Dimensional profiling of psychopathology in children and adolescents based on the K-SADS-PL and an analysis of the construct validity of two ADHD symptom dimensions.

OBJECTIVES: The traditional view on psychiatric disorders as categorical and distinct is being challenged by perspectives emphasizing the relevance of dimensional and transdiagnostic assessment. However, most diagnostic instruments are based on a categorical view with a threshold-approach to disease classification. METHODS: We here describe algorithms for dimensionalizing the psychopathological ratings of the widely used diagnostic interview for children and adolescents, the Kiddie-Schedule for Affective Disorders and Schizophrenia - Present and Lifetime Version (K-SADS-PL). We further evaluate the criterion-related construct validity of the dimensionalized attention-deficit/hyperactivity disorder (ADHD) scales using Rasch models in a sample of 590 children (mean age 10.29 (.36), 49% girls). RESULTS: The algorithms generate scores of current symptom load, i.e., the sum of clinician-rated symptoms within each disorder assessed with the interview. We found support for counting symptoms of inattention and hyperactivity/impulsivity, respectively, but not for a single combined ADHD scale. CONCLUSIONS: The algorithms constitute an initial step in creating a framework for clinician-rated dimensional analyses of symptoms derived from the K-SADS-PL, but future studies are needed to further evaluate the construct validity of the remaining scales and the reliability and clinical utility of the method. We believe that our proposed algorithms offer a novel method of dimensional psychopathological assessment, which can be applied in multiple branches of child and adolescent psychiatry.

Linking essay-writing tests using many-facet models and neural automated essay scoring.

For essay-writing tests, challenges arise when scores assigned to essays are influenced by the characteristics of raters, such as rater severity and consistency. Item response theory (IRT) models incorporating rater parameters have been developed to tackle this issue, exemplified by the many-facet Rasch models. These IRT models enable the estimation of examinees' abilities while accounting for the impact of rater characteristics, thereby enhancing the accuracy of ability measurement. However, difficulties can arise when different groups of examinees are evaluated by different sets of raters. In such cases, test linking is essential for unifying the scale of model parameters estimated for individual examinee-rater groups. Traditional test-linking methods typically require administrators to design groups in which either examinees or raters are partially shared. However, this is often impractical in real-world testing scenarios. To address this, we introduce a novel method for linking the parameters of IRT models with rater parameters that uses neural automated essay scoring technology. Our experimental results indicate that our method successfully accomplishes test linking with accuracy comparable to that of linear linking using few common examinees.

Performance of a Rasch-based method for group comparisons of longitudinal change and response shift at the item level in PRO data: A simulation study.

The growing interest in patient perception and experience in healthcare has led to an increase in the use of patient-reported outcomes (PRO) data. However, chronically ill patients may regularly adapt to their disease and, as a consequence, might change their perception of the PRO being measured. This phenomenon named response shift (RS) may occur differently depending on clinical and individual characteristics. The RespOnse Shift ALgorithm at the Item level (ROSALI), a method for RS analysis at the item level based on Rasch models, has recently been extended to explore heterogeneity of item-level RS between two groups of patients. The performances of ROSALI in terms of RS detection at the item level and biases of estimated differences in latent variable means were assessed. A simulation study was performed to investigate four scenarios: no RS, RS in only one group, RS affecting both groups either in a similar or a different way. Performances of ROSALI were assessed using rates of false detection of RS when no RS was simulated and a set of criteria (presence of RS, correct identification of items and groups affected by RS) when RS was simulated. Rates of false detection of RS were low indicating that ROSALI satisfactorily prevents from mistakenly inferring RS. ROSALI is able to detect RS and identify the item and group(s) affected when RS affects all response categories of an item in the same way. The performances of ROSALI depend mainly on the sample size and the degree of heterogeneity of item-level RS.

The development and initial validation of the Breast Cancer Recurrence instrument (BreastCaRe)-a patient-reported outcome measure for detecting symptoms of recurrence after breast cancer.

