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110706s2011 njua ob 001 0 eng d |
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|z 2011007711
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|z (OCoLC)1303403671
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037 |
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|a 10.1002/9781119970583
|b Wiley InterScience
|n http://www3.interscience.wiley.com
|
050 |
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4 |
|a QA278.6
|b .B37 2011
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072 |
|
7 |
|a MAT
|x 029020
|2 bisacsh
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049 |
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|a HCDD
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100 |
1 |
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|a Bartholomew, David J.
|
245 |
1 |
0 |
|a Latent variable models and factor analysis :
|b a unified approach.
|
250 |
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|a 3rd ed. /
|b David Bartholomew, Martin Knott, Irini Moustaki.
|
260 |
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|a Hoboken, N.J. :
|b Wiley,
|c 2011.
|
300 |
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|a 1 online resource (xiii, 277 pages) :
|b illustrations
|
336 |
|
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|a text
|b txt
|2 rdacontent
|
337 |
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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347 |
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|a data file
|
490 |
1 |
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|a Wiley series in probability and statistics
|
504 |
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|a Includes bibliographical references and index.
|
505 |
0 |
0 |
|6 880-01
|t Front matter --
|t Basic ideas and examples --
|t The general linear latent variable model --
|t The normal linear factor model --
|t Binary data : latent trait models --
|t Polytomous data : latent trait models --
|t Latent class models --
|t Models and methods for manifest variables of mixed type --
|t Relationships between latent variables --
|t Related techniques for investigating dependency --
|t Software appendix --
|t References --
|t Author index --
|t Subject index.
|
520 |
|
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|a 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 techniques for investigating dependency. This book:Provides a unified approach showing how such apparently diverse methods as Latent Class Analysis and Factor Analysis are actually members of the same family. Presen.
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588 |
0 |
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|a Print version record.
|
650 |
|
0 |
|a Latent variables.
|
650 |
|
0 |
|a Latent structure analysis.
|
650 |
|
0 |
|a Factor analysis.
|
650 |
|
7 |
|a MATHEMATICS
|x Probability & Statistics
|x Multivariate Analysis.
|2 bisacsh
|
650 |
|
7 |
|a Factor analysis
|2 fast
|
650 |
|
7 |
|a Latent structure analysis
|2 fast
|
650 |
|
7 |
|a Latent variables
|2 fast
|
700 |
1 |
|
|a Knott, M.
|q (Martin)
|1 https://id.oclc.org/worldcat/entity/E39PBJtX7RrhPgmDDmX6drBdcP
|
700 |
1 |
|
|a Moustaki, Irini.
|
758 |
|
|
|i has work:
|a Latent variable models and factor analysis (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCH4cbWYW4JKKjdmkW74W9P
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
776 |
0 |
8 |
|i Print version:
|a Bartholomew, David J.
|t Latent variable models and factor analysis.
|b 3rd ed.
|d Hoboken, N.J. : Wiley, 2011
|z 9780470971925
|w (DLC) 2011007711
|w (OCoLC)710044915
|
830 |
|
0 |
|a Wiley series in probability and statistics.
|
856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=819225
|y Click for online access
|
880 |
0 |
0 |
|6 505-01/(S
|g Machine generated contents note:
|g 1.
|t Basic ideas and examples --
|g 1.1.
|t statistical problem --
|g 1.2.
|t basic idea --
|g 1.3.
|t Two examples --
|g 1.3.1.
|t Binary manifest variables and a single binary latent variable --
|g 1.3.2.
|t model based on normal distributions --
|g 1.4.
|t broader theoretical view --
|g 1.5.
|t Illustration of an alternative approach --
|g 1.6.
|t overview of special cases --
|g 1.7.
|t Principal components --
|g 1.8.
|t historical context --
|g 1.9.
|t Closely related fields in statistics --
|g 2.
|t general linear latent variable model --
|g 2.1.
|t Introduction --
|g 2.2.
|t model --
|g 2.3.
|t Some properties of the model --
|g 2.4.
|t special case --
|g 2.5.
|t sufficiency principle --
|g 2.6.
