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The Personal Need for Structure (PNS) scale assesses individuals' tendency to seek out clarity and structured ways of understanding and interacting with their environment. The main aim of this study was to adapt the PNS scale to Spanish and assess its psychometric properties. There are two versions of the PNS scale being used, which vary in the number of dimensions (1 vs. 2), and in the number of items (12 vs. 11; because one version excludes Item 5). Therefore, an additional aim of this study was to compare the two existing versions of the PNS scale. This comparison aimed to address the debate regarding the inclusion of Item 5, and the number of dimensions that comprise the PNS scale. A sample of 735 individuals was collected. First, through an approach combining exploratory and confirmatory analyses, evidence was found in favor of the scale being composed of two related but distinguishable factors: Desire for Structure and Response to the Lack of Structure. Scores on these subscales showed acceptable internal consistency and test-retest reliability. Evidence supporting the invariance of the internal structure across sociodemographic variables such as gender and age was found. Validity evidence was also analyzed by examining the relationships with other relevant measures. The results indicated that Item 5 can be excluded without reducing scores validity or reliability, which supports preceding research in the literature. In conclusion, the PNS scale was satisfactorily adapted to and validated in Spanish and its use in this context is recommended.

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., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Objective: The purpose of this research was to design and psychometrically validate a new instrument (the Biobehavioural Pain and Movement Questionnaire/BioPMovQ), which assesses the relationship between pain and various factors related to motor behaviour from a biobehavioural perspective., Methods: A mixed-method design combining a qualitative study with an observational and cross-sectional study was employed to develop (content validity) and psychometrically validate (construct validity, reliability and concurrent/discriminant validity) a new instrument. A total of 200 patients with chronic musculoskeletal pain were recruited., Results: According to the exploratory factor analysis, the final version of the BioPMovQ consists of 16 items distributed across 4 subscales (1, disability, 2, self-efficacy for physical activity; 3, movement avoidance behaviours; and 4, self-perceived functional ability), all with an eigen value greater than 1, explaining 55.79% of the variance. The BioPMovQ showed high internal consistency (Cronbach's α  = 0.82; McDonald's ω  = 0.83). The intraclass correlation coefficient was 0.86 (95% confidence interval 0.76 to 0.91), which was considered to demonstrate excellent test-retest reliability. The standard error of measurement and minimal detectable change were 3.43 and 8.04 points, respectively. No floor or ceiling effects were identified. There was a positive, significant and moderate magnitude correlation with the Graded Chronic Pain Scale ( r  = 0.54), kinesiophobia ( r  = 0.60), pain catastrophising ( r  = 0.44) and chronic pain self-efficacy ( r  = -0.31)., Conclusion: The BioPMovQ showed good psychometric properties. Based on the findings of this study, the BioPMovQ can be used in research and clinical practice to assess patients with chronic musculoskeletal pain., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer CC-L declared a shared affiliation with the author AP-A to the handling editor at the time of review. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 La Touche, Paris-Alemany, Pardo-Montero, Miñambres-Martín, Mercado-Romero, de la Rosa-Díaz, Sorrel and Grande-Alonso.)

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 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.

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., (© 2022. The Author(s).)

Background: Need for affect (NA) refers to individual differences in the motivation to approach or avoid emotion-inducing situations and activities. Prior research has demonstrated that NA is a relevant construct for understanding psychological processes related to affect. The present study aimed to adapt and validate the English version of the Need for Affect Questionnaire (NAQ), as well as the short version (i.e., the NAQ-S), to Spanish. We examined evidence of validity and reliability in the NAQ and NAQ-S scores., Method: Study 1 ( N = 416) analyzed the psychometric properties of scores from the Spanish adaptation of the NAQ, as well as the relationships with measures of other relevant psychological constructs. Study 2 ( N = 118) tested the validity and reliability of scores from the Spanish adaptation of the NAQ-S., Results: We found two main factors (i.e., Emotion Approach and Emotion Avoidance), and the internal consistency of both the NAQ and the NAQ-S scores, as well as their test-retest reliability, were adequate. Relationships with other measures were in line with prior research, providing positive evidence of concurrent validity., Conclusions: Scores from both instruments showed validity and reliability, so should be considered in future research related to NA.

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) 2023 APA, all rights reserved).

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).

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 = .50, .70, and .90), and restriction size (from R = 1, .90, .80, and so on till .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., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

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., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

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.

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.

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., (© 2021. The Author(s).)

