David Aguado

Combining scales to assess suicide risk

OBJECTIVES A major interest in the assessment of suicide risk is to develop an accurate instrument, which could be easily adopted by clinicians. This article aims at identifying the most discriminative items from a collection of scales usually employed in the assessment of suicidal behavior. METHODS The answers to the Barrat Impulsiveness Scale, International Personality Disorder Evaluation Screening Questionnaire, Brown-Goodwin Lifetime History of Aggression, and Holmes & Rahe Social Readjustment Rating Scale provided by a group of 687 subjects (249 suicide attempters, 81 non-suicidal psychiatric inpatients, and 357 healthy controls) were used by the Lars-en algorithm to select the most discriminative items. RESULTS We achieved an average accuracy of 86.4%, a specificity of 89.6%, and a sensitivity of 80.8% in classifying suicide attempters using 27 out of the 154 items from the original scales. CONCLUSIONS The 27 items reported here should be considered a preliminary step in the development of a new scale evaluating suicidal risk in settings where time is scarce.

Psychometric Properties of an Emotional Adjustment Measure An Application of the Graded Response Model

Item response theory (IRT) provides valuable methods for the analysis of the psychometric properties of a psychological measure. However, IRT has been mainly used for assessing achievements and ability rather than personality factors. This paper presents an application of the IRT to a personality measure. Thus, the psychometric properties of a new emotional adjustment measure that consists of a 28-six graded response items is shown. Classical test theory (CTT) analyses as well as IRT analyses are carried out. Samejimas (1969) graded-response model has been used for estimating item parameters. Results show that the bank of items fulfills model assumptions and fits the data reasonably well, demonstrating the suitability of the IRT models for the description and use of data originating from personality measures. In this sense, the model fulfills the expectations that IRT has undoubted advantages: (1) The invariance of the estimated parameters, (2) the treatment given to the standard error of measurement, an...

Validity and reliability of situational judgement test scores : a new approach based on cognitive diagnosis models

Conventional methods for assessing the validity and reliability of situational judgment test (SJT) scores have proven to be inadequate. For example, factor analysis techniques typically lead to nonsensical solutions, and assumptions underlying Cronbach’s alpha coefficient are violated due to the multidimensional nature of SJTs. In the current article, we describe how cognitive diagnosis models (CDMs) provide a new approach that not only overcomes these limitations but that also offers extra advantages for scoring and better understanding SJTs. The analysis of the Q-matrix specification, model fit, and model parameter estimates provide a greater wealth of information than traditional procedures do. Our proposal is illustrated using data taken from a 23-item SJT that presents situations about student-related issues. Results show that CDMs are useful tools for scoring tests, like SJTs, in which multiple knowledge, skills, abilities, and other characteristics are required to correctly answer the items. SJT classifications were reliable and significantly related to theoretically relevant variables. We conclude that CDM might help toward the exploration of the nature of the constructs underlying SJT, one of the principal challenges in SJT research.

Using the Implicit Association Test to Assess Risk Propensity Self-concept: Analysis of its Predictive Validity on a Risk-taking Behaviour in a Natural Setting

The present work analyses the predictive validity of measures provided by several available self–report and indirect measurement instruments to assess risk propensity (RP) and proposes a measurement instrument using the Implicit Association Test: the IAT of Risk Propensity Self–Concept (IAT–RPSC), an adaptation of the prior IAT–RP of Dislich et al. Study 1 analysed the relationship between IAT–RPSC scores and several RP self–report measures. Participants’ risk–taking behaviour in a natural setting was also assessed, analyzing the predictive validity of the IAT–RPSC scores on risk–taking behaviour compared with the self–report measures. Study 2 analysed the predictive validity of the IAT–RPSC scores in comparison with other indirect measures. Results of these studies showed that the IAT–RPSC scores exhibited good reliability and were positively correlated to several self–report and indirect measures, providing evidence for convergent validity. Most importantly, the IAT–RPSC scores predicted risk–taking behaviour in a natural setting with real consequences above and beyond all other self–report and indirect measures analysed. Copyright

Individual identification using personality traits

In this article, a pioneer study is conducted to evaluate the possibility of identifying people through their personality traits. The study is conducted using the answers of a population of 734 individuals to a collection of 206 items. These items aim at measuring five common different personality traits usually called the big five. These five levels are neuroticism, extraversion, agreeableness, conscientiousness and openness. The traits are estimated using the widely used Samejimas model and then used to discriminate the individuals. Results point biometrics using personality traits as a new promising biometric modality.