PURPOSE: Patient-reported outomes (PRO) may facilitate prompt treatment. We describe the development and psychometric properties of the first instrument to monitor for symptoms of breast cancer (BC) recurrence. METHODS: This study is nested in the MyHealth randomized trial of nurse-led follow-up based on electronically-collected PROs. We constructed items assessing symptoms of potential recurrence through expert interviews with six BC specialists in Denmark. Semi-structured cognitive interviews were carried out with a patient panel to assess acceptability and comprehensibility. Items were subsequently tested in a population of 1170 women 1-10 years after completing BC treatment. We carried out multiple-groups confirmatory factor analysis (CFA) and Rasch analysis to test dimensionality, local dependence (LD) and differential item functioning (DIF) according to sociodemographic and treatment-related factors. Clinical data was obtained from the Danish Breast Cancer Group registry. RESULTS: Twenty-two items were generated for the Breast Cancer Recurrence instrument (BreastCaRe). Cognitive testing resulted in clearer items. Seven subscales based on general, bone, liver, lung, brain, locoregional and contralateral recurrence symptoms were proposed. Both CFA and Rasch models confirmed the factor structure. No DIF was identified. Five item pairs showed LD but all items were retained to avoid loss of clinical information. Rasch models taking LD into account were used to generate a standardized scoring table for each subscale. CONCLUSIONS: The BreastCaRe has good content and structural validity, patient acceptability and measurement invariance. We are preparing to examine the predictive validity of this new instrument.

Accuracy of performance-test linking based on a many-facet Rasch model.

Performance assessments, in which human raters assess examinee performance in practical tasks, have attracted much attention in various assessment contexts involving measurement of higher-order abilities. However, difficulty persists in that ability measurement accuracy strongly depends on rater and task characteristics such as rater severity and task difficulty. To resolve this problem, various item response theory (IRT) models incorporating rater and task parameters, including many-facet Rasch models (MFRMs), have been proposed. When applying such IRT models to datasets comprising results of multiple performance tests administered to different examinees, test linking is needed to unify the scale for model parameters estimated from individual test results. In test linking, test administrators generally need to design multiple tests such that raters and tasks partially overlap. The accuracy of linking under this design is highly reliant on the numbers of common raters and tasks. However, the numbers of common raters and tasks required to ensure high accuracy in test linking remain unclear, making it difficult to determine appropriate test designs. We therefore empirically evaluate the accuracy of IRT-based performance-test linking under common rater and task designs. Concretely, we conduct evaluations through simulation experiments that examine linking accuracy based on a MFRM while changing numbers of common raters and tasks with various factors that possibly affect linking accuracy.

Test equating sleep scales: applying the Leunbach's model.

BACKGROUND: In most cases, the total scores from different instruments assessing the same construct are not directly comparable, but must be equated. In this study we aimed to illustrate a novel test equating methodology applied to sleep functions, a domain in which few score comparability studies exist. METHODS: Eight scales from two cross-sectional self-report studies were considered, and one scale was common to both studies. The International Classification of Functioning, Disability and Health (ICF) was used to establish content comparability. Direct (common persons) and indirect (common item) equating was assessed by means of Leunbach's model, which equates the scores of two scales depending on the same person parameter, taking into account several tests of fit and the Standard Error of Equating (SEE). RESULTS: All items were linked to the body functions category b134 of the ICF, which corresponds to 'Sleep functions'. The scales were classified into three sleep aspects: four scales were assessing mainly sleep disturbance, one quality of sleep, and three impact of sleep on daily life. Of 16 direct equated pairs, 15 could be equated according to Leunbach's model, and of 12 indirect equated pairs, 8 could be equated. Raw score conversion tables between each of these 23 equated pairs are provided. The SEE was higher for indirect than for direct equating. Pairs measuring the same sleep aspect did not show better fit indices than pairs from different aspects. The instruments mapped to a higher order concept of sleep functions. CONCLUSION: Leunbach's equating model has been successfully applied to a functioning domain little explored in test equating. This novel methodology, together with the ICF, enables comparison of clinical outcomes and research results, and facilitates communication among clinicians.

