Mariano Porcu

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.

A segmented regression model for event history data: an application to the fertility patterns in Italy

We propose a segmented discrete-time model for the analysis of event history data in demographic research. Through a unified regression framework, the model provides estimates of the effects of explanatory variables and jointly accommodates flexibly non-proportional differences via segmented relationships. The main appeal relies on ready availability of parameters, changepoints, and slopes, which may provide meaningful and intuitive information on the topic. Furthermore, specific linear constraints on the slopes may also be set to investigate particular patterns. We investigate the intervals between cohabitation and first childbirth and from first to second childbirth using individual data for Italian women from the Second National Survey on Fertility. The model provides insights into dramatic decrease of fertility experienced in Italy, in that it detects a ‘common’ tendency in delaying the onset of childbearing for the more recent cohorts and a ‘specific’ postponement strictly depending on the educational level and age at cohabitation.

Comparing degree programs from students’ assessments: A LCRA-based adjusted composite indicator

Taking into account the students’ evaluation of the quality of degree programs this paper presents a proposal for building up an adjusted performance indicator based on Latent Class Regression Analysis. The method enables us (i) to summarize in a single indicator statement multiple facets evaluated by students through a survey questionnaire and (ii) to control the variability in the evaluations that is mainly attributable to the characteristics (often referred as the Potential Confounding Factors) of the evaluators (students) rather than to real differences in the performances of the degree programs under evaluation. A simulation study is implemented in order to test the method and assess its potential when the composition of the degree programs as regards to students’ characteristics is sensibly different between one another. Results suggest that when the evaluations are strongly affected by the students’ covariates, the assessment based on the value of an unadjusted indicator can lead to bias and unreliable conclusions about the differences in performance. An application to real data is also provided.

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.

Handling Missing Data in Item Response Theory. Assessing the Accuracy of a Multiple Imputation Procedure Based on Latent Class Analysis

A critical issue in analyzing multi-item scales is missing data treatment. Previous studies on this topic in the framework of item response theory have shown that imputation procedures are in general associated with more accurate estimates of item location and discrimination parameters under several missing data generating mechanisms. This paper proposes a model-based multiple imputation procedure for multiple categorical items (dichotomous, multinomial or Likert-type) which relies on the results of latent class analysis to impute missing item responses. The effectiveness of the proposed technique is assessed in the estimation of item response theory parameters using a range of ad hoc measures. The accuracy of the method is assessed with respect to other single and multiple imputation procedures, under different missing data generating mechanisms and different rate of missingness (5% to 30%). The simulation results indicate that the proposed technique performs satisfactorily under all conditions and has the greatest potential with severe rates of missingness and under non ignorable missing data mechanisms. The method was implemented in R code with a function that calls scripts from a latent class analysis routine.

Cultural capital and educational strategies. Shaping boundaries between groups of students with homologous cultural behaviours

Abstract Rather than assessing its causal effect on educational attainment, the authors of this article aim to use the concept of cultural capital to define a huge, complex and interconnected collection of educational and school strategies adopted by students and families and to examine the way that these strategies are related to school inequalities. Data analysed come from the 2009 Italian survey for the Program for International Student Assessment run by the OECD. A Latent Class Regression Analysis has been applied to categorize four groups of individuals who share specific cultural habits, educational dispositions and choices, and social status; in short, the four groups differentiate individuals with a different endowment of the intangible asset, cultural capital. Moreover, using the socio-economic status as a covariate we link the latent class membership probability with individuals’ social standing and, consequently school choices.

Assessing the Quality of the Management of Degree Programs by Latent Class Analysis

In the evaluation of university quality, questionnaires with multi-item scales (Likert type) are often used in order to measure specific characteristics which are known to be relevant for the evaluation. The joint distribution of multiple responses provides a complete information in order to attach an overall measure of perceived quality to each student.

A multiple imputation approach in a survey on university teaching evaluation

Missing data is a problem frequently met in many surveys on the evaluation of university teaching. The method proposed in this work uses multiple imputation by stochastic regression (MISR) in order to recover partially observed units in surveys where multi-item Likert-type scale are used to measure a latent attribute, namely the quality of university teaching. The accuracy of the method has been tested simulating missing values in a benchmark data set completely at random (MCR) and at random (MAR). A simulation analysis has been carried out in order to assess the accuracy of the imputation procedure according to two standard criteria: accuracy in “distribution” and in “estimation”. The procedure has been compared with others widely applied missing data handling methods: multiple imputation by chained equation (MICE) and complete cases analysis (CCA).

The Credit Accumulation Process to Assess the Performances of Degree Programs: An Adjusted Indicator Based on the Result of Entrance Tests

In the frame of the performance indicators this paper aims to consider the bias produced by micro-level Potential Confounding Factors—PCF—by comparing the results observed using adjusted and unadjusted measures of outcome. Results at the university entrance tests together with the previous school experiences have been used as proxies of students’ competencies at the beginning of their academic career. The regularity of schooling process has been monitored using as an outcome variable the students’ status (drop out, still enrolled) and the number of credits gathered after one academic year. Adjusted indicators of the regularity of the students’ career are obtained using the results of zero-augmented models to investigate the relationships between the outcome measures and the potential PCF which are not directly associated to the learning process under evaluation.

Scaling the Latent Variable Cultural Capital Via Item Response Models and Latent Class Analysis

One of the main tasks of an educational system is to enrich the Cultural Capital of its students. The Cultural Capital linked to social origins is considered crucial in determining students’social life and subsequent professional achievement. This work moves from an ad hoc survey carried out on a sample of students who enrolled or applied for an entrance test at the university. The Cultural Capital is treated as a latent variable which students are supposed to possess at a greater or lesser degree. Latent Class Analysis is adopted in order to provide a non arbitrary scaling of Cultural Capital and to sort out mutually exclusive classes of students. Moreover, Item Response Models are implemented to assess the calibration of the questionnaire as an instrument to measure the Cultural Capital of the surveyed population.