Psychometrical assessment and item analysis of the general health questionnaire in victims of terrorism

There is a need to assess the psychiatric morbidity that appears as a consequence of terrorist attacks. The General Health Questionnaire (GHQ) has been used to this end, but its psychometric properties have never been evaluated in a population affected by terrorism. A sample of 891 participants included 162 direct victims of terrorist attacks and 729 relatives of the victims. All participants were evaluated using the 28-item version of the GHQ (GHQ-28). We examined the reliability and external validity of scores on the scale using Cronbachs alpha and Pearson correlation with the State-Trait Anxiety Inventory (STAI), respectively. The factor structure of the scale was analyzed with varimax rotation. Samejimas (1969) graded response model was used to explore the item properties. The GHQ-28 scores showed good reliability and item-scale correlations. The factor analysis identified 3 factors: anxious-somatic symptoms, social dysfunction, and depression symptoms. All factors showed good correlation with the STAI. Before rotation, the first, second, and third factor explained 44.0%, 6.4%, and 5.0% of the variance, respectively. Varimax rotation redistributed the percentages of variance accounted for to 28.4%, 13.8%, and 13.2%, respectively. Items with the highest loadings in the first factor measured anxiety symptoms, whereas items with the highest loadings in the third factor measured suicide ideation. Samejimas model found that high scores in suicide-related items were associated with severe depression. The factor structure of the GHQ-28 found in this study underscores the preeminence of anxiety symptoms among victims of terrorism and their relatives. Item response analysis identified the most difficult and significant items for each factor.

Improving sale performance prediction using support vector machines

Research highlights? A system capable of predicting salesmen performance with acceptable accuracy is developed. ? Only three features which measures agreeableness, openness to experience and sociability are required to obtain accurate results. ? A salesmen performance prediction system reduces considerable the company costs. In this article, an expert system based on support vector machines is developed to predict the sale performance of some insurance company candidates. The system predicts the performance of these candidates based on some scores, which are measurements of cognitive characteristics, personality, selling skills and biodata. An experiment is conducted to compare the accuracy of the proposed system with respect to previously reported systems which use discriminant functions or decision trees. Results show that the proposed system is able to improve the accuracy of a baseline linear discriminant based system by more than 10% and that also exceeds the state of the art systems by almost 5%. The proposed approach can help to reduce considerably the direct and indirect expenses of the companies.

A new IRT-based standard setting method: application to ecat-listening

BACKGROUND Criterion-referenced interpretations of tests are highly necessary, which usually involves the difficult task of establishing cut scores. Contrasting with other Item Response Theory (IRT)-based standard setting methods, a non-judgmental approach is proposed in this study, in which Item Characteristic Curve (ICC) transformations lead to the final cut scores. METHOD eCat-Listening, a computerized adaptive test for the evaluation of English Listening, was administered to 1,576 participants, and the proposed standard setting method was applied to classify them into the performance standards of the Common European Framework of Reference for Languages (CEFR). RESULTS The results showed a classification closely related to relevant external measures of the English language domain, according to the CEFR. CONCLUSIONS It is concluded that the proposed method is a practical and valid standard setting alternative for IRT-based tests interpretations.

La influencia del tutor en el seguimiento de programas eLearning: Estudio de acciones en un caso práctico

Resumen es: Las propuestas de Educacion a Distancia basadas en tecnologia Web ocupan en la actualidad un lugar indiscutible. El uso de los nuevos recursos en los pro...

Small sample discrimination and professional performance assessment

Class overlapping and small sample class sizes, a situation not infrequent in practical settings, can make very difficult the successful construction of a classification system. In this paper we will address this question by means of a new procedure, which we call Nonlinear Discriminant Analysis (NLDA), for classifier construction in such cases and that combines the excellent approximation properties of the well known Multilayer Perceptrons with the target-free classical discrimination technique of Fishers Analysis. Besides a short description of NLDA fundamentals, we will give an illustration of its use in a practical problem, the assessment of professional performance of insurance salespersons.