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. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The validity of cross-cultural comparisons of test scores requires that scores have the same meaning across cultures, which is usually tested by checking the invariance of the measurement model across groups. In the last decade, a large number of studies were conducted to verify the equivalence across cultures of the dimensional Alternative Model of Personality Disorders (DSM-5 Section III). These studies have provided information on configural invariance (i.e., the facets that compose the domains are the same) and metric invariance (i.e., facet-domain relationships are equal across groups), but not on the stricter scalar invariance (i.e., the baseline levels of the facets are the same), which is a prerequisite for meaningfully comparing group means. The present study aims to address this gap. The Personality Inventory for DSM-5 (PID-5) was administered to five samples differing on country and language (Belgium, Catalonia, France, Spain, and Switzerland), with a total of 4,380 participants. Configural and metric invariance were supported, denoting that the model structure was stable across samples. Partial scalar invariance was supported, being minimal the influence of non-invariant facets. This allowed cross-cultural mean comparisons. Results are discussed in light of the sample composition and a possible impact of culture on development of psychopathology., (Copyright © 2021. Published by Elsevier B.V.)

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-R 2 ). 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., (© 2020 The British Psychological Society.)

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., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2020.)

Cognitive diagnosis models (CDMs) allow classifying respondents into a set of discrete attribute profiles. The internal structure of the test is determined in a Q-matrix, whose correct specification is necessary to achieve an accurate attribute profile classification. Several empirical Q-matrix estimation and validation methods have been proposed with the aim of providing well-specified Q-matrices. However, these methods require the number of attributes to be set in advance. No systematic studies about CDMs dimensionality assessment have been conducted, which contrasts with the vast existing literature for the factor analysis framework. To address this gap, the present study evaluates the performance of several dimensionality assessment methods from the factor analysis literature in determining the number of attributes in the context of CDMs. The explored methods were parallel analysis, minimum average partial, very simple structure, DETECT, empirical Kaiser criterion, exploratory graph analysis, and a machine learning factor forest model. Additionally, a model comparison approach was considered, which consists in comparing the model-fit of empirically estimated Q-matrices. The performance of these methods was assessed by means of a comprehensive simulation study that included different generating number of attributes, item qualities, sample sizes, ratios of the number of items to attribute, correlations among the attributes, attributes thresholds, and generating CDM. Results showed that parallel analysis (with Pearson correlations and mean eigenvalue criterion), factor forest model, and model comparison (with AIC) are suitable alternatives to determine the number of attributes in CDM applications, with an overall percentage of correct estimates above 76% of the conditions. The accuracy increased to 97% when these three methods agreed on the number of attributes. In short, the present study supports the use of three methods in assessing the dimensionality of CDMs. This will allow to test the assumption of correct dimensionality present in the Q-matrix estimation and validation methods, as well as to gather evidence of validity to support the use of the scores obtained with these models. The findings of this study are illustrated using real data from an intelligence test to provide guidelines for assessing the dimensionality of CDM data in applied settings., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Nájera, Abad and Sorrel.)

In the context of cognitive diagnosis models (CDMs), a Q-matrix reflects the correspondence between attributes and items. The Q-matrix construction process is typically subjective in nature, which may lead to misspecifications. All this can negatively affect the attribute classification accuracy. In response, several methods of empirical Q-matrix validation have been developed. The general discrimination index (GDI) method has some relevant advantages such as the possibility of being applied to several CDMs. However, the estimation of the GDI relies on the estimation of the latent group sizes and success probabilities, which is made with the original (possibly misspecified) Q-matrix. This can be a problem, especially in those situations in which there is a great uncertainty about the Q-matrix specification. To address this, the present study investigates the iterative application of the GDI method, where only one item is modified at each step of the iterative procedure, and the required cutoff is updated considering the new parameter estimates. A simulation study was conducted to test the performance of the new procedure. Results showed that the performance of the GDI method improved when the application was iterative at the item level and an appropriate cutoff point was used. This was most notable when the original Q-matrix misspecification rate was high, where the proposed procedure performed better 96.5% of the times. The results are illustrated using Tatsuoka's fraction-subtraction data set., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2020.)

Recent studies in different countries indicate that PhD students are more vulnerable to psychological disorders compared to the general population. No such data are available for the Spanish population. This study addresses this issue by studying prevalence rates and factors related to a common response to prolonged stress such as burnout syndrome. Burnout, emotional abilities, resilience, satisfaction with the dissertation advisor, and sociodemographic data were collected from 305 PhD students. The results indicated that the burnout rates are high in this group, especially for the emotional exhaustion dimension. Different linear regression models explained between 14% and 41% of the overall burnout scores variance and its dimensions. The psychological variables and the satisfaction with the dissertation advisor were the most relevant predictors. Consistent with what has been found in other countries, the evidence found indicates that the mental state of PhD students in Spain is alarming. The results of this study have important implications for the design and implementation of interventions to alleviate this problem.

