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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]

There is growing interest in integrating explanatory and predictive research practices in psychological research. For this integration to be successful, the psychologist's toolkit must incorporate standard procedures that enable a direct estimation of the prediction error, such as cross-validation (CV). Despite their apparent simplicity, CV methods are intricate, and thus it is crucial to adapt them to specific contexts and predictive metrics. This study delves into the performance of different CV methods in estimating the prediction error in the R 2 and MSE metrics in regression analysis, ubiquitous in psychological research. Current approaches, which rely on the 5- or 10-fold rule of thumb or on the squared correlation between predicted and observed values, present limitations when computing the prediction error in the R 2 metric, a widely used statistic in the behavioral sciences. We propose the use of an alternative method that overcomes these limitations and enables the computation of the leave-one-out (LOO) in the R 2 metric. Through two Monte Carlo simulation studies and the application of CV to the data from the Many Labs Replication Project, we show that the LOO consistently has the best performance. The CV methods discussed in the present study have been implemented in the R package OutR2. [ABSTRACT FROM AUTHOR]

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]

Most existing diagnostic models are developed to detect whether students have mastered a set of skills of interest, but few have focused on identifying what scientific misconceptions students possess. This article developed a general dual‐purpose model for simultaneously estimating students' overall ability and the presence and absence of misconceptions. The expectation‐maximization algorithm was developed to estimate the model parameters. A simulation study was conducted to evaluate to what extent the parameters can be accurately recovered under varied conditions. A set of real data in science education was also analyzed to examine the viability of the proposed model in practice. [ABSTRACT FROM AUTHOR]

Cognitive diagnostic models (CDMs) are increasingly being used in various assessment contexts to identify cognitive processes and provide tailored feedback. However, the most commonly used estimation method for CDMs, marginal maximum likelihood estimation with Expectation–Maximization (MMLE-EM), can present difficulties when sample sizes are small. This study compares the results of different estimation methods for CDMs under varying sample sizes using simulated and empirical data. The methods compared include MMLE-EM, Bayes modal, Markov chain Monte Carlo, a non-parametric method, and a parsimonious parametric model such as Restricted DINA. We varied the sample size, and assessed the bias in the estimation of item parameters, the precision in attribute classification, the bias in the reliability estimate, and computational cost. The findings suggest that alternative estimation methods are preferred over MMLE-EM under low sample-size conditions, whereas comparable results are obtained under large sample-size conditions. Practitioners should consider using alternative estimation methods when working with small samples to obtain more accurate estimates of CDM parameters. This study aims to maximize the potential of CDMs by providing guidance on the estimation of the parameters. [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]

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]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [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]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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-[sup X2], 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-[sup X2] 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. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

AbstractForced-choice (FC) questionnaires have gained scientific interest over the last decades. However, the inclusion of unequally keyed item pairs in FC questionnaires remains a subject of debate, as there is evidence supporting both their usage and avoidance. Designing unequally keyed pairs may be more difficult when considering social desirability, as they might allow the identification of ideal responses. Nevertheless, they may enhance the reliability and the potential for normative interpretation of scores. To empirically investigate this topic, data were collected from 1,125 undergraduate Psychology students who completed a personality item pool measuring the Big Five personality traits in Likert-type format and two FC questionnaires (with and without unequally keyed pairs). These questionnaires were compared in terms of reliability, convergent and criterion validity, and ipsativity of the scores, along with insights on the construction process. While constructing questionnaires with unequally keyed blocks presented challenges in matching items on their social desirability, the differences observed in terms of reliability, validity, or ipsativity were sporadic and lacked systematic patterns. This suggests that neither questionnaire format exhibited a clear superiority. Given these results, it is recommended using only equally keyed blocks to minimize potential validity issues associated with response biases. [ABSTRACT FROM AUTHOR]

The current research asks whether children's judgments of harmful actions toward animals depend on animals' perceived attractiveness. In Study 1, primary school children (N = 359) rated the perceived attractiveness of six animals and judged how severe it is to hurt them, as compared to moral transgressions, social-conventional transgressions, and personal choices. Hurting attractive animals was perceived as severe as hurting another child, while hurting unattractive animals was evaluated as less serious than social-conventional transgressions. In Study 2, we experimentally tested whether the attractiveness of animals rated as unattractive in Study 1 could be influenced by an environmental education intervention. After the intervention, children in the experimental group (N = 21) rated unattractive animals as more attractive than before the intervention, and this led to judging harming these animals more severely than before the intervention. No changes were found in the control group (N = 20). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 conscientiousness 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. [ABSTRACT FROM AUTHOR]

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. [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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Despite the globalization of HRM, there is a dearth of research on the potential use of contextualized selection instruments such as situational judgment tests (SJTs) in other countries than those where the selection instruments were originally developed. Therefore, two studies are conducted to examine the transportability of an integrity SJT that was originally developed in the United States to a Spanish context. Study 1 showed that most SJT scenarios (16 out of 19) that were developed in the United States were also considered realistic in a Spanish context. In Study 2, the item option endorsement patterns converged to the original scoring scheme, with the exception of two items. In addition, there were high correlations between the original US empirical scoring scheme and two empirical scoring schemes that were tailored to the Spanish context (i.e., mode consensus scoring and proportional consensus scoring). Finally, correlations between the SJT integrity scores and ratings on a self-report integrity measure did not differ significantly from each other according to the type of scoring key (original US scoring vs. Spanish scoring keys). Overall, these results shed light on potential issues and solutions related to the cross-cultural use of contextualized selection instruments such as SJTs. [ABSTRACT FROM AUTHOR]

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 proecological attitudes and behaviors. Conclusions: The Spanish version of the CEB scale emerges as a reliable tool to measure children's proecological behaviors. [ABSTRACT FROM AUTHOR]