|t Principal special cases --
|g 2.7.
|t Latent variable models with non-linear terms --
|g 2.8.
|t Fitting the models --
|g 2.9.
|t Fitting by maximum likelihood --
|g 2.10.
|t Fitting by Bayesian methods --
|g 2.11.
|t Rotation --
|g 2.12.
|t Interpretation --
|g 2.13.
|t Sampling error of parameter estimates --
|g 2.14.
|t prior distribution --
|g 2.15.
|t Posterior analysis --
|g 2.16.
|t further note on the prior --
|g 2.17.
|t Psychometric inference --
|g 3.
|t normal linear factor model --
|g 3.1.
|t model --
|g 3.2.
|t Some distributional properties --
|g 3.3.
|t Constraints on the model --
|g 3.4.
|t Maximum likelihood estimation --
|g 3.5.
|t Maximum likelihood estimation by the E-M algorithm --
|g 3.6.
|t Sampling variation of estimators --
|g 3.7.
|t Goodness of fit and choice of q --
|g 3.7.1.
|t Model selection criteria --
|g 3.8.
|t Fitting without normality assumptions: least squares methods --
|g 3.9.
|t Other methods of fitting --
|g 3.10.
|t Approximate methods for estimating Φ --
|g 3.11.
|t Goodness of fit and choice of q for least squares methods --
|g 3.12.
|t Further estimation issues --
|g 3.12.1.
|t Consistency --
|g 3.12.2.
|t Scale-invariant estimation --
|g 3.12.3.
|t Heywood cases --
|g 3.13.
|t Rotation and related matters --
|g 3.13.1.
|t Orthogonal rotation --
|g 3.13.2.
|t Oblique rotation --
|g 3.13.3.
|t Related matters --
|g 3.14.
|t Posterior analysis: the normal case --
|g 3.15.
|t Posterior analysis: least squares --
|g 3.16.
|t Posterior analysis: a reliability approach --
|g 3.17.
|t Examples --
|g 4.
|t Binary data: latent trait models --
|g 4.1.
|t Preliminaries --
|g 4.2.
|t logit/normal model --
|g 4.3.
|t probit/normal model --
|g 4.4.
|t equivalence of the response function and underlying variable approaches --
|g 4.5.
|t Fitting the logit/normal model: the E-M algorithm --
|g 4.5.1.
|t Fitting the probit/normal model --
|g 4.5.2.
|t Other methods for approximating the integral --
|g 4.6.
|t Sampling properties of the maximum likelihood estimators --
|g 4.7.
|t Approximate maximum likelihood estimators --
|g 4.8.
|t Generalised least squares methods --
|g 4.9.
|t Goodness of fit --
|g 4.10.
|t Posterior analysis --
|g 4.11.
|t Fitting the logit/normal and probit/normal models: Markov chain Monte Carlo --
|g 4.11.1.
|t Gibbs sampling --
|g 4.11.2.
|t Metropolis-Hastings --
|g 4.11.3.
|t Choosing prior distributions --
|g 4.11.4.
|t Convergence diagnostics in MCMC --
|g 4.12.
|t Divergence of the estimation algorithm --
|g 4.13.
|t Examples --
|g 5.
|t Polytomous data: latent trait models --
|g 5.1.
|t Introduction --
|g 5.2.
|t response function model based on the sufficiency principle --
|g 5.3.
|t Parameter interpretation --
|g 5.4.
|t Rotation --
|g 5.5.
|t Maximum likelihood estimation of the polytomous logit model --
|g 5.6.
|t approximation to the likelihood --
|g 5.6.1.
|t One factor --
|g 5.6.2.
|t More than one factor --
|g 5.7.
|t Binary data as a special case --
|g 5.8.
|t Ordering of categories --
|g 5.8.1.
|t response function model for ordinal variables --
|g 5.8.2.
|t Maximum likelihood estimation of the model with ordinal variables --
|g 5.8.3.
|t partial credit model --
|g 5.8.4.
|t underlying variable model --
|g 5.9.
|t alternative underlying variable model --
|g 5.10.