Autoregressive transitional ordinal model to test for treatment effect in neurological trials with complex endpoints.

BACKGROUND: A number of potential therapeutic approaches for neurological disorders have failed to provide convincing evidence of efficacy, prompting pharmaceutical and health companies to discontinue their involvement in drug development. Limitations in the statistical analysis of complex endpoints have very likely had a negative impact on the translational process. METHODS: We propose a transitional ordinal model with an autoregressive component to overcome previous limitations in the analysis of Upper Extremity Motor Scores, a relevant endpoint in the field of Spinal Cord Injury. Statistical power and clinical interpretation of estimated treatment effects of the proposed model were compared to routinely employed approaches in a large simulation study of two-arm randomized clinical trials. A revisitation of a key historical trial provides further comparison between the different analysis approaches. RESULTS: The proposed model outperformed all other approaches in virtually all simulation settings, achieving on average 14 % higher statistical power than the respective second-best performing approach (range: -1 %, +34 %). Only the transitional model allows treatment effect estimates to be interpreted as conditional odds ratios, providing clear interpretation and visualization. CONCLUSION: The proposed model takes into account the complex ordinal nature of the endpoint under investigation and explicitly accounts for relevant prognostic factors such as lesion level and baseline information. Superior statistical power, combined with clear clinical interpretation of estimated treatment effects and widespread availability in commercial software, are strong arguments for clinicians and trial scientists to adopt, and further extend, the proposed approach.

Using item response theory to model multiple phenotypes and their joint heritability in family data.

Many important complex diseases are composed of a series of phenotypes, which makes the disease diagnosis and its genetic dissection difficult. The standard procedures to determine heritability in such complex diseases are either applied for single phenotype analyses or to compare findings across phenotypes or multidimensional reduction procedures, such as principal components analysis using all phenotypes. However each method has its own problems and the challenges are even more complex for extended family data and categorical phenotypes. In this paper, we propose a methodology to determine a scale for complex outcomes involving multiple categorical phenotypes in extended pedigrees using item response theory (IRT) models that take all categorical phenotypes into account, allowing informative comparison among individuals. An advantage of the IRT framework is that a straightforward joint heritability parameter can be estimated for categorical phenotypes. Furthermore, our methodology allows many possible extensions such as the inclusion of covariates and multiple variance components. We use Markov Chain Monte Carlo algorithm for the parameter estimation and validate our method through simulated data. As an application we consider the metabolic syndrome as the multiple phenotype disease using data from the Baependi Heart Study consisting of 1,696 individuals in 95 families. We adjust IRT models without covariates and include age and age squared as covariates. The results showed that adjusting for covariates yields a higher joint heritability (h2=0.53) than without co variates (h2=0.21) indicating that the covariates absorbed some of the error variance.

On the Identifiability of 3- and 4-Parameter Item Response Theory Models From the Perspective of Knowledge Space Theory.

The present work aims at showing that the identification problems (here meant as both issues of empirical indistinguishability and unidentifiability) of some item response theory models are related to the notion of identifiability in knowledge space theory. Specifically, that the identification problems of the 3- and 4-parameter models are related to the more general issues of forward- and backward-gradedness in all items of the power set, which is the knowledge structure associated with IRT models under the assumption of local independence. As a consequence, the identifiability problem of a 4-parameter model is split into two parts: a first one, which is the result of a trade-off between the left-side added parameters and the remainder of the Item Response Function, e.g., a 2-parameter model, and a second one, which is the already well-known identifiability issue of the 2-parameter model itself. Application of the results to the logistic case appears to provide both a confirmation and a generalization of the current findings in the literature for both fixed- and random-effects IRT logistic models.

