Francesca Giambona

Introduction to bifactor polytomous item response theory analysis

A bifactor item response theory model can be used to aid in the interpretation of the dimensionality of a multifaceted questionnaire that assumes continuous latent variables underlying the propensity to respond to items. This model can be used to describe the locations of people on a general continuous latent variable as well as on continuous orthogonal specific traits that characterize responses to groups of items. The bifactor graded response (bifac-GR) model is presented in contrast to a correlated traits (or multidimensional GR model) and unidimensional GR model. Bifac-GR model specification, assumptions, estimation, and interpretation are demonstrated with a reanalysis of data (Campbell, 2008) on the Shared Activities Questionnaire. We also show the importance of marginalizing the slopes for interpretation purposes and we extend the concept to the interpretation of the information function. To go along with the illustrative example analyses, we have made available supplementary files that include command file (syntax) examples and outputs from flexMIRT, IRTPRO, R, Mplus, and STATA. Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.jsp.2016.11.001. Data needed to reproduce analyses in this article are available as supplemental materials (online only) in the Appendix of this article.

Introduction to Latent Class Analysis With Applications

Latent class analysis (LCA) is a statistical method used to group individuals (cases, units) into classes (categories) of an unobserved (latent) variable on the basis of the responses made on a set of nominal, ordinal, or continuous observed variables. In this article, we introduce LCA in order to demonstrate its usefulness to early adolescence researchers. We provide an application of LCA to empirical data collected from a national survey carried out in 2010 in Italy to assess mathematics and reading skills of fifth-grade primary school pupils (10 years in age). The data were used to measure pupils’ supplies of cultural capital by specifying a latent class model. This article aims to describe and interpret results of LCA, allowing users to replicate the analysis. All LCA examples included in the text are illustrated using the Latent GOLD package, and command files needed to reproduce all analyses with SAS and R are available as supplemental online appendix files along with the example data files.

Using Discrete-Time Multistate Models to Analyze Students’ University Pathways

The methodologies adopted in the last decades to analyze students’ university careers using cohort studies focus mainly on the risk to observe one of the possible competing states, specifically dropout or graduation, after several years of follow-up. In this perspective all the other event types that may prevent the occurrence of the target event are treated as censored observations. A broader analysis of students’ university careers from undergraduate to postgraduate status reveals that several competing and noncompeting events may occur, some of which have been denoted as absorbing while others as intermediate. In this study we propose to use multistate models to analyze the complexity of students’ careers and to assess how the risk to experience different states varies along the time for students’ with different profiles. An application is provided to show the usefulness of this approach.