Latent Variable Analysis with Ordinal Data: Comparing the Structural Equation and the Rating Scale Models (original) (raw)

New Developments of Latent Variable Models with Ordinal Data

2004

Riassunto: Nel contesto delle scienze sociali e comportamentali i modelli a variabili latenti rivestono un ruolo fondamentale nella valutazione degli atteggiamenti e delle abilit à. In tale contesto, frequentemente i dati raccolti presentano modalit à di tipo ordinale. Nel presente lavoro vengono illustrati due differenti approcci per la modellizzazione dei legami tra variabili latenti continue e variabili manifeste di natura ordinale, l’ Underlying Variable Approach(UVA) e l’ Item Response Function (IRF) approach. In prima istanza, le due soluzioni sono confrontate in termini teorici e successivamente con riferimento alle propriet̀a delle stime, attraverso uno studio di simulazione. Per la prima volta, viene proposto un algoritmo di stima per un modello di IRF con pi ù d due variabili latenti. Infine tali modelli sono applicati ad un data set reale che raccoglie i risultati di un’indagine di Customer Satisfaction degli utenti dei mezzi di trasporto pubblico.

Latent variable models for ordinal data

Contributions to Statistics, 2009

ABSTRACT Latent variable models with observed ordinal variables are particularly useful for analyzing survey data. Typical ordinal variables express attitudinal statements with response alternatives like “strongly disagree”, “disagree”, “strongly agree” or “very dissatisfied”, “dissatisfied”, “satisfied” and “very satisfied”.

Structural equation modeling with ordinal variables: a large sample case study

Quality & Quantity, 2011

In the behavioral sciences, response variables are often non-continuous, ordinal variables. Conventional structural equation models (SEMs) have been generalized to accommodate ordinal responses. In this study, three different estimation methods on real data were performed with ordinal variables. Empirical results obtained from the different estimation methods on given real large sample educational data were investigated and compared to recent simulation results. As a result, even very large sample is available, model estimations and fits for ordinal data are affected from inconvenient estimation methods thus it is concluded that asymptotically distribution free estimation method specialized for ordinal variables is more convenient way to model ordinal variables.

Modelling approaches for ordinal data: the case of Orientation Service evaluation

Quaderni di Statistica QdS, 2010

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