By Jacques A. Hagenaars (auth.), Kees van Montfort, Johan H.L. Oud, Albert Satorra (eds.)
This booklet combines longitudinal study and latent variable learn, i.e. it explains how longitudinal experiences with ambitions formulated by way of latent variables will be conducted, with an emphasis on detailing how the equipment are utilized. simply because longitudinal learn with latent variables at present makes use of varied methods with diversified histories, kinds of examine questions, and diverse machine courses to accomplish the research, the ebook is split into 9 chapters. ranging from (a) a few heritage information regarding the explicit strategy (a brief historical past and the most publications), every one bankruptcy then (b) describes the kind of study questions the procedure is ready to solution, (c) offers statistical and mathematical reasons of the types utilized in the knowledge research, (d) discusses the enter and output of the courses used, and (e) presents a number of examples with usual information units, permitting the readers to use the courses themselves.
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This e-book combines longitudinal learn and latent variable study, i. e. it explains how longitudinal reports with goals formulated by way of latent variables can be conducted, with an emphasis on detailing how the equipment are utilized. simply because longitudinal learn with latent variables presently makes use of varied techniques with diverse histories, sorts of learn questions, and various computing device courses to accomplish the research, the booklet is split into 9 chapters.
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Additional info for Longitudinal Research with Latent Variables
In this way, it is more clearly seen that the basic principles underlying SEMs for categorical data are essentially the same as for SEMs for continuous variables (Kiiveri & Speed, 1982; Pearl, 2000). Only their parameterizations differ: for continuous variables, linear regression equations are used; for categorical data, loglinear and logit equations (and more recent developments make it possible to mix continuous and categorical variables in many ways; see the last section). 4), makes the representation of SEMs simpler and, more importantly in practice, the estimation procedures often much more efficient enabling the researcher to estimate models that otherwise could not be handled.
LEM: A general program for the analysis of categorical data: Users manual(Tech. ). Tilburg, The Netherlands: Tilburg University. Vermunt, J. K. (1999). A general class of nonparametric models for ordinal categorical data. Sociological Methodology 1999, 29, 187-223. Washington DC: American Sociological Association. Vermunt, J. , & Magidson, J. (2002). Latent class cluster analysis. In J. A. Hagenaars & A. ), Applied latent class analysis. (pp. 89-106). Cambridge, UK: Cambridge University Press. Vermunt, J.
Oxford: Clarendon Press. Lord, F. , & Novick, M. R. (1968). Statistical theories of mental test scores. Reading, MA: Addison-Wesley. , & Verbeke, G. (2005). Models for discrete longitudinal data. New York: Springer-Verlag. , & van Montfort, K. (2006). Latent Markov models for categorical variables and time-dependent covariates. In K. van Montfort, J. Oud, & A. ), Longitudinal models in the behavioral and related sciences (pp. 1-18). Mahwah, NJ: Lawrence Erlbaum Associates. Muth´en, L. , & Muth´en, B.