Investigating the Functioning of Rating Scales With Rasch Models.

The hypothesis implicit in the rating scale design is that the categories reflect increasing levels of the latent variable. Rasch models for ordered polytomous items include parameters, called thresholds, that allow for empirically testing this hypothesis. Failure of the thresholds to advance monotonically with the categories (a condition that is referred to as "threshold disordering") provides evidence that the rating scale is not functioning as intended. This work focuses on scales consisting of rather large numbers of categories, whose use is often recommended in the literature. Threshold disordering is observed in both an extended 8-point scale specially developed for the Patient Health Questionnaire-9 and the original 10-point scale of the Behavioral Religiosity Scale. The results of this work prompt practitioners not to take the functioning of the rating scale for granted, but to verify it empirically.

Use of the Rasch Model for Fit Statistics and Rating Scale Diagnosis for the Student Perception Appraisal-Revised.

Background and Purpose: Nursing student retention is essential to meet workforce demands. Jeffrey's Nursing Student Retention Student Perception Appraisal-Revised (SPA-R1) has been used extensively to understand factors that impact retention. Psychometric testing of the SPA-R1 contributes to greater confidence in the instrument's reliability and validity. Methods: Item response theory and specifically, the single parameter polytomous Rasch model was used as a framework for fit statistic testing and rating scale diagnostics of the SPA-R1. This was a secondary analysis of a convenience sample of undergraduate prelicensure nursing students. The setting for the previous study was virtual, and the study period was 2022. Results: The model item characteristic curves for the 27 items of the SPA-R1 have similar shapes and are clustered in proximity. Overall, there are three clusters of items evident in the Rasch standardized residual contrast. The Rasch scale diagnostics indicated that the scale appropriately monotonically increases. However, there is a greater than 5 logit distance between does not apply and severely restricts, between severely restricts and moderately restricts, and between does not restrict or support and moderately supports. These large threshold distances indicate that additional steps in the scale may be warranted. The items cover the mid-range of the amount of retention perceptions; however, there are no items that represent the highest magnitude of the perceived amount of influence on retention. Conclusions: This study contributes further evidence to support the validity and reliability of the SPA-R1. We recommend adding steps to the scale, removing the does not apply response option, and considering scoring by three domains or clusters.

R Statistics: survey and review of packages for the estimation of Rasch models.

Abstract: R Statistics is a comprehensive and widely-used suite of packages for statistical operations. From 27 R packages indexed with the word "Rasch", 11 packages capable of Rasch estimation and analysis are identified and critiqued. A commercial Rasch application is included for comparison. Three R data frames are used. A larger and a smaller 0/1 data frame are analyzed with the Dichotomous Rasch Model. A polytomous 0/1/2 data frame is analyzed with the Partial Credit Model. The R packages can all use the same data frame. They are easy to use and mostly fast, though their documentation is generally skimpy. Every package has obvious shortcomings, but the unique features of each package could make them all useful. For general Rasch estimation and fit analysis of dichotomous data, three packages stand out: eRm, TAM and autoRasch. Two packages stand out for polytomous data: TAM and autoRasch.

Perceived Stress and Perceived Lack of Control of Spanish Education-Degree University Students: Differences Dependent on Degree Year, Basis for Admission and Gender.

Stress in higher education students is an issue of growing concern, as stress impacts quality of life and has been linked to drop-out from higher education. The aim was to investigate how stress was related to how far along students were in their degree program, using the Spanish Perceived Stress Scale (PSS10). We expected 1) first-year education students to experience more perceived lack of control than second-year students, 2) second-year students to experience more perceived stress than first-year students. Participants were students in three university education-degree programs (N = 399). The psychometric properties of the Perceived Stress (PS) and Perceived Lack of Control (PLC) subscales were investigated using Rasch models. One item was eliminated from each subscale. The estimated interval-level Rasch scores were utilized for testing hypotheses with general linear models. Results showed a somewhat more complex pattern than predicted as interaction effects between degree year and gender and between degree year and basis for admission were found.

