A general multivariate latent growth model with applications in student careers Data warehouses

The evaluation of the formative process in the University system has been assuming an ever increasing importance in the European countries. Within this context the analysis of student performance and capabilities plays a fundamental role. In this work we propose a multivariate latent growth model for studying the performances of a cohort of students of the University of Bologna. The model proposed is innovative since it is composed by: (1) multivariate growth models that allow to capture the different dynamics of student performance indicators over time and (2) a factor model that allows to measure the general latent student capability. The flexibility of the model proposed allows its applications in several fields such as socio-economic settings in which personal behaviours are studied by using panel data.Comment: 20 page

Approximate likelihood inference in generalized linear latent variable models based on the dimension-wise quadrature

We propose a new method to perform approximate likelihood inference in latent variable models. Our approach provides an approximation of the integrals involved in the likelihood function through a reduction of their dimension that makes the computation feasible in situations in which classical and adaptive quadrature based methods are not applicable. We derive new theoretical results on the accuracy of the obtained estimators. We show that the proposed approximation outperforms several existing methods in simulations, and it can be successfully applied in presence of multidimensional longitudinal data when standard techniques are not applicable or feasible

Refinement of the NISECI ecological index reference conditions for Italian freshwater fish communities in the eastern Emilia-Romagna region

Following the Water Framework Directive 2000/60/CE (WFD), each member state of the European Union must monitor compliance of its rivers with ecological quality standards through biological quality indicators. The New Italian Index of the Ecological State of Fish Communities (NISECI) was developed in 2017 for the assessment of fish communities, as directed by the WFD in Italian freshwater habitats. According to the WFD, the general reference conditions (GRCs) of NISECI must be refined on a regional scale through new calculation of its metrics and sub-metrics. In the present study we used environmental and ichthyological data from 457 fish samplings distributed among 299 sampling sites within 84 different water bodies collected from 1995 to 2012 to develop: 1) new lists of expected species for six homogeneous zones identified in the Reno basin (Italy) and in the eastern regional basins of the Emilia-Romagna region; and 2) the threshold values for their species-specific abundance. Results were set as refined reference conditions (RRCs) for two of the metrics used in the application of the NISECI index in the study area (i.e. X1, relating to indigenous species and X2,b, for the abundance of expected species). The RRCs were tested by applying NISECI to 24 monitoring sites of the regional surface water monitoring network (i.e., ARPAE) and comparing the results with the application of NISECI using the GRCs. Furthermore, the analytical power of the refined NISECI was evaluated by relating the findings to three expertbased blind assessments of fish community ecological status. The results confirmed an increase in refined NISECI values and its higher consistency with expert-based assessment, supporting the validity of the presented method for RRC development and its potential for application in other regions

Granulomatous Reactivation during the Course of a Leprosy Infection: Reaction or Relapse

Leprosy is a serious infectious disease whose treatment still poses some challenges. Patients are usually treated with a combination of antimicrobial drugs called multidrug therapy. Although this treatment is effective against Mycobacterium leprae, the bacillus that causes leprosy, patients may develop severe inflammatory reactions during treatment. These reactions may be either attributed to an improvement in the immunological reactivity of the patient along with the treatment, or to relapse of the disease due to the proliferation of remaining bacilli. In certain patients these two conditions may be difficult to differentiate. The present study addresses the histopathology picture of and the M. leprae bacilli in sequential biopsies taken from lesions of patients who presented such reactions aiming to improve the differentiation of the two conditions. This is important because these reactions are one of the major causes of the disabilities of the patients with leprosy, and should be treated early and appropriately. Our results show that the histopathology picture alone is not sufficient, and that bacilli's counting is necessary

Asymptotic properties of adaptive Gauss-Hermite based estimators in latent variable models.

Latent variable models have been widely applied in different fields of research in which the constructs of interest are not directly observable, so that one or more latent variables are required to reduce the complexity of the data. In these cases, problems related to the integration of the likelihood function of the model arise since analytical solutions do not exist. A recent applied numerical technique is the Adaptive Gauss-Hermite (AGH) that provides a good approximation of the function to be inte- grated, and it is also computational feasible in presence of many latent variables and/or random effects. In this paper, we analyze the asymptotic behavior of the AGH-based estimators used to perform inference in generalized linear latent variable models

Psychometrika

Psychometrika, the official journal of the Psychometric Society, is devoted to the development of psychology as a quantitative rational science, including the advancement of theory and methodology for behavioral data analysis in psychology, education, and the social and behavioral sciences generally. Psychometrika contains articles on the development of quantitative models of psychological phenomena, as well as statistical methods and mathematical techniques for evaluating psychological and educational data