Longitudinal structural equation modeling with Mplus :: a latent state-trait perspective /
"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 relia...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York :
Guilford Publications,
2021.
|
| Schriftenreihe: | Methodology in the Social Sciences Ser.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "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"-- |
| Beschreibung: | Description based upon print version of record. 3.2.5 Mplus Application. |
| Beschreibung: | 1 online resource (371 pages) |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781462544264 1462544266 |
Internformat
MARC
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| 100 | 1 | |a Geiser, Christian, |e author. | |
| 245 | 1 | 0 | |a Longitudinal structural equation modeling with Mplus : |b a latent state-trait perspective / |c Christian Geiser. |
| 264 | 1 | |a New York : |b Guilford Publications, |c 2021. | |
| 264 | 4 | |c ©2021 | |
| 300 | |a 1 online resource (371 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 1 | |a Methodology in the Social Sciences Ser. | |
| 500 | |a Description based upon print version of record. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |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 | 8 | |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 | 8 | |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 | 8 | |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 | |
| 500 | |a 3.2.5 Mplus Application. | ||
| 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. | ||
| 630 | 0 | 0 | |a Mplus. |0 http://id.loc.gov/authorities/names/n2011042550 |
| 630 | 0 | 7 | |a Mplus |2 fast |
| 650 | 0 | |a Structural equation modeling. |0 http://id.loc.gov/authorities/subjects/sh2005008800 | |
| 650 | 0 | |a Longitudinal method. |0 http://id.loc.gov/authorities/subjects/sh85078296 | |
| 650 | 6 | |a Modèles d'équations structurales. | |
| 650 | 6 | |a Méthode longitudinale. | |
| 650 | 7 | |a Longitudinal method |2 fast | |
| 650 | 7 | |a Structural equation modeling |2 fast | |
| 758 | |i has work: |a Longitudinal structural equation modeling with Mplus (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGjBKrWgwY34F4VFVK3DC3 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |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 | 0 | |a Methodology in the Social Sciences Ser. | |
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| 880 | 8 | |6 505-00/(S |a 2.3.1 Introduction -- 2.3.2 Model Description -- BOX 2.4. Means of Linear Combinations -- BOX 2.5. Defining the Random and Fixed Intercepts Model Based on LST-R Theory -- 2.3.3 Variance Decomposition and Reliability Coefficient -- 2.3.4 Mplus Application -- 2.3.5 Summary -- 2.4 The ξ-Congeneric Model -- 2.4.1 Introduction -- 2.4.2 Model Description -- BOX 2.6. Defining the ξ-Congeneric Model Based on LST Theory -- 2.4.3 Variance Decomposition and Reliability Coefficient -- 2.4.4 Mplus Application -- BOX 2.7. The MODEL CONSTRAINT and MODEL TEST Options in Mplus -- 2.4.5 Summary | |
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL6336106 | ||
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Datensatz im Suchindex
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| contents | 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 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 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 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 |
| ctrlnum | (OCoLC)1223097731 |
| dewey-full | 001.4/22028553 |
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| id | ZDB-4-EBA-on1223097731 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:30:08Z |
| institution | BVB |
| isbn | 9781462544264 1462544266 |
| language | English |
| oclc_num | 1223097731 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (371 pages) |
| psigel | ZDB-4-EBA |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Guilford Publications, |
| record_format | marc |
| series | Methodology in the Social Sciences Ser. |
| series2 | Methodology in the Social Sciences Ser. |
| spelling | Geiser, Christian, author. Longitudinal structural equation modeling with Mplus : a latent state-trait perspective / Christian Geiser. New York : Guilford Publications, 2021. ©2021 1 online resource (371 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Methodology in the Social Sciences Ser. Description based upon print version of record. Includes bibliographical references and index. 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 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 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 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 3.2.5 Mplus Application. "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"-- Provided by publisher. Mplus. http://id.loc.gov/authorities/names/n2011042550 Mplus fast Structural equation modeling. http://id.loc.gov/authorities/subjects/sh2005008800 Longitudinal method. http://id.loc.gov/authorities/subjects/sh85078296 Modèles d'équations structurales. Méthode longitudinale. Longitudinal method fast Structural equation modeling fast has work: Longitudinal structural equation modeling with Mplus (Text) https://id.oclc.org/worldcat/entity/E39PCGjBKrWgwY34F4VFVK3DC3 https://id.oclc.org/worldcat/ontology/hasWork Print version: Geiser, Christian Longitudinal Structural Equation Modeling with Mplus : A Latent State-Trait Perspective New York : Guilford Publications,c2020 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2473982 Volltext 505-00/(S 2.3.1 Introduction -- 2.3.2 Model Description -- BOX 2.4. Means of Linear Combinations -- BOX 2.5. Defining the Random and Fixed Intercepts Model Based on LST-R Theory -- 2.3.3 Variance Decomposition and Reliability Coefficient -- 2.3.4 Mplus Application -- 2.3.5 Summary -- 2.4 The ξ-Congeneric Model -- 2.4.1 Introduction -- 2.4.2 Model Description -- BOX 2.6. Defining the ξ-Congeneric Model Based on LST Theory -- 2.4.3 Variance Decomposition and Reliability Coefficient -- 2.4.4 Mplus Application -- BOX 2.7. The MODEL CONSTRAINT and MODEL TEST Options in Mplus -- 2.4.5 Summary |
| spellingShingle | Geiser, Christian Longitudinal structural equation modeling with Mplus : a latent state-trait perspective / Methodology in the Social Sciences Ser. 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 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 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 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 Mplus. http://id.loc.gov/authorities/names/n2011042550 Mplus fast Structural equation modeling. http://id.loc.gov/authorities/subjects/sh2005008800 Longitudinal method. http://id.loc.gov/authorities/subjects/sh85078296 Modèles d'équations structurales. Méthode longitudinale. Longitudinal method fast Structural equation modeling fast |
| subject_GND | http://id.loc.gov/authorities/names/n2011042550 http://id.loc.gov/authorities/subjects/sh2005008800 http://id.loc.gov/authorities/subjects/sh85078296 |
| title | Longitudinal structural equation modeling with Mplus : a latent state-trait perspective / |
| title_auth | Longitudinal structural equation modeling with Mplus : a latent state-trait perspective / |
| title_exact_search | Longitudinal structural equation modeling with Mplus : a latent state-trait perspective / |
| title_full | Longitudinal structural equation modeling with Mplus : a latent state-trait perspective / Christian Geiser. |
| title_fullStr | Longitudinal structural equation modeling with Mplus : a latent state-trait perspective / Christian Geiser. |
| title_full_unstemmed | Longitudinal structural equation modeling with Mplus : a latent state-trait perspective / Christian Geiser. |
| title_short | Longitudinal structural equation modeling with Mplus : |
| title_sort | longitudinal structural equation modeling with mplus a latent state trait perspective |
| title_sub | a latent state-trait perspective / |
| topic | Mplus. http://id.loc.gov/authorities/names/n2011042550 Mplus fast Structural equation modeling. http://id.loc.gov/authorities/subjects/sh2005008800 Longitudinal method. http://id.loc.gov/authorities/subjects/sh85078296 Modèles d'équations structurales. Méthode longitudinale. Longitudinal method fast Structural equation modeling fast |
| topic_facet | Mplus. Mplus Structural equation modeling. Longitudinal method. Modèles d'équations structurales. Méthode longitudinale. Longitudinal method Structural equation modeling |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2473982 |
| work_keys_str_mv | AT geiserchristian longitudinalstructuralequationmodelingwithmplusalatentstatetraitperspective |