Psychometrics in Practice at RCEC

A broad range of topics is dealt with in this volume: from combining the psychometric generalizability and item response theories to the ideas for an integrated formative use of data-driven decision making, assessment for learning and diagnostic testing. A number of chapters pay attention to computerized (adaptive) and classification testing. Other chapters treat the quality of testing in a general sense, but for topics like maintaining standards or the testing of writing ability, the quality of testing is dealt with more specifically.\ud All authors are connected to RCEC as researchers. They present one of their current research topics and provide some insight into the focus of RCEC. The selection of the topics and the editing intends that the book should be of special interest to educational researchers, psychometricians and practitioners in educational assessment

Business intelligence in risk management: Some recent progresses

Risk management has become a vital topic both in academia and practice during the past several decades. Most business intelligence tools have been used to enhance risk management, and the risk management tools have benefited from business intelligence approaches. This introductory article provides a review of the state-of-the-art research in business intelligence in risk management, and of the work that has been accepted for publication in this issue of Information Sciences

Is there a regulatory trade-off between stability and performance? Evidence from italian banks.

Disentangling the direct causal effect that sanctions exert on bank performance from the indirect through default risk, we show that a trade-off exists for regulators between banks’ performance and stability in Italy. Two key findings provide evidence for the nontriviality of the return-risk nexus: (i) banks’ liquidations are concentrated at the lower-end of the profitability distribution, resulting in (attrition) biased estimates; (ii) the drop-out is informative since it depends on the unobserved measurements of profitability. Despite this evidence, while returns are affected by sanctions and regulatory requirements, default risk is not. However, looking at growth of gross loans, enforcement actions reduce default risk though at a cost of a significant fall in lending, creating a regulatory tradeoff. In fact, through loans’ growth, we account for the key dynamics of intermediaries’ soundness, namely higher profits and less non-performing loans

The Role of Mindfulness in the Regulation of Behavior Among Those Prone to Negative Urgency

Negative emotions can be challenging to regulate, and for some individuals can lead to failures of behavior regulation. The present study is an initial effort to explore the role that mindfulness may play in fostering effective behavior regulation among those prone to high negative urgency (NU). Eighty undergraduate students were recruited based on their high or low scores of NU. First, participants completed a self-report measure of mindfulness (Mindful Attention Awareness Scale; MAAS), an Emotional Go/No Go task in an fMRI scanner, and then reported alcohol consumption. Results showed that those with high in NU had low levels of mindfulness compared to those low in NU. Mindfulness predicted substance use at the one- month follow-up after controlling for the predictive roles of NU and gender. Further exploration of the underlying neural mechanisms of mindfulness is needed to better understand its impact on emotion- and self-regulatory processes, especially during difficult emotional experience

Models of verbal working memory capacity: What does it take to make them work?

Theories of working memory (WM) capacity limits will be more useful when we know what aspects of performance are governed by the limits and what aspects are governed by other memory mechanisms. Whereas considerable progress has been made on models of WM capacity limits for visual arrays of separate objects, less progress has been made in understanding verbal materials, especially when words are mentally combined to form multiword units or chunks. Toward a more comprehensive theory of capacity limits, we examined models of forced-choice recognition of words within printed lists, using materials designed to produce multiword chunks in memory (e.g., leather brief case). Several simple models were tested against data from a variety of list lengths and potential chunk sizes, with test conditions that only imperfectly elicited the interword associations. According to the most successful model, participants retained about 3 chunks on average in a capacity-limited region of WM, with some chunks being only subsets of the presented associative information (e.g., leather brief case retained with leather as one chunk and brief case as another). The addition to the model of an activated long-term memory component unlimited in capacity was needed. A fixed-capacity limit appears critical to account for immediate verbal recognition and other forms of WM. We advance a model-based approach that allows capacity to be assessed despite other important processing contributions. Starting with a psychological-process model of WM capacity developed to understand visual arrays, we arrive at a more unified and complete model

Dissociation of behavioral and neural responses to provocation during reactive aggression in healthy adults with high versus low externalization

