Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-05T21:13:21.103Z Has data issue: false hasContentIssue false
  • English
  • Français

The utility of latent trait models in psychiatric epidemiology

Published online by Cambridge University Press:  09 July 2009

P. Duncan-Jones ,
D. A. Grayson  and
P. A. P. Moran
P. Duncan-Jones*
Affiliation:
NH and MRC Social Psychiatry Research Unit, The Australian National University, Canberra, Australia
D. A. Grayson
Affiliation:
NH and MRC Social Psychiatry Research Unit, The Australian National University, Canberra, Australia
P. A. P. Moran
Affiliation:
NH and MRC Social Psychiatry Research Unit, The Australian National University, Canberra, Australia
*
1Address for correspondence: P. Duncan-Jones, NH and MRC Social Psychiatry Research Unit, The Australian National University, GPO Box 4, Canberra, ACT 2601. Australia
Get access Rights & Permissions [Opens in a new window]

Synopsis

Latent trait modelling is a recent psychometric technique with great potential for the construction and refinement of psychiatric instruments. It provides a greater insight into the nature of measurement in psychiatry and the statistical machinery for improving it. This expository paper starts with a non-technical outline of the latent trait model, gives a detailed analysis of the 12-item General Health Questionnaire (GHQ) and examines points raised by the empirical analysis through computer simulation. It is shown that the latent trait model can give a good representation of empirical data and uncover new aspects of a familiar instrument. It provides a precise methodology for evaluating the functioning of a questionnaire and for developing better short instruments. It highlights the need, and provides the means, to tailor instruments for different tasks, such as (a) screening, and (b) measuring over the whole range of the population. We examine scoring in the light of the model, and show that simple scoring is often adequate. While points for further methodological development are noted, it is argued that the method is already sufficiently developed for general application.

Type
Research Article
Information
Psychological Medicine , Volume 16 , Issue 2 , May 1986 , pp. 391 - 405
Copyright
Copyright © Cambridge University Press 1986

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bech, P. (1981). Rating scales for affective disorders: their validity and consistency. Acta Psychiatrica Scandinavica, Suppl. 295.Google Scholar
Bejar, I. I. (1983). Achievement Testing. Quantitative Applications in the Social Sciences. Sage Publications: Beverly Hills.Google Scholar
Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee's ability. In Statistical Theories of Mental Test Scores (ed. Lord, F. M. and Novick, M. R.), pp. 395–479. Addison-Wesley: Reading, Mass.Google Scholar
Bock, R. D. & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: an application of an EM algorithm. Psychometrika 46, 443–459.CrossRefGoogle Scholar
Christofferson, A. (1975). Factor analysis of dichotomized variables. Psychometrika 40, 5–32.CrossRefGoogle Scholar
Fraser, C. (n.d.). NOHARM II: A Fortran program for fitting unidimensional and multidimensional normal ogive models of latent trait theory. Centre for Behavioural Studies, The University of New England, Armidale, NSW.Google Scholar
Goldberg, D. P. (1972). The Detection of Psychiatric Illness by Questionnaire. Maudsley Monograph No. 21. Oxford University Press: London.Google Scholar
Goldberg, D. P. (1978). Manual of the General Health Questionnaire. NFER-Nelson: Windsor.Google Scholar
Goldstein, H. (1980). Dimensionality, bias, independence and measurement scale problems in latent trait test score models. British Journal of Mathematical and Statistical Psychology 33, 234–246.CrossRefGoogle Scholar
Goodchild, M. E. & Duncan-Jones, P. (1985). Chronicity and the General Health Questionnaire. British Journal of Psychiatry 146, 55–61.CrossRefGoogle ScholarPubMed
Lewis, P. A. W., Goodman, A. S. & Miller, J. M. (1969). A pseudo-random number generator for the System /360. IBM Systems Journal 8.CrossRefGoogle Scholar
Lord, F. M. (1980). Applications of Item Response Theory to Practical Testing Problems. Lawrence Erlbaum: Hillsdale, N.J.Google Scholar
McDonald, R. P. (1967). Nonlinear Factor Analysis. Psychometric Monograph No. 15.CrossRefGoogle Scholar
McDonald, R. P. (1980). A simple comprehensive model for the analysis of covariance structures: some remarks on applications. British Journal of Mathematical and Statistical Psychology 33, 161–183.CrossRefGoogle Scholar
McDonald, R. P. (1982 a). Linear versus nonlinear models in item response theory. Applied Psychological Measurement 6, 379–396.CrossRefGoogle Scholar
McDonald, R. P. (1982 b). Some alternative approaches to the improvement of measurement in education and psychology: fitting latent trait models. In The Improvement of Measurement in Education and Psychology (ed. Spearritt, D.), pp. 213–237. Australian Council for Educational Research: Hawthorn, Victoria.Google Scholar
Mislevy, R. J. & Bock, R. D. (1981). BILOG 1: Maximum Likelihood Item Analysis and Test Scoring: Logistic Model. Distributed by International Educational Services: Mooresville, USA.Google Scholar
Mislevy, R. J. & Bock, R. D. (1982). Biweight estimates of latent ability. Educational and Psychological Measurement 42, 725–737.CrossRefGoogle Scholar
Mokken, R. J. & Lewis, C. (1982). A nonparametric approach to the analysis of dichotomous item responses. Applied Psychological Measurement 6, 417–430.Google Scholar
Múthen, B. (1979). A structural probit model with latent variables. Journal of the American Statistical Association 74, 807–811.Google Scholar
Múthen, B. (1984). A general structural equation model with dichotomous, ordered categorical and continuous latent variable indicators. Psychometrika 49, 115–132.CrossRefGoogle Scholar
Rasch, G. (1960). Probabilistic Models for Some Intelligence and Attainment Tests. Danish Institute of Educational Research: Copenhagen.Google Scholar
Reiser, M. (1981). Latent trait modelling of attitude items. In Social Measurement. Current Issues (ed. Bohrnstedt, G. W. and Borgatta, E. F.), pp. 117–144. Sage Publications: Beverly Hills.Google Scholar
SAS (1982). SAS User's Guide: Basics. SAS Institute: Cary, N.C.Google Scholar