Assessing the effectiveness of a sthocastic regression imputation method for ordered categorical data

The main aim of this paper is to describe a workable method based on stochastic regression and multiple imputation analysis (MISR) to recover for missingness in surveys where multi-item Likert-type scale are used to measure a latent attribute (namely, the quality of university teaching). A simulation analysis has been carried out and results have been compared in terms of bias and efficiency with other missing data handling methods, specifically: Complete Cases Analysis (CCA) and Multiple Imputation by Chained Equations (MICE). The authors provide also functions (implemented in R language) to apply the procedure to a matrix of ordered categorical items. Functions described allow: (i) to simulate missing data at random and completely at random; (ii) to replicate the simulation study presented in this work in order to assess the accuracy in distribution and in estimation of a multiple imputation procedure

Quality of life among university students in Cagliari. A synthetic indicator

This article reports findings from a survey addressed to measure university students’ quality of life in Cagliari. The aim to build up a synthetic indicator of students’ ‘quality of life at the university’ has been pursued by adopting an ad hoc modeling approach to scale ordered items (Item Response Models) which belongs to the family of the Generalized Linear and non Linear Mixed Models . The sensibility of the results has been deeply analyzed by setting up several models with different characteristics. A comparison study with other scaling methods has been made

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

Differences of cultural capitol among students in transition to university some first survey evidences

The role played by ‘Cultural Capital’ is crucial in shaping students’ decisions with respect to the school university transition. This work is based on an ad hoc survey carried out on a sample of students enrolled in 2006 in the University of Cagliari. The ‘cultural capital’ is a latent variable which students are supposed to possess at a greater or lesser degree. It has been here operationalized in four sub-components: (i) built-up by activities made by students themselves; (ii) built up by activities made by students’ parents; (iii) transmitted by students’ parents; (iv) built-up by formal education experiences. Each sub-component has been evaluated via students’ responses to a battery of items in a questionnaire. Latent Class Analysis has been adopted in order to provide non arbitrary scaling of some of the sub-components and to sort out mutually exclusive classes of students, characterized by a different intensity of the latent variable. Moreover, Item Response Models have been used to assess the calibration of the questionnaire as an instrument to measure the cultural capital of the targeted population

Ulteriori proposte per la determinazione di indicatori di inefficienza dell'attività formativa dell'Università

Evaluation is one of the major tasks for managing the Italian University system. One of the evaluation parameters has to do with the efficiency/inefficiency of the university in pursuing one of its main goals: graduation of the students. Inefficiency could be seen with respect to three different components: (a) students quitting before the end of the legal length of their studies, (b) students quitting in a period that is the double of the legal length of the studies, (c) students that do not terminate their studies in time. The aim of the present paper is to propose some indicators that can be fruitfully used in a joint evaluation of these components. Some simulations are also provided

Don’t call It smart: working from home during the pandemic crisis

The recent COVID-19 pandemic and related social distancing measures have significantly changed worldwide employment conditions. In developed economies, institutions and organizations, both public and private, are called upon to reflect on new organizational models of work and human resource management, which - in fact - should offer workers sufficient flexibility in adapting their work schedules remotely to their personal (and family) needs. This study aims to explore, within a Job Demands-Resources framework, whether and to what extent job demands (workload and social isolation), organizational job resources (perceived organizational support), and personal resources (self-efficacy, vision about the future and commitment to organizational change) have affected workers’ quality of life during the pandemic, taking into account the potential mediating role of job satisfaction and perceived stress. Using data from a sample of 293 workers, we estimate measurement and structural models, according to the Item Response Theory and the Path analysis frameworks, which allow us to operationalize the latent traits and study the complex structure of relationships between the latent dimensions. We inserted in the model as control variables, the socio-economic and demographic characteristics of the respondents, with particular emphasis on gender differences and the presence and age of children. The study offers insights into the relationship between remote work and quality of life, and the need to rethink human resource management policies considering the opportunities and critical issues highlighted by working full-time remotely

A further proposal to perform multiple imputation on a bunch of polytomous items based on latent class analysis

This work advances an imputation procedure for categorical scales which relays on the results of Latent Class Analysis and Multiple Imputation Analysis. The procedure allows us to use the information stored in the joint multivariate structure of the data set and to take into account the uncertainty related to the true unobserved values. The accuracy of the results is validated in the Item Response Models framework by assessing the accuracy in estimation of key parameters in a data set in which observations are simulated Missing at Random. The sensitivity of the multiple imputation methods is assessed with respect to the following factors: The number of latent classes set up in the Latent Class Model and the rate of missing observations in each variable. The relative accuracy in estimation is assessed with respect to the Multiple Imputation By Chained Equation missing data handling method for categorical variables

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

L'imputazione dei dati mancanti: l'effetto sui parametri di un Extended Logistic Rasch Model

Il problema dei dati mancanti è abbastanza comune nella ricerca empirica, specialmente nelle scienze sociali in cui il tentativo di misurazione di quantità non direttamente osservabili (variabili latenti)avviene attraverso la somministrazione di test o questionari costituiti da più item. I modelli statistici finalizzati alla soluzione di tale problema richiedono, in genere, un elevato numero di osservazioni per ogni unità coinvolta nell’analisi. In un contesto multivariato il problema si amplifica, poiché nel modello sono considerati più item per ciascuna osservazione: la probabilità, quindi, di avere almeno un dato mancante non è irrilevante ed è, inoltre, crescente al crescere del numero di item. Dopo una breve panoramica di alcuni dei principali approcci ai dati mancanti, il lavoro pone l’attenzione sul metodo della Multiple Imputation, valutandone i vantaggi tramite il confronto con altri tre approcci noti e largamente usati in letteratura. L’esempio che viene riportato si focalizza sul confronto dell’efficienza delle stime dei parametri di posizione degli item quando si applica l’Extended Logistic Model (ELM) di Rasch. L’ambito applicativo a cui si vuole fare qui riferimento è quello della valutazione della didattica universitaria, da anni adottata da tutti gli Atenei pubblici italiani