Home > Research > Publications & Outputs > Latent variable models for categorical data
View graph of relations

Latent variable models for categorical data

Research output: Contribution to journalJournal article


<mark>Journal publication date</mark>2002
<mark>Journal</mark>Statistics and Computing
Issue number2
Number of pages9
Pages (from-to)153-161
<mark>Original language</mark>English


Two useful statistical methods for generating a latent variable are described and extended to incorporate polytomous data and additional covariates. Item response analysis is not well-known outside its area of application, mainly because the procedures to fit the models are computer intensive and not routinely available within general statistical software packages. The linear score technique is less computer intensive, straightforward to implement and has been proposed as a good approximation to item response analysis. Both methods have been implemented in the standard statistical software package GLIM 4.0, and are compared to
determine their effectiveness.