An introduction to network psychometrics:Relating ising network models to item response theory models

In recent years, network models have been proposed as an alternative representation of psychometric constructs such as depression. In such models, the covariance between observables (e.g., symptoms like depressed mood, feelings of worthlessness, and guilt) is explained in terms of a pattern of causal interactions between these observables, which contrasts with classical interpretations in which the observables are conceptualized as the effects of a reflective latent variable. However, few investigations have been directed at the question how these different models relate to each other. To shed light on this issue, the current paper explores the relation between one of the most important network models—the Ising model from physics—and one of the most important latent variable models—the Item Response Theory (IRT) model from psychometrics. The Ising model describes the interaction between states of particles that are connected in a network, whereas the IRT model describes the probability distribution associated with item responses in a psychometric test as a function of a latent variable. Despite the divergent backgrounds of the models, we show a broad equivalence between them and also illustrate several opportunities that arise from this connection

The ambiguous utility of psychometrics for the interpretative foundation of socially relevant avatars

Accepted for publication in Theory & PsychologyInternational audienceThe persisting debates that measurement in psychology elicits can be explained by the conflict between two aspiration types. One, the epistemologic aspiration, resting on the search for scientific truth, and two, the social aspiration, resting on the demonstration of a capacity to contribute to psychological assessment problems in particular. Psychometrics answer essentially to psychology's demand for social utility, leading to the quasi-exclusive attribution of importance to quantitative interpretation. For psychology to be considered an empirical science, it has to establish its capacity for the measurement of psychological phenomena, even if this means that it recognizes that these phenomena are essentially qualitative

Network analysis of multivariate data in psychological science

Stress and Psychopatholog

Inequality in learning outcomes: Unveiling educational deprivation through complex network analysis

Understanding which factors are determinant to guarantee the human right to education entails the study of a large number of non-linear relationships among multiple agents and their impact on the properties of the entire system. Complex network analysis of large-scale assessment results provides a set of unique advantages over classical tools for facing the challenge of measuring inequality gaps in learning outcomes and recognizing those factors associated with educational deprivation, combining the richness of qualitative analysis with quantitative inferences. This study establishes two milestones in educational research using a census high-quality data from a Latin American country. The first one is to provide a direct method to recognize the structure of inequality and the relationship between social determinants as ethnicity, socioeconomic status of students, rurality of the area and type of school funding and educational deprivation. The second one focus in unveil and hierarchize educational and non-educational factors associated with the conditional distribution of learning outcomes. This contribution provides new tools to current theoretical framework for discovering non-trivial relationships in educational phenomena, helping policymakers to address the challenge of ensuring inclusive and equitable education for those historically marginalized population groups.info:eu-repo/semantics/acceptedVersio

Effectiveness of single dose rifampicin in preventing leprosy in close contacts of patients with newly diagnosed leprosy

Objective To determine the effectiveness of chemoprophylaxis using a single dose of rifampicin to prevent leprosy in close contacts. Design Single centre, double blind, cluster randomised, placebo controlled trial. SettingLeprosy control programme in two districts of northwest Bangladesh with a population of more than four million. Participants28 092 close contacts of 1037 patients with newly diagnosed leprosy. 21 711 contacts fulfilled the study requirements. Interventions A single dose of rifampicin or placebo given to close contacts in the second month of starting the index patient’s treatment, with follow-up for four years. Main outcome measure Development of clinical leprosy. Results 18 869 of the 21 711 contacts (86.9%) were followed-up at four years. Ninety one of 9452 contacts in the placebo group and 59 of 9417 in the rifampicin group had developed leprosy. The overall reduction in incidence of leprosy using a single dose of rifampicin in the first two years was 57% (95% confidence interval 33% to 72%). The groups did not differ between two and four years. The overall number needed to treat (NNT) to prevent a single case of leprosy among contacts was 297 (95% confidence interval 176 to 537). Differences were found between subgroups at two years, both in reduction of incidence and in NNT. ConclusionA single dose of rifampicin given to contacts of patients with newly diagnosed leprosy is effective at preventing the development of clinical leprosy at two years. The effect was maintained, but no difference was seen between the placebo and rifampicin groups beyond two years. Trial registration Current Controlled Trials ISRCTN61223447

A complex network approach to structural inequality of educational deprivation in a Latin American country

To guarantee the human right to education established by the fourth UNESCO’s Sustainable Development Goal, a deep understanding of a big set of non-linear relationships at different scales is need it, as well as to know how they impact on learning outcomes. In doing so, current methods do not provide enough evidence about interactions and, for this reason, some researchers have proposed to model education as a complex system for considering all interactions at individual level, as well as using computer simulation and network analysis to provide a comprehensive look at the educational processes, as well as to predict the outcomes of different public policies. The highlight of this paper is modeling the structure of the inequality of a national educational system as a complex network from learning outcomes and socio-economic, ethnicity, rurality and type of school funding, for providing a better understanding and measuring of the educational gaps. This new approach might help to integrate insights improving the theoretical framework, as well as to provide valuable information about non-trivial relationships between educational and non-educational variables in order to help policymakers to implement effective solutions for the educational challenge of ensuring inclusive and equitable education.info:eu-repo/semantics/acceptedVersio

Quantifying resilience of humans and other animals

All life requires the capacity to recover from challenges that are as inevitable as they are unpredictable. Understanding this resilience is essential for managing the health of humans and their livestock. It has long been difficult to quantify resilience directly, forcing practitioners to rely on indirect static indicators of health. However, measurements from wearable electronics and other sources now allow us to analyze the dynamics of physiology and behavior with unsurpassed resolution. The resulting flood of data coincides with the emergence of novel analytical tools for estimating resilience from the pattern of micro-recoveries observed in natural time series. Such dynamic indicators of resilience (DIORs) may be used to monitor the risk of systemic failure across systems ranging from organs to entire organisms. These tools invite a fundamental rethink of our approach to the adaptive management of health and resilience

Short-term health-related quality of life consequences in a lung cancer CT screening trial (NELSON)

Item does not contain fulltextBACKGROUND: In lung cancer CT screening, participants often have an indeterminate screening result at baseline requiring a follow-up CT. In subjects with either an indeterminate or a negative result after screening, we investigated whether health-related quality of life (HRQoL) changed over time and differed between groups in the short term. METHODS: A total of 733 participants in the NELSON trial received four questionnaires: T0, before randomisation; T1, 1 week before the baseline screening; T2, 1 day after the screening; and T3, 2 months after the screening results but before the 3-month follow-up CT. HRQoL was measured as generic HRQoL (the 12-item Short Form, SF-12; the EuroQol questionnaire, EQ-5D), anxiety (the Spielberger State-Trait Anxiety Inventory, STAI-6), and lung-cancer-specific distress (the Impact of Event Scale, IES). For analyses, repeated-measures analysis of variance was used, adjusted for covariates. RESULTS: Response to each questionnaire was 88% or higher. Scores on SF-12, EQ-5D, and STAI-6 showed no clinically relevant changes over time. At T3, IES scores that were clinically relevant increased after an indeterminate result, whereas these scores showed a significant decrease after a negative result. At T3, differences in IES scores between the two baseline result groups were both significant and clinically relevant (P<0.01). CONCLUSION: This longitudinal study among participants of a lung cancer screening programme showed that in the short term recipients of an indeterminate result experienced increased lung-cancer-specific distress, whereas the HRQoL changes after a negative baseline screening result may be interpreted as a relief