A Zero-Inflated Box-Cox Normal Unipolar Item Response Model for Measuring Constructs of Psychopathology

This research introduces a latent class item response theory (IRT) approach for modeling item response data from zero-inflated, positively skewed, and arguably unipolar constructs of psychopathology. As motivating data, the authors use 4,925 responses to the Patient Health Questionnaire (PHQ-9), a nine Likert-type item depression screener that inquires about a variety of depressive symptoms. First, Lucke’s log-logistic unipolar item response model is extended to accommodate polytomous responses. Then, a nontrivial proportion of individuals who do not endorse any of the symptoms are accounted for by including a nonpathological class that represents those who may be absent on or at some floor level of the latent variable that is being measured by the PHQ-9. To enhance flexibility, a Box-Cox normal distribution is used to empirically determine a transformation parameter that can help characterize the degree of skewness in the latent variable density. A model comparison approach is used to test the necessity of the features of the proposed model. Results suggest that (a) the Box-Cox normal transformation provides empirical support for using a log-normal population density, and (b) model fit substantially improves when a nonpathological latent class is included. The parameter estimates from the latent class IRT model are used to interpret the psychometric properties of the PHQ-9, and a method of computing IRT scale scores that reflect unipolar constructs is described, focusing on how these scores may be used in clinical contexts

Assessment of school performance through a multilevel latent Markov Rasch model

An extension of the latent Markov Rasch model is described for the analysis of binary longitudinal data with covariates when subjects are collected in clusters, e.g. students clustered in classes. For each subject, the latent process is used to represent the characteristic of interest (e.g. ability) conditional on the effect of the cluster to which he/she belongs. The latter effect is modeled by a discrete latent variable associated with each cluster. For the maximum likelihood estimation of the model parameters we outline an EM algorithm. We show how the proposed model may be used for assessing the development of cognitive Math achievement. This approach is applied to the analysis of a dataset collected in the Lombardy Region (Italy) and based on test scores over three years of middle-school students attending public and private schools

Social and Emotional Competencies and Science Performance in the USA: Evidence from PISA 2015

This paper asks whether students with different socioemotional learning (SEL) profiles perform differently in science. Using latent class analysis, we found three distinct groups of students: a majority of students who are relatively unmotivated and isolated, a sizeable group of students who are strong co-operators, and a relatively small group of students who are highly motivated and enjoy science, but do not value cooperation. After controlling for student and family covariates, as well as classroom, teaching and school leadership and institutional variables, the highly motivated, individualist group substantially outperformed the isolated group, with the co-operator group having intermediate performance. These SEL related differences in science performance were large, larger than performance differences associated with socioeconomic variables