- Docente: Silvia Cagnone
- Credits: 6
- SSD: SECS-S/01
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Bologna
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Corso:
Second cycle degree programme (LM) in
Statistics, Economics and Business (cod. 8876)
Also valid for Second cycle degree programme (LM) in Statistical Sciences (cod. 9222)
Learning outcomes
By the end of the course the student acquires knowledge of multivariate statistical methods based on latent variable models for the analysis of categorical and continuous data. The student is also able to choose the best method to perform multivariate analyses of a given dataset and to interpret the obtained results.
Course contents
Introduction to the latent variable models.
The normal linear factor model: specification, maximum likelihood estimation by the EM algorithm, goodness of fit.
Latent trait model with binary data: specification of logit/normit model and normit/normit model, model estimation by the E-M algorithm, goodness of fit.
Latent trait model with polytomous and ordinal data: specification and parameter interpretation. The underlyng variable approach.
Latent class model with binary data: specification, identifiability, maximum likelihood estimation, goodness of fit.
Readings/Bibliography
Bartholomew D., Knott M., Moustaki I (2011), Latent variable models and factor analysis : a unified approach/third ed. Chichester, UK : Wiley.
Bartholomew D., Moustaki I., Steele F., Galbraith J.I. (2002), The Analysis and Interpretation of Multivariate Data for Social Scientists,Chapman and Hall/CRC.
Teaching methods
Lectures and tutorials
Assessment methods
The exam consists of a mandatory written exam. It is composed by questions concerning the theoretical aspects and questions mainly focused on the data analysis and the interpretion of the results. During the exam the use of textbooks, notes and computers tools are not allowed.
Teaching tools
Teacher's notes available at the web-site https://virtuale.unibo.it/
Office hours
See the website of Silvia Cagnone