Development and Validation of Technology-Facilitated Sexual Violence Perpetration and Victimization Scales Among Adults.

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.

Spanish Adaptation of the Need for Affect Questionnaire (NAQ and NAQ-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.

FoCo: A Shiny App for Formative Assessment using Cognitive Diagnosis Modeling / [FoCo: una aplicación Shiny para la evaluación formativa usando modelos de diagnóstico cognitivo]

Combining formative and summative evaluations could improve assessment. Cognitive diagnosis modeling (CDM) has been proposed as a tool for diagnosing students’ strengths and weaknesses in formative assessment. However, there is no user-friendly software to implement it. For this reason, a Shiny app, FoCo, has been developed (https://foco.shinyapps.io/FoCo/), to conduct CDM and classical test theory analyses. The responses from 86 undergraduate students to a research methods course examination were analyzed. Students’ strengths and needs were diagnosed concerning their dominance of the syllabus contents and the first three competencies in Bloom’s taxonomy. The validity of the results was analyzed. The exam showed acceptable about evaluating students’ knowledge, as students with similar scores showed different strengths and weaknesses. Additionally, these attributes were found to predict different relevant criteria. It is expected that FoCo’s easiness to use promotes the employment of CDM in real educational settings.(AU)

La combinación de evaluaciones formativas y sumativas podría mejorar la evaluación. El modelado de diagnóstico cognitivo (MDC) se ha propuesto para diagnosticar fortalezas y debilidades de estudiantes en la evaluación formativa. Sin embargo, ningún software permite implementarlo fácilmente. Así, se ha desarrollado FoCo (https://foco.shinyapps.io/FoCo/), permitiendo realizar análisis MDC y teoría clásica de tests. Se analizaron respuestas de 86 estudiantes de grado a un examen de métodos de investigación, diagnosticándose sus fortalezas y necesidades en cuanto a su dominio de los contenidos de la asignatura y las tres primeras competencias de la taxonomía de Bloom y se analizó la validez de los resultados. El análisis ha sido informativo, ya que para estudiantes con puntuaciones similares ha sido posible detectar diferentes fortalezas y debilidades. Además, se encontró que estos atributos predicen criterios relevantes. Se espera que FoCo facilite el uso de MDC en contextos educativos.(AU)

Is exploratory factor analysis always to be preferred? A systematic comparison of factor analytic techniques throughout the confirmatory-exploratory continuum.

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).

Range Restriction Affects Factor Analysis: Normality, Estimation, Fit, Loadings, and Reliability.

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.

On Bank Assembly and Block Selection in Multidimensional Forced-Choice Adaptive Assessments.

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.

Development and Validation of the Self-efficacy for Writing and Defending Academic Texts Scale1 / El desarrollo y la validación de la escala de autoeficacia para escribir y defender textos académicos

Writing and defending a thesis is a requirement to earn a university degree. Previous findings indicate that self-efficacy is related to academic performance. However, no existing tools register students’ perception of efficacy towards writing and defining academic texts. Our purpose was to develop and validate such a scale. Scale scores content, structural, convergent, and criterion-related validity as well as the measurement invariance across sex was evaluated using data from 418 students from 23 Spanish universities. Our findings showed that the scale holds a unidimensional structure that is invariant across sex. Data also supported the convergent validity, with correlations with self-efficacy and anxiety measures. The scale could track the effect of an educational intervention designed to improve students’ writing and defending academic texts skills, and the scores were related to performance on a writing task. Norms are provided to facilitate the interpretation of the scale scores.(AU)

Redactar y defender una tesina es un requisito para obtener un título universitario. La investigación previa indica que la autoeficacia está relacionada con el rendimiento académico. Sin embargo, no existen instrumentos que registren la percepción de la eficacia de los estudiantes para escribir y definir textos académicos. Nuestro objetivo fue desarrollar y validar una escala de este tipo. Se recopilaron pruebas de validez de contenido, estructural, convergente y de criterio y de invarianza de medida entre sexos, utilizando datos de 418 estudiantes de 23 universidades españolas. La escala presenta una estructura unidimensional invariante en cuanto al sexo. También se encontraron correlaciones con medidas de autoeficacia y ansiedad. La escala pudo seguir el efecto de una intervención educativa diseñada para mejorar las habilidades de escritura y defensa de textos académicos, y se encontró relación con el rendimiento en una tarea de escritura. Se proporcionan baremos para interpretar las puntuaciones.(AU)

Improving reliability estimation in cognitive diagnosis modeling.

