By George A. Marcoulides, Irini Moustaki
This edited quantity positive aspects state-of-the-art subject matters from the best researchers within the components of latent variable modeling. content material highlights comprise assurance of ways facing lacking values, semi-parametric estimation, strong research, hierarchical info, issue ratings, multi-group research, and version checking out. New methodological issues are illustrated with actual purposes. the fabric awarded brings jointly traditions: psychometrics and structural equation modeling. Latent Variable and Latent constitution Models' thought-provoking chapters from the best researchers within the zone can help to stimulate principles for additional study for a few years to return.
This quantity can be of curiosity to researchers and practitioners from a wide selection of disciplines, together with biology, company, economics, schooling, medication, psychology, sociology, and different social and behavioral sciences. A operating wisdom of uncomplicated multivariate statistics and size idea is assumed.
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Additional info for Latent Variable and Latent Structure Models
1 gives a list of all the different types of latent variable models that can be used for inferring information about attitude from non-responses or middle categories. All these models are latent trait models and therefore assume that the latent variables are continuous variables. All models, except from 1 and 7, are models that deal with a mixture of item types. 1 without missing values can be found in Moustaki (1996), Sammel, Ryan, and Legler (1997), Moustaki and Knott (2000a) and Moustaki (2000b).
2 Disciplinary focus Another distorting feature springs from the diverse disciplinary origins of the various models. Factor analysis was invented by a psychologist and largely developed by psychologists. Latent structure analysis was a product of sociology. This close tie with substantive problems had obvious advantages, principally that the problems tackled were those which are important rather than merely tractable. But it also had disadvantages. Many of the innovators lacked the technical tools necessary and did not always realize that some, at least, were already available in other fields.
The coefficients ri1(s) (Eq. 11) shows how the probability of expressing an opinion increases or decreases with respect to the attitude dimension. All the above models can be fitted with the program LATENT Moustaki (2000a) in the following steps: first generate the p polytomous response propensity variables from the original manifest variables; second fit the two factor model to the 2p items while constraining the loadings of the second factor for the manifest attitudinal variables, binary, polytomous and metric, to zero.