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Saturday, July 25, 2020 | History

2 edition of Statistical modelling and latent variables found in the catalog.

Statistical modelling and latent variables

Statistical modelling and latent variables

  • 110 Want to read
  • 38 Currently reading

Published by North-Holland in Amsterdam, New York .
Written in English

    Subjects:
  • Linear models (Statistics) -- Congresses.,
  • Latent variables -- Congresses.

  • Edition Notes

    Statementedited by K. Haagen, D.J. Bartholomew, M. Deistler.
    ContributionsHaagen, Klaus, 1944-, Bartholomew, David J., Deistler, M.
    Classifications
    LC ClassificationsQA279 .S724 1993
    The Physical Object
    Paginationx, 355 p. :
    Number of Pages355
    ID Numbers
    Open LibraryOL1405441M
    ISBN 100444898328
    LC Control Number93013660

    A latent variable model, as the name suggests, is a statistical model that contains latent, that is, unobserved, variables. Their roots go back to Spearman's seminal work on factor analysis, which is arguably the first well-articulated latent variable model to be widely used in psychology, mental health research, and allied disciplines. Because of the association of factor analysis with. Latent Variable Models/Latent Class Models. Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models by Anders Skrondal and Sophia Rabe-Hesketh (2 copies) Latent Variable Mixture Models Edited by Gregory R. Hancock and Karen M. Samuelsen; Latent Class Scaling Analysis by C. Mitchell Dayton.

    Nonlinear Latent Variable Models. A simple nonlinear model. Polynomial models. Models for qualitative and limited-dependent variables. The LISCOMP model. General parametric nonlinear regression. Bibliographical notes. Appendix A. Matrices, Statistics, and Calculus. Some results from matrix algebra. Some specific results. Definite matrices. modelling and its relationship to other statistical methods, particularly regression, path, and latent variable models. Linear structural equations with latent variables - Springer A one-week summer course on latent variables and.

    Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective. This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples. Nature and interpretation of a latent variable is also introduced along with related . Structural Equations with Latent Variables is a book by Kenneth explains basic ideas and methods in the field of structural equation modeling and is considered to be an important technical reference. It is held to be a classic textbook on the topic.


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Statistical modelling and latent variables Download PDF EPUB FB2

This book reviews the statistical procedures used to detect measurement bias. Measurement bias is examined from a general latent variable perspective so as to accommodate different forms of testing in a variety of contexts including cognitive or clinical variables, attitudes, personality dimensions, or emotional states.

Measurement models that underlie psychometric practice are described. Statistical methods based on models with latent variables play a role in the analysis of multivariate data. This book presents a selection of papers which address the problems involved in developing a unifying statistical theory for latent variable models.

The measurement model of a latent variable with effect indicators is the set of relationships (modeled as equations) in which the latent variable is set as the predictor of the indicators. This diagram could be written as a set of 5 regression models. Latent Variable Models and Factor Analysis: A Unified Approach (Wiley Series in Probability and Statistics Book ) - Kindle edition by Bartholomew, David J., Knott, Martin, Moustaki, Irini.

Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Latent Variable Models and Factor Analysis: /5(3). The Generalized Linear Latent and Mixed Modeling Statistical modelling and latent variables book framework allows such models to be specified by simply letting the usual structural part of the model include latent and observed variables varying at different levels.

Models of this kind are applied to the U.S. sample of the Program for International Student Assessment (PISA) to. Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective.

This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples. The book, belonging to the series “Studies in Theoretical and Applied Statistics– Selected Papers from the Statistical Societies”, presents a peer-reviewed selection of contributions on relevant topics organized by the editors on the occasion of the SIS Statistical Conference "Advances in Latent Variables.

Methods, Models and. Intended for use in graduate or advanced undergraduate courses in latent variable modeling, factor analysis, structural equation modeling, item response theory, measurement, or multivariate statistics taught in psychology, education, human development, and social and health sciences, researchers in these fields also appreciate this book’s.

The software Lisrel was developed to model and analyze data using structural equation models which involve the introduction of latent variables. Although this topic has historically been most commonly used in the social sciences including psychology and sociology, it is finding a wide range of applications as statisticians encounter more and Reviews: encompasses such diverse statistical techniques as path analysis, confirmatory factor analysis, causal modeling with latent variables, and even analysis of variance and multiple linear regression.

The course features an introduction to the logic of SEM, the assumptions and required input for SEM analysis, and how to perform SEM analyses using AMOS. A latent variable model is a statistical model that relates a set of observable variables (so-called manifest variables) to a set of latent variables.

It is assumed that the responses on the indicators or manifest variables are the result of an individual's position on the latent variable(s), and that the manifest variables have nothing in common after controlling for the latent variable.

Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective. This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples.

Nature and interpretation of a latent variable is also introduced along with. In statistics, latent variables (from Latin: present participle of lateo (“lie hidden”), as opposed to observable variables) are variables that are not directly observed but are rather inferred (through a mathematical model) from other variables that are observed (directly measured).Mathematical models that aim to explain observed variables in terms of latent variables are called latent.

Using latent variable models, we can assess how well these latent variables are measured, how they change over time, and/or how they are associated with other (latent or directly observed) variables. Two widely known frameworks for latent variable modeling are Structural Equation Modeling (SEM) and Item Response Theory (IRT).

The position is essentially the same with the latent variable models dis-cussed above. The latent variable is a source of unobservable variation in some quantity which characterizes members of the population.

For the la-tent class model this latent variable is categorical, for the latent trait and factor analysis model it is continuous. A Nontechnical Introduction to Latent Class Models by Jay Magidson, Ph.D.

Statistical Innovations Inc. Jeroen K. Vermunt, Ph.D. Tilburg University, the Netherlands Over the past several years more significant books have been published on latent class (LC) and finite mixture models than any other class of statistical models.

The recent. Latent Variable Models and Factor Analysis. New York: Oxford University Press. Berkane, Maia, ed. Latent variable modeling and applications to causality. New York: Springer-Verlag.

ISBN: Bollen, Kenneth A. Structural Equations with Latent Variables. New York: John Wiley. ISBN: In statistics, a latent class model (LCM) relates a set of observed (usually discrete) multivariate variables to a set of latent is a type of latent variable is called a latent class model because the latent variable is discrete.

A class is characterized by a pattern of conditional probabilities that indicate the chance that variables take on certain values. Latent Variable Models. A latent variable model is a statistical model that relates a set of observed variables to a set of latent variables.

From: Psychology of Learning and Motivation, Related terms. This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use.

The lavaan package is developed to provide useRs, researchers and teachers a free open-source, but commercial-quality package for latent variable modeling. You can use lavaan to estimate a large variety of multivariate statistical models, including path analysis, confirmatory factor analysis, structural equation modeling and growth curve models.An alternative to the normal factor model Replacing latent variables by linear functions of the manifest variables Estimation of correlations and regressions between latent variables Q-Methodology Concluding reflections of the role of latent variables in statistical modelling Software appendix   This book demonstrates how to conduct latent variable modeling (LVM) in R by highlighting the features of each model, their specialized uses, examples, sample code and output, and an interpretation of the results.

Each chapter features a detailed example including the analysis of the data using R, the relevant theory, the assumptions underlying the model, and other statistical details .