Latent Variable Models and Factor Analysis
Seiten
1999
|
2nd Revised edition
Hodder Arnold (Verlag)
978-0-340-69243-1 (ISBN)
Hodder Arnold (Verlag)
978-0-340-69243-1 (ISBN)
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The latent variable is an unobserved variable but one which is supposed to have an impact on the system under study, such as "demand" in economics. This edition provides a unified treatment of latent variable models based on a common theoretical framework set out in the first edition.
Hitherto latent variable modelling has hovered on the fringes of the statistical mainstream but if the purpose of statistics is to deal with real problems, there is every reason for it to move closer to centre stage. In the social sciences especially, latent variables are common and if they are to be handled in a truly scientific manner, statistical theory must be developed to include them. This book aims to show how that should be done. This second edition is a complete re-working of the book of the same name which appeared in the Griffin's Statistical Monographs in 1987. Since then there has been a surge of interest in latent variable methods which has necessitated a radical revision of the material but the prime object of the book remains the same. It provides a unified and coherent treatment of the field from a statistical perspective. This is achieved by setting up a sufficiently general framework to enable the derivation of the commonly used models. The subsequent analysis is then done wholly within the realm of probability calculus and the theory of statistical inference.Numerical examples are provided as well as the software to carry them out ( where this is not otherwise available).
Additional data sets are provided in some cases so that the reader can aquire a wider experience of analysis and interpretation.
Hitherto latent variable modelling has hovered on the fringes of the statistical mainstream but if the purpose of statistics is to deal with real problems, there is every reason for it to move closer to centre stage. In the social sciences especially, latent variables are common and if they are to be handled in a truly scientific manner, statistical theory must be developed to include them. This book aims to show how that should be done. This second edition is a complete re-working of the book of the same name which appeared in the Griffin's Statistical Monographs in 1987. Since then there has been a surge of interest in latent variable methods which has necessitated a radical revision of the material but the prime object of the book remains the same. It provides a unified and coherent treatment of the field from a statistical perspective. This is achieved by setting up a sufficiently general framework to enable the derivation of the commonly used models. The subsequent analysis is then done wholly within the realm of probability calculus and the theory of statistical inference.Numerical examples are provided as well as the software to carry them out ( where this is not otherwise available).
Additional data sets are provided in some cases so that the reader can aquire a wider experience of analysis and interpretation.
David Bartholomew is Professor of Statistics at the London School of Economics
Basic ideas; foundations; factor analysis and latent profile analysis; latent class and latent trait analysis - binary response data, polysomous response data; mixed response variables; posterior analysis; linear structural relations methods; comparison with principle components analysis; synoptic view.
| Erscheint lt. Verlag | 28.5.1999 |
|---|---|
| Reihe/Serie | Kendall's Library of Statistics ; v. 7 |
| Überarbeitung | Martin Knott |
| Zusatzinfo | b&w illustrations |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 160 x 240 mm |
| Gewicht | 522 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
| Sozialwissenschaften ► Soziologie ► Empirische Sozialforschung | |
| ISBN-10 | 0-340-69243-X / 034069243X |
| ISBN-13 | 978-0-340-69243-1 / 9780340692431 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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