A Lognormal Ipsative Model for Multidimensional Compositional Items.

Compositional items - a form of forced-choice items - require respondents to allocate a fixed total number of points to a set of statements. To describe the responses to these items, the Thurstonian item response theory (IRT) model was developed. Despite its prominence, the model requires that items composed of parts of statements result in a factor loading matrix with full rank. Without this requirement, the model cannot be identified, and the latent trait estimates would be seriously biased. Besides, the estimation of the Thurstonian IRT model often results in convergence problems. To address these issues, this study developed a new version of the Thurstonian IRT model for analyzing compositional items - the lognormal ipsative model (LIM) - that would be sufficient for tests using items with all statements positively phrased and with equal factor loadings. We developed an online value test following Schwartz's values theory using compositional items and collected response data from a sample size of N = 512 participants with ages from 13 to 51 years. The results showed that our LIM had an acceptable fit to the data, and that the reliabilities exceeded 0.85. A simulation study resulted in good parameter recovery, high convergence rate, and the sufficient precision of estimation in the various conditions of covariance matrices between traits, test lengths and sample sizes. Overall, our results indicate that the proposed model can overcome the problems of the Thurstonian IRT model when all statements are positively phrased and factor loadings are similar.

A Comparison of Classical and Modern Measures of Internal Consistency.

Three measures of internal consistency - Kuder-Richardson Formula 20 (KR20), Cronbach's alpha (α), and person separation reliability (R) - are considered. KR20 and α are common measures in classical test theory, whereas R is developed in modern test theory and, more precisely, in Rasch measurement. These three measures specify the observed variance as the sum of true variance and error variance. However, they differ for the way in which these quantities are obtained. KR20 uses the error variance of an "average" respondent from the sample, which overestimates the error variance of respondents with high or low scores. Conversely, R uses the actual average error variance of the sample. KR20 and α use respondents' test scores in calculating the observed variance. This is potentially misleading because test scores are not linear representations of the underlying variable, whereas calculation of variance requires linearity. Contrariwise, if the data fit the Rasch model, the measures estimated for each respondent are on a linear scale, thus being numerically suitable for calculating the observed variance. Given these differences, R is expected to be a better index of internal consistency than KR20 and α. The present work compares the three measures on simulated data sets with dichotomous and polytomous items. It is shown that all the estimates of internal consistency decrease with the increasing of the skewness of the score distribution, with R decreasing to a larger extent. Thus, R is more conservative than KR20 and α, and prevents test users from believing a test has better measurement characteristics than it actually has. In addition, it is shown that Rasch-based infit and outfit person statistics can be used for handling data sets with random responses. Two options are described. The first one implies computing a more conservative estimate of internal consistency. The second one implies detecting individuals with random responses. When there are a few individuals with a consistent number of random responses, infit and outfit allow for correctly detecting almost all of them. Once these individuals are removed, a "cleaned" data set is obtained that can be used for computing a less biased estimate of internal consistency.

On a Generalization of Local Independence in Item Response Theory Based on Knowledge Space Theory.

Knowledge space theory (KST) structures are introduced within item response theory (IRT) as a possible way to model local dependence between items. The aim of this paper is threefold: firstly, to generalize the usual characterization of local independence without introducing new parameters; secondly, to merge the information provided by the IRT and KST perspectives; and thirdly, to contribute to the literature that bridges continuous and discrete theories of assessment. In detail, connections are established between the KST simple learning model (SLM) and the IRT General Graded Response Model, and between the KST Basic Local Independence Model and IRT models in general. As a consequence, local independence is generalized to account for the existence of prerequisite relations between the items, IRT models become a subset of KST models, IRT likelihood functions can be generalized to broader families, and the issues of local dependence and dimensionality are partially disentangled. Models are discussed for both dichotomous and polytomous items and conclusions are drawn on their interpretation. Considerations on possible consequences in terms of model identifiability and estimation procedures are also provided.