The externalizing spectrum describes a range of heterogeneous personality traits and behavioral patterns, primarily characterized by antisocial behavior, disinhibition, and substance (mis)use. In psychopathology, abnormalities in neural threat, reward responses and the impulse-control system may be responsible for these externalizing symptoms. Within the non-clinical range, mechanisms remain still unclear. In this fMRI-study, 61 healthy participants (31 men) from the higher versus lower range of the non-clinical variation in externalization (31 participants with high externalization) as assessed by the subscales disinhibition and meanness of the Triarchic-Psychopathy-Measure (TriPM) performed a monetary modified Taylor-Aggression-Paradigm (mTAP). This paradigm consisted of a mock competitive-reaction-time-task played against a fictional opponent with preprogrammed win- and lose-trials. In lose-trials, participants were provoked by subtraction of an amount of money between 0 and 90 cents. As a manipulation check, provocation induced a significant rise in behavioral aggression levels linked with an increased activation in the anterior cingulate cortex (ACC). High externalization predicted reduced ACC responses to provocation. However, high externalizing participants did not behave more aggressively than the low externalization group. Additionally, the high externalizing group showed a significantly lower positive affect while no group differences emerged for negative affect. In conclusion, high externalization in the non-clinical range was related to neural alterations in regions involved in affective decision-making as well as to changes in affect but did not lead to higher behavioral aggression levels in response to the mTAP. This is in line with previous findings suggesting that aberrations at multiple levels are essential for developing externalizing disorders

A novel knowledge discovery based approach for supplier risk scoring with application in the HVAC industry

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThis research has led to a novel methodology for assessment and quantification of supply risks in the supply chain. The research has built on advanced Knowledge Discovery techniques and has resulted to a software implementation to be able to do so. The methodology developed and presented here resembles the well-known consumer credit scoring methods as it leads to a similar metric, or score, for assessing a supplier’s reliability and risk of conducting business with that supplier. However, the focus is on a wide range of operational metrics rather than just financial, which credit scoring techniques typically focus on. The core of the methodology comprises the application of Knowledge Discovery techniques to extract the likelihood of possible risks from within a range of available datasets. In combination with cross-impact analysis, those datasets are examined for establish the inter-relationships and mutual connections among several factors that are likely contribute to risks associated with particular suppliers. This approach is called conjugation analysis. The resulting parameters become the inputs into a logistic regression which leads to a risk scoring model the outcome of the process is the standardized risk score which is analogous to the well-known consumer risk scoring model, better known as FICO score. The proposed methodology has been applied to an Air Conditioning manufacturing company. Two models have been developed. The first identifies the supply risks based on the data about purchase orders and selected risk factors. With this model the likelihoods of delivery failures, quality failures and cost failures are obtained. The second model built on the first one but also used the actual data about the performance of supplier to identify risks of conducting business with particular suppliers. Its target was to provide quantitative measures of an individual supplier’s risk level. The supplier risk scoring model is tested on the data acquired from the company for its performance analysis. The supplier risk scoring model achieved 86.2% accuracy, while the area under curve (AUC) was 0.863. The AUC curve is much higher than required model’s validity threshold value of 0.5. It represents developed model’s validity and reliability for future data. The numerical studies conducted with real-life datasets have demonstrated the effectiveness of the proposed methodology and system as well as its future potential for industrial adoption

Predictive Power of Criminal Background on Losses

Product and data science teams for the auto insurance industry have been trying to increase pricing segmentation with validated rating variables to decrease rate subsidization. The criminal background data availability provided a new behavior variable to test against insurance-based credit scores as a potential predictive variable in the generalized linear rating model. Criminal background was analyzed using a Poisson Log Linear model and other key insurance rating variables for predicting loss costs. The study supported the inclusion of the criminal background data in combination with insurance-based credit score as the variable’s addition could improve the overall fit of the predictive model. The study also acknowledged there was a statistically significant association between criminal background and insurance-based credit score, but the overall size of the effect was small and weak. The overall contribution of value criminal background variable needs to be considered with a full rating dataset to determine if other, less powerful variables could be removed from the generalized linear to reduce the overall model complexity

A data mining-based framework for supply chain risk management

Increased risk exposure levels, technological developments and the growing information overload in supply chain networks drive organizations to embrace data-driven approaches in Supply Chain Risk Management (SCRM). Data Mining (DM) employs multiple analytical techniques for intelligent and timely decision making; however, its potential is not entirely explored for SCRM. The paper aims to develop a DM-based framework for the identification, assessment and mitigation of different type of risks in supply chains. A holistic approach integrates DM and risk management activities in a unique framework for effective risk management. The framework is validated with a case study based on a series of semi-structured interviews, discussions and a focus group study. The study showcases how DM supports in discovering hidden and useful information from unstructured risk data for making intelligent risk management decisions