|t Posterior analysis --
|g 5.11.
|t Further observations --
|g 5.12.
|t Examples of the analysis of polytomous data using the logit model --
|g 6.
|t Latent class models --
|g 6.1.
|t Introduction --
|g 6.2.
|t latent class model with binary manifest variables --
|g 6.3.
|t latent class model for binary data as a latent trait model --
|g 6.4.
|t K latent classes within the GLLVM --
|g 6.5.
|t Maximum likelihood estimation --
|g 6.6.
|t Standard errors --
|g 6.7.
|t Posterior analysis of the latent class model with binary manifest variables --
|g 6.8.
|t Goodness of fit --
|g 6.9.
|t Examples for binary data --
|g 6.10.
|t Latent class models with unordered polytomous manifest variables --
|g 6.11.
|t Latent class models with ordered polytomous manifest variables --
|g 6.12.
|t Maximum likelihood estimation --
|g 6.12.1.
|t Allocation of individuals to latent classes --
|g 6.13.
|t Examples for unordered polytomous data --
|g 6.14.
|t Identifiability --
|g 6.15.
|t Starting values --
|g 6.16.
|t Latent class models with metrical manifest variables --
|g 6.16.1.
|t Maximum likelihood estimation --
|g 6.16.2.
|t Other methods --
|g 6.16.3.
|t Allocation to categories --
|g 6.17.
|t Models with ordered latent classes --
|g 6.18.
|t Hybrid models --
|g 6.18.1.
|t Hybrid model with binary manifest variables --
|g 6.18.2.
|t Maximum likelihood estimation --
|g 7.
|t Models and methods for manifest variables of mixed type --
|g 7.1.
|t Introduction --
|g 7.2.
|t Principal results --
|g 7.3.
|t Other members of the exponential family --
|g 7.3.1.
|t binomial distribution --
|g 7.3.2.
|t Poisson distribution --
|g 7.3.3.
|t gamma distribution --
|g 7.4.
|t Maximum likelihood estimation --
|g 7.4.1.
|t Bernoulli manifest variables --
|g 7.4.2.
|t Normal manifest variables --
|g 7.4.3.
|t general E-M approach to solving the likelihood equations --
|g 7.4.4.
|t Interpretation of latent variables --
|g 7.5.
|t Sampling properties and goodness of fit --
|g 7.6.
|t Mixed latent class models --
|g 7.7.
|t Posterior analysis --
|g 7.8.
|t Examples --
|g 7.9.
|t Ordered categorical variables and other generalisations --
|g 8.
|t Relationships between latent variables --
|g 8.1.
|t Scope --
|g 8.2.
|t Correlated latent variables --
|g 8.3.
|t Procrustes methods --
|g 8.4.
|t Sources of prior knowledge --
|g 8.5.
|t Linear structural relations models --
|g 8.6.
|t LISREL model --
|g 8.6.1.
|t structural model --
|g 8.6.2.
|t measurement model --
|g 8.6.3.
|t model as a whole --
|g 8.7.
|t Adequacy of a structural equation model --
|g 8.8.
|t Structural relationships in a general setting --
|g 8.9.
|t Generalisations of the LISREL model --
|g 8.10.
|t Examples of models which are indistinguishable --
|g 8.11.
|t Implications for analysis --
|g 9.
|t Related techniques for investigating dependency --
|g 9.1.
|t Introduction --
|g 9.2.
|t Principal components analysis --
|g 9.2.1.
|t distributional treatment --
|g 9.2.2.
|t sample-based treatment --
|g 9.2.3.
|t Unordered categorical data --
|g 9.2.4.
|t Ordered categorical data --
|g 9.3.
|t alternative to the normal factor model --
|g 9.4.
|t Replacing latent variables by linear functions of the manifest variables --
|g 9.5.
|t Estimation of correlations and regressions between latent variables --
|g 9.6.
|t Q-Methodology --
|g 9.7.
|t Concluding reflections of the role of latent variables in statistical modelling.
|
903 |
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|a EBC-AC
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|a 92
|b HCD
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