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  000000534403

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  20210114161844

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  cr |||||||||||

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  200910s2020 xx o ||| 0 eng d

• 019

  ▼a  1195446727

• 020

  ▼a  1462544266
  ▼q  (electronic bk.)

• 020

  ▼a  9781462544264
  ▼q  (electronic bk.)

• 035

  ▼a  2473982
  ▼b  (N$T)

• 035

  ▼a  (OCoLC)1193591665
  ▼z  (OCoLC)1195446727

• 040

  ▼a  YDX
  ▼b  eng
  ▼c  YDX
  ▼d  YDX
  ▼d  OCLCF
  ▼d  OCLCO
  ▼d  EBLCP
  ▼d  N$T
  ▼d  UKAHL
  ▼d  248023

• 050

  ▼a  QA278.3
  ▼b  .G45 2020

• 082

  ▼a  001.4/22028553
  ▼2  23

• 100

  ▼a  GEISER, CHRISTIAN.

• 245

  ▼a  LONGITUDINAL STRUCTURAL EQUATION MODELING WITH MPLUS
  ▼h  [electronic resource]:
  ▼b  a latent state-trait perspective;a latent state-trait perspective.

• 260

  ▼a  [S.l.]:
  ▼b  GUILFORD PRESS, THE,
  ▼c  2020.

• 300

  ▼a  1 online resource.

• 490

  ▼a  Methodology in the Social Sciences Ser.

• 505

  ▼a  Cover -- Half Title Page -- Series Page -- Title Page -- Copyright -- Series Editor's Note -- Preface -- Brief Contents -- List of Abbreviations -- Guide to Statistical Symbols -- 1. A Measurement Theoretical Framework for Longitudinal Data: Introduction to Latent State-Trait Theory -- 2. Single-Factor Longitudinal Models for Single-Indicator Data -- 3. Multifactor Longitudinal Models for Single-Indicator Data -- 4. Latent State Models and Measurement Equivalence Testing in Longitudinal Studies -- 5. Multiple-Indicator Longitudinal Models -- 6. Modeling Intensive Longitudinal Data

• 505

  ▼a  7. Missing Data Handling -- 8. How to Choose between Models and Report the Results -- Extended Contents -- List of Abbreviations -- Guide to Statistical Symbols -- 1. A Measurement Theoretical Framework for Longitudinal Data: Introduction to Latent State-Trait Theory -- 1.1 Introduction -- 1.2 Latent State-Trait Theory -- 1.2.1 Introduction -- 1.2.2 Basic Idea -- 1.2.3 Random Experiment -- 1.2.4 Variables in LST-R Theory -- BOX 1.1. Key Concepts and Definitions in CTT -- 1.2.5 Properties -- 1.2.6 Coefficients -- BOX 1.2. Properties of the Latent Variables in LST-R Theory -- 1.3 Chapter Summary

• 505

  ▼a  1.4 Recommended Readings -- 2. Single-Factor Longitudinal Models for Single-Indicator Data -- 2.1 Introduction -- 2.2 The Random Intercept Model -- 2.2.1 Introduction -- 2.2.2 Model Description -- BOX 2.1. Available Information, Model Degrees of Freedom, and Model Identification in Single-Indicator Longitudinal Designs -- BOX 2.2. Defining the Random Intercept Model Based on LST-R Theory -- 2.2.3 Variance Decomposition and Reliability Coefficient -- 2.2.4 Mplus Application -- BOX 2.3. Model Fit Assessment and Model Comparisons -- 2.2.5 Summary -- 2.3 The Random and Fixed Intercepts Model

• 505

  ▼a  2.5 Chapter Summary -- 2.6 Recommended Reading -- Note -- 3. Multifactor Longitudinal Models for Single-Indicator Data -- 3.1 Introduction -- 3.2 The Simplex Model -- 3.2.1 Introduction -- 3.2.2 Model Description -- BOX 3.1. Defining the Simplex Model Based on LST-R Theory -- BOX 3.2. Should a Researcher Constrain State Residual or Measurement Error Variances in the Simplex Model? -- 3.2.3 Variance Decomposition and Coefficients -- 3.2.4 Assessing Stability and Change in the Simplex Model -- BOX 3.3. Endogenous versus Exogenous Variables in Structural Equation Models and Mplus

• 520

  ▼a  "An in-depth guide to executing longitudinal confirmatory factor analysis (CFA) and structural equation modeling (SEM) in Mplus, this book uses latent state-trait (LST) theory as a unifying conceptual framework, including the relevant coefficients of consistency, occasion-specificity, and reliability. Following a standard format, chapters review the theoretical underpinnings, strengths, and limitations of the various models; present data examples; and demonstrate each model's application and interpretation in Mplus, with numerous screen shots and output excerpts. Coverage encompasses both traditional models (autoregressive, change score, and growth curve models) and LST models, for analyzing single- and multiple-indicator data. The book discusses measurement equivalence testing, intensive longitudinal data modeling, and missing data handling, and provides strategies for model selection and reporting of results. User-friendly features include special-topic boxes, chapter summaries, and suggestions for further reading. The companion website features data sets, annotated syntax files, and output for all of the examples"--
  ▼c  Provided by publisher.

• 590

  ▼a  OCLC control number change

• 630

  ▼a  Mplus.

• 630

  ▼a  Mplus.
  ▼2  fast
  ▼0  (OCoLC)fst01787691

• 650

  ▼a  Structural equation modeling.

• 650

  ▼a  Longitudinal method.

• 650

  ▼a  Longitudinal method.
  ▼2  fast
  ▼0  (OCoLC)fst01002409

• 650

  ▼a  Structural equation modeling.
  ▼2  fast
  ▼0  (OCoLC)fst01738928

• 655

  ▼a  Electronic books.

• 655

  ▼a  Electronic books.

• 776

  ▼i  Print version:
  ▼a  Geiser, Christian
  ▼t  Longitudinal Structural Equation Modeling with Mplus : A Latent State-Trait Perspective.
  ▼d  New York : Guilford Publications,c2020

• 830

  ▼a  Methodology in the social sciences.

• 856

  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2473982

• 938

  ▼a  YBP Library Services
  ▼b  YANK
  ▼n  16939098

• 938

  ▼a  Askews and Holts Library Services
  ▼b  ASKH
  ▼n  AH37435598

• 938

  ▼a  ProQuest Ebook Central
  ▼b  EBLB
  ▼n  EBL6336106

• 938

  ▼a  EBSCOhost
  ▼b  EBSC
  ▼n  2473982

• 990

  ▼a  강리원

• 991

  ▼a  eBook

• 994

  ▼a  92
  ▼b  N$T