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.

Borderline, where are you? A psychometric approach to the personality domains in the International Classification of Diseases, 11th Revision (ICD-11).

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).

Spanish Adaptation of the Need for Affect Questionnaire (NAQ and NAQ-S) / Adaptación Española del Cuestionario de Necesidad de Afecto (NAQ y NAQ-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.(AU)

Antecedentes: La necesidad de afecto (NA) se refiere a las diferencias individuales en la motivación para buscar o evitar situaciones y actividades que inducen emoción. La investigación previa ha demostrado que la NA es un constructo relevante para entender procesos psicológicos relacionados con el afecto. La presente investigación adaptó al castellano el cuestionario de necesidad de afecto (NAQ), así como su versión breve (NAQ-S). Por tanto, examinamos las evidencias de validez y fiabilidad de las puntuaciones en ambos cuestionarios. Método: En el Estudio 1 (N = 416), exploramos las propiedades psicométricas de las puntuaciones en la adaptación del NAQ, así como las relaciones con medidas de otros constructos psicológicos relevantes. En el Estudio 2 (N = 118), analizamos la fiabilidad y la validez de las puntuaciones en la adaptación del NAQ-S. Resultados: Se encontraron dos factores (i.e., Aproximación a la Emoción y Evitación de la Emoción) y la consistencia interna, así como la fiabilidad test-retest, de las puntuaciones en ambos cuestionarios fueron adecuadas. Las relaciones con otras medidas proporcionaron evidencia positiva de la validez concurrente. Conclusiones: Las puntuaciones obtenidas en ambos cuestionarios mostraron validez y fiabilidad, por lo que deberían ser considerados en la investigación futura relacionada con la NA.(AU)

Construyendo test adaptativos de elección forzosa “On The Fly” para la medición de la personalidad / Building adaptive forced choice tests “On The Fly” for personality measurement

Los nuevos desarrollos metodológicos y tecnológicos de la última década permiten resolver, o al menos atenuar, los problemas psicométricos de los test de elección forzosa (EF) para la medición de la personalidad. En estas pruebas, a la persona evaluada se le muestran bloques de dos o más frases de parecida deseabilidad social, entre las que debe elegir aquella que le represente mejor. De esta manera, los test de EF buscan reducir los sesgos de respuesta en pruebas de autoinforme. No obstante, su uso no está exento de riesgos y complicaciones si no se elaboran adecuadamente. Afortunadamente, los nuevos modelos psicométricos permiten modelar las respuestas en este tipo de test, así como optimizar su construcción. Más aún, permiten la construcción de Test Adaptativos Informatizados de EF (TAI-EF) “on-the-fiy”, en los que cada bloque se construye en el mismo momento de aplicación, emparejando óptimamente las frases de un banco previamente calibrado.(AU)

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.(AU)

Psychometric properties of the Five-Factor Personality Inventory for ICD-11 (FFiCD) in Spanish community samples.

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).

A genetic algorithm for optimal assembly of pairwise forced-choice questionnaires.

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.

Determining the Number of Attributes in Cognitive Diagnosis Modeling.

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.

Improving Accuracy and Usage by Correctly Selecting: The Effects of Model Selection in Cognitive Diagnosis Computerized Adaptive Testing.

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.

Balancing fit and parsimony to improve Q-matrix validation.

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-R2 ). 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.

Improving Robustness in Q-Matrix Validation Using an Iterative and Dynamic Procedure.

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.

It Must have been Burnout: Prevalence and Related Factors among Spanish PhD Students.

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.

Adapting cognitive diagnosis computerized adaptive testing item selection rules to traditional item response theory.

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.

It must have been burnout: prevalence and related factors among Spanish PhD students

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

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