New Perspectives in Computing the Point of Subjective Equality Using Rasch Models.

In psychophysics, the point of subject equality (PSE) is any of the points along a stimulus dimension at which a variable stimulus (visual, tactile, auditory, and so on) is judged by an observer to be equal to a standard stimulus. Rasch models have been found to offer a valid solution for computing the PSE when the method of constant stimuli is applied in the version of the method of transitions. The present work provides an overview of the procedures for computing the PSE using Rasch models and proposes some new developments. An adaptive procedure is described that allows for estimating the PSE of an observer without presenting him/her with all stimuli pairs. This procedure can be particularly useful in those situations in which psychophysical conditions of the individuals require that the number of trials is limited. Moreover, it allows for saving time that can be used to scrutinize the results of the experiment or to run other experiments. Also, the possibility of using Rasch-based fit statistics for identifying observers who gave unexpected judgments is explored. They could be individuals who, instead of carefully evaluating the presented stimuli pairs, gave random, inattentive, or careless responses, or gave the same response to many consecutive stimuli pairs. Otherwise, they could be atypical and clinically relevant individuals who deserve further investigation. The aforementioned developments are implemented using procedures and statistics that are well established in the framework of Rasch models. In particular, computerized adaptive testing procedures are used for efficiently estimating the PSE of the observers, whereas infit and outfit mean-squares statistics are used for detecting observers who gave unexpected judgments. Results of the analyses carried out on simulated data sets suggest that the proposed developments can be used in psychophysical experiments.

Using Overt and Covert Items in Self-Report Personality Tests: Susceptibility to Faking and Identifiability of Possible Fakers.

Self-report personality tests widely used in clinical, medical, forensic, and organizational areas of psychological assessment are susceptible to faking. Several approaches have been developed to prevent or detect faking, which are based on the use of faking warnings, ipsative items, social desirability scales, and validity scales. The approach proposed in this work deals with the use of overt items (the construct is clear to test-takers) and covert items (the construct is obscure to test-takers). Covert items are expected to be more resistant to faking than overt items. Two hundred sixty-seven individuals were presented with an alexithymia scale. Two experimental conditions were considered. Respondents in the faking condition were asked to reproduce the profile of an alexithymic individual, whereas those in the sincere condition were not asked to exhibit a particular alexithymia profile. The items of the scale were categorized as overt or covert by expert psychotherapists and analyzed through Rasch models. Respondents in the faking condition were able to exhibit measures of alexithymia in the required direction. This occurred for both overt and covert items, but to a greater extent for overt items. Differently from overt items, covert items defined a latent variable whose meaning was shared between respondents in the sincere and faking condition, and resistant to deliberate distortion. Rasch fit statistics indicated unexpected responses more often for respondents in the faking condition than for those in the sincere condition and, in particular, for the responses to overt items by individuals in the faking condition. More than half of the respondents in the faking condition showed a drift rate (difference between the alexithymia levels estimated on the responses to overt and covert items) significantly larger than that observed in the respondents in the sincere condition.

Perceived danger during deployment: a Rasch validation of an instrument assessing perceived combat exposure and the witnessing of combat consequences in a war zone.

