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 reliability...
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New York ; London
The Guilford Press
[2021]
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| Series: | Methodology in the social sciences
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Summary: | 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. |
| Physical Description: | xxiii, 344 Seiten Diagramme 23,5 cm |
| ISBN: | 9781462538782 9781462544240 |
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| 520 | 3 | |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. | |
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Record in the Search Index
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| adam_text | Extended Contents List of Abbreviations xix Guide to Statistical Symbols xxi 1 · A Measurement Theoretical Framework for Longitudinal Data: Introduction to Latent State-Trait Theory 1 1.1 Introduction /1 1.2 Latent State-Trait Theory/3 1.2.1 1.2.2 1.2.3 1.2.4 Introduction / 3 Basic Idea/3 Random Experiment / 5 Variables in LST-R Theory / 5 BOX 1.1. Key Concepts and Definitions in CTT / 7 1.2.5 Properties /10 1.2.6 Coefficients 111 BOX 1.2. Properties of the Latent Variables in LST-R Theory / 12 1.3 Chapter Summary / 14 1.4 Recommended Readings/ 15 2 · Single-Factor Longitudinal Models for Single-Indicator Data 1 ó 2.1 Introduction/ 16 2.2 The Random Intercept Model /17 2.2.1 Introduction /17 2.2.2 Model Description /17 XIII
xiv Extended Contents BOX 2.1. Available Information, Model Degrees of Freedom, and Model Identification in Single-Indicator Longitudinal Designs / 19 BOX 2.2. Defining the Random Intercept Model Based on LST-R Theory / 20 2.2.3 Variance Decomposition and Reliability Coefficient / 21 2.2.4 Mplus Application / 22 BOX 2.3. Model Fit Assessment and Model Comparisons / 24 2.2.5 Summary/26 2.3 The Random and Fixed Intercepts Model / 28 2.3.1 Introduction / 28 2.3.2 Model Description / 28 BOX 2.4. Means of Linear Combinations / 28 BOX 2.5. Defining the Random and Fixed Intercepts Model Based on LST-R Theory / 31 2.3.3 Variance Decomposition and Reliability Coefficient / 32 2.3.4 Mplus Application / 32 2.3.5 Summary/34 2.4 The ^-Congeneric Model / 34 2.4.1 Introduction / 34 2.4.2 Model Description / 35 BOX 2.6. Defining the /-Congeneric Model Based on LST Theory / 37 2.4.3 Variance Decomposition and Reliability Coefficient / 38 2.4.4 Mplus Application / 38 BOX 2.7. The MODEL CONSTRAINT and MODEL TEST Options in Mplus / 40 2.4.5 Summary / 43 2.5 Chapter Summary / 43 2.6 Recommended Reading / 44 Note / 44 3 · Multifactor Longitudinal Models for Single-Indicator Data 3.1 Introduction / 45 3.2 The Simplex Model / 45 3.2.1 Introduction / 45 3.2.2 Model Description / 46 BOX 3.1. Defining the Simplex Model Based on LST-R Theory / 48 BOX 3.2. Should a Researcher Constrain State Residual or Measurement Error Variances in the Simplex Model? / 51 3.2.3 Variance Decomposition and Coefficients /51 3.2.4 Assessing Stability and Change in the Simplex Model/53 BOX 3.3. Endogenous versus Exogenous
Variables in Structural Equation Models and Mplus / 54 3.2.5 Mplus Application / 56 BOX 3.4. Specifying the Simplex Model with Equal State Residual Factor Variances / 57 BOX 3.5. Direct versus Indirect (Mediated) Variable Effects in the Simplex Model /61 3.2.6 Summary/62 45
Extended Contents xv 3.3 The Latent Change Score Model / 62 3.3.1 Introduction / 62 3.3.2 Model Description / 63 3.3.3 Variance Decomposition and Coefficients /64 3.3.4 Mplus Application / 65 3.3.5 Summary / 68 3.4 The Trait-State-Error Model / 69 3.4.1 Introduction / 69 3.4.2 Model Description / 69 BOX 3.6. Defining the TSE Model Based on LST-R Theory / 72 3.4.3 Variance Decomposition and Coefficients / 75 BOX 3.7. The Mean Structure in the TSE Model / 76 3.4.4 Mplus Application / 79 BOX 3.8. Estimation Problems and Bias in the TSE Model / 83 3.4.5 Computing the Con(rt), TCon(rt), SCon(rt), and Osp(rt) Coefficients in Mplus / 85 3.4.6 Summary/86 3.5 Latent Growth Curve Models / 88 3.5.1 Introduction / 88 3.5.2 The Linear LGC Model/88 BOX 3.9. Defining the Linear LGC Model Based on LST-R Theory / 91 3.5.3 The LGC Model with Unspecified Growth Pattern / 97 BOX 3.10. Defining the LGC Model with Unspecified Growth Pattern Using the Concepts of LST-R Theory / 99 3.6 Chapter Summary / 105 BOX 3.11. Using Ordered Categorical Observed Variables as Indicators / 109 3.7 Recommended Readings / 110 Notes/110 4 · Latent State Models and Measurement Equivalence Testing in Longitudinal Studies 4.1 Introduction/113 4.2 The Latent State Model /114 4.2.1 Introduction /114 4.2.2 Model Description /114 4.2.3 Scale Setting /116 BOX 4.1. Relationships Between the LS and CTT Models /116 BOX 4.2. Available Information and Model Degrees of Freedom in Multiple-Indicator Longitudinal Designs /117 BOX 4.3. Alternative Methods of Defining the Scale of Latent State Variables / 120 4.2.4 Model
Definition Based on LST-R Theory /120 4.2.5 Variance Decomposition and Reliability Coefficient /120 BOX 4.4. Defining the LS Model Based on LST-R Theory /121 4.2.6 Testing ME across Time /122 BOX 4.5. Levels of ME According to Widaman and Reise (1997) /123 BOX 4.6. Nested Models and Chi-Square Difference Testing /124 4.2.7 Other Features of the LS Model /125 4.2.8 Mplus Application /126 113
xvi Extended Contents BOX 4.7. Different Ways to Specify and Analyze the Mean Structure in the LS