Currently, there are two predominant approaches in adaptive testing. One, referred to as cognitive diagnosis computerized adaptive testing (CD-CAT), is based on cognitive diagnosis models, and the other, the traditional CAT, is based on item response theory. The present study evaluates the performance of two item selection rules (ISRs) originally developed in the CD-CAT framework, the double Kullback-Leibler information (DKL) and the generalized deterministic inputs, noisy "and" gate model discrimination index (GDI), in the context of traditional CAT. The accuracy and test security associated with these two ISRs are compared to those of the point Fisher information and weighted KL using a simulation study. The impact of the trait level estimation method is also investigated. The results show that the new ISRs, particularly DKL, could be used to improve the accuracy of CAT. Better accuracy for DKL is achieved at the expense of higher item overlap rate. Differences among the item selection rules become smaller as the test gets longer. The two CD-CAT ISRs select different types of items: items with the highest possible a parameter with DKL, and items with the lowest possible c parameter with GDI. Regarding the trait level estimator, expected a posteriori method is generally better in the first stages of the CAT, and converges with the maximum likelihood method when a medium to large number of items are involved. The use of DKL can be recommended in low-stakes settings where test security is less of a concern., Competing Interests: The authors have declared that no competing interests exist.

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., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2019.)

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., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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.

Research related to the fit evaluation at the item level involving cognitive diagnosis models (CDMs) has been scarce. According to the parsimony principle, balancing goodness of fit against model complexity is necessary. General CDMs require a larger sample size to be estimated reliably, and can lead to worse attribute classification accuracy than the appropriate reduced models when the sample size is small and the item quality is poor, which is typically the case in many empirical applications. The main purpose of this study was to systematically examine the statistical properties of four inferential item-fit statistics: S - X 2 , the likelihood ratio (LR) test, the Wald (W) test, and the Lagrange multiplier (LM) test. To evaluate the performance of the statistics, a comprehensive set of factors, namely, sample size, correlational structure, test length, item quality, and generating model, is systematically manipulated using Monte Carlo methods. Results show that the S - X 2 statistic has unacceptable power. Type I error and power comparisons favor LR and W tests over the LM test. However, all the statistics are highly affected by the item quality. With a few exceptions, their performance is only acceptable when the item quality is high. In some cases, this effect can be ameliorated by an increase in sample size and test length. This implies that using the above statistics to assess item fit in practical settings when the item quality is low remains a challenge., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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. (PsycINFO Database Record, ((c) 2016 APA, all rights reserved).)

IQ summary scores may not involve equivalent psychological meaning for different educational levels. Ultimately, this relates to the distinction between constructs and measurements. Here, we explore this issue studying the standardization of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) for Spain. A representative sample of 743 individuals (374 females and 369 males) who completed the 15 subtests comprising this intelligence battery was considered. We analyzed (a) the best latent factor structure for modeling WAIS-IV subtest performance, (b) measurement invariance across educational levels, and (c) the relationships of educational level/attainment with latent factors, Full Scale IQ (FSIQ), and index factor scores. These were the main findings: (a) the bifactor model provides the best fit; (b) there is partial invariance, and therefore it is concluded that the battery is a proper measure of the constructs of interest for the educational levels analyzed (nevertheless, the relevance of g decreases at high educational levels); (c) at the latent level, g and, to a lesser extent, Verbal Comprehension and Processing Speed, are positively related to educational level/attainment; (d) despite the previous finding, we find that Verbal Comprehension and Processing Speed factor index scores have reduced incremental validity beyond FSIQ; and (e) FSIQ is a slightly biased measure of g. (PsycINFO Database Record, ((c) 2016 APA, all rights reserved).)

Background: Children's pro-ecological behaviors are usually registered using scales based on the idea of a simple connection between attitudes and behaviors. However, this relationship seems to be more complicated. The Children's Ecological Behavior (CEB) Scale has been proposed as an alternative. Based on the Rasch model, it considers the different efforts needed to conduct a series of behaviors. This paper presents an improved adaptation of the CEB to Spanish population., Method: We back-translated the CEB into Spanish, increased the number of behaviors and collected data from 6- to 12-year-olds, using a game format procedure., Results: The scale can detect differences in the effort needed to perform various behaviors. Children's pro-ecological attitudes and behaviors are positively related. No relationship was found between parents' and children's pro-ecological attitudes and behaviors., Conclusions: The Spanish version of the CEB scale emerges as a reliable tool to measure children's pro-ecological behaviors.