The potential stressors associated with military deployment are related to an increased risk of adverse mental health outcomes. Perceived exposure to combat has been found to be proportional to the severity of post-deployment posttraumatic stress disorder (PTSD). However, other perceived adversities during deployment, such as witnessing danger, distress, and hardship in the war zone, have been less systematically studied, but might play an equally substantial role for post-deployment mental health. The development and validation of scales that assess these related constructs are needed to distinguish their contribution to post-deployment risk of PTSD. We evaluated the validity of 10 items measuring perceived danger distributed to all deployed personnel with the Danish Defense since 1998. We hypothesize two scales: Exposure to Danger and Combat (EDC) and Witnessing Consequences of War (WCW). Two military cohorts deployed to Afghanistan in 2009 (Cohort 1, N = 276) and 2013 (Cohort 2, N = 273) were included. Questionnaire data was collected six months after homecoming, including deployment experiences and post-deployment reactions. We tested the construct validity of the 10 items of perceived danger with Rasch models (RM), focusing specifically on presence of subscales, and differential item functioning (DIF) across cohorts. We confirmed the existence of two separate subscales, EDCS and WCWS, both with adequate reliability. None of the subscales fitted a pure RM, but adequate fit was found for graphical log-linear RMs with evidence of DIF for the ECDS. However, adjusting the score to account for DIF had practically no effect, suggesting that the total non-adjusted mean score can be used in future cohort comparisons. Perceived exposure to combat and danger and witnessing consequences of war are related, but essentially distinct, concepts, each providing unique information about deployment adversities. Future studies should evaluate their shared and unique contribution to the risk of post-deployment PTSD.

La percepción de exposición al combate ha resultado ser proporcional a la severidad del trastorno por estrés post-traumático (TEPT) posterior al despliegue militar. Sin embargo, otras adversidades percibidas durante el despliegue, tales como experimentar peligro, angustia, y dificultades en la zona de guerra, han sido menos sistemáticamente estudiadas, pero podrían jugar un rol sustancialmente equivalente para el impacto en la salud mental posterior al despliegue militar. Se requiere desarrollar y validar escalas que evalúen estos constructos relacionados para distinguir su contribución al riesgo de desarrollar TEPT posterior al despliegue. El objetivo es evaluar la validez de 10 ítems que miden el peligro percibido distribuido a todo el personal desplegado con la Defensa Danesa desde 1998. Se hipotetizan dos escalas; Exposición a Peligro y Combate (EDC) y Exposición a Consecuencias de Guerra (WCW). Se incluyó a dos cohortes militares, que se desplegaron en Afganistán el año 2009 (Cohorte 1, N=276), y el 2013 (Cohorte 2, N=273). Se recopiló datos a partir de cuestionarios seis meses después de regreso al país de origen, incluyendo experiencias dentro del despliegue y reacciones posteriores a éste. Se probó en las cohortes la validez de constructo de los 10 ítems de peligro percibido con modelos Rasch (RM), poniendo especial énfasis en la presencia de subescalas, e Ítem de funcionamiento diferencial (DIF). Se confirmó la existencia de dos subescalas separadas, EDC y WCW, ambas con fiabilidad adecuada. Ninguna de las subescales correspondía a un modelo Rasch puro, pero sí se encontró una relación adecuada para un modelo Rasch logarítmico-lineal con evidencia de DIF para EDC. No obstante, el ajustar el puntaje para explicar el IFD prácticamente no tuvo efectos, sugiriendo que la puntuación media total no ajustada puede ser utilizada en futuras comparaciones de cohortes. La exposición percibida al combate y el peligro y exposición a consecuencias de guerra son dos conceptos relacionados, pero esencialmente distintos, cada uno proveyendo información única sobre los efectos adversos del despliegue militar. Futuros estudios deberían evaluar su contribución compartida y única al riesgo de desarrollar TEPT posterior al despliegue militar.

Monitoring Countries in a Changing World: A New Look at DIF in International Surveys.

This paper discusses the issue of differential item functioning (DIF) in international surveys. DIF is likely to occur in international surveys. What is needed is a statistical approach that takes DIF into account, while at the same time allowing for meaningful comparisons between countries. Some existing approaches are discussed and an alternative is provided. The core of this alternative approach is to define the construct as a large set of items, and to report in terms of summary statistics. Since the data are incomplete, measurement models are used to complete the incomplete data. For that purpose, different models can be used across countries. The method is illustrated with PISA's reading literacy data. The results indicate that this approach fits the data better than the current PISA methodology; however, the league tables are nearly identical. The implications for monitoring changes over time are discussed.