By B. S. Everitt (auth.)
Latent variable types are utilized in many components of the social and behavioural sciences, and the expanding availability of machine applications for becoming such versions is probably going to extend their recognition. This ebook makes an attempt to introduce such types to utilized statisticians and learn employees attracted to exploring the constitution of covari ance and correlation matrices by way of a small variety of unob servable constructs. The emphasis is at the functional program of the systems instead of on certain dialogue in their mathe matical and statistical houses. it truly is assumed that the reader knows the main favourite statistical suggestions and techniques, rather regression, and likewise has a good wisdom of matrix algebra. My thank you are because of my colleagues Dr David Hand and Dr Graham Dunn for invaluable reviews at the ebook, to Mrs Bertha Lakey for her cautious typing of a tricky manuscript and to Peter Cuttance for tips with the LlSREL package deal. moreover the textual content sincerely owes greatly to the paintings on structural equation types released by means of Karl Joreskog, Dag Sorbom, Peter Bentler, Michael Browne and others.
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Additional resources for An Introduction to Latent Variable Models
Tests 1 and 2 are conventional 'intelligence tests' purporting to measure conventional thinking. 00 tests oftluency (F) and originality (0) in divergent thinking. Tests 36 require verbal responses, whilst tests 7 and 8 require non-verbal responses. On the basis of psychological theory it is believed that the tests measure three relatively distinct aspects of mental behaviour: convergent thinking, verbal divergent thinking and non-verbal divergent thinking. In other words it is thought that three latent variables may account for the observed correlations between the eight tests, the first latent variable being indicated by tests 1 and 2, the second by variables 3-6 and the third by variables 7 and 8.
2 The LISREL model The LISREL model consists essentially of two parts, the measurement model and the structural equation model; the first of these specifies how the latent variables are related to the observed or measured variables, and the second specifies the relationship between the latent variables. (Since, in this section, we shall be interested only in the analysis of data from a single population, location parameters will not be of concern and, consequently, we shall assume that all variables, observed as well as latent, have zero expected values.
Factor analysis has probably attracted more critical comment than almost any other statistical technique, primarily because the factor loadings are not uniquely determined by the basic factor model. However, much of this criticism has been ill-judged and perhaps the greatest danger in the use of factor analysis lies in taking the model too seriously since it is only likely to be a very idealized approximation to the truth in the situations in which it is generally applied. Such an approximation may, however, prove a valuable starting point for further investigations.
An Introduction to Latent Variable Models by B. S. Everitt (auth.)