer additional validity evidence. Data were collected for a total of 1,409 community individuals from two populations in Spain. They responded to the FFiCD, PiCD, the PID-5/SF, and measures of personality functioning and personality disorder screening (Level of Personality Functioning Scale Self-Report [LPFS-SR] and International Personality Disorder Examination [IPDE]). The internal structure of the FFiCD was analyzed through exploratory factor analysis with oblique and bifactor rotations. The predictive capacity of the domains and facets was examined. The structure reported in the original study was replicated, as were the convergent validity data with respect to the PID-5/SF and the PiCD. Facets were grouped into four factors corresponding to the theoretical domains, including a bipolar Anankastia/Disinhibition factor. High correlations were found with the LPFS-SR, and the patterns of relationships with the IPDE shed light on which aspects of pathological personality contribute most to the 10 traditional personality disorders. In general, validity evidence is provided to support the use of the FFiCD, though four of the 20 facets merit revision so that a simpler factor structure can be obtained. Public Significance Statement: The present study investigated the internal structure of the FFiCD in a large sample and sought to provide additional validity evidence using the PiCD, the PID-5/SF, LPFS-SR, and a measure for screening personality disorders (IPDE). The results support the use of the FFiCD, although four of the 20 facets could be revised for enhanced performance. [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. [ABSTRACT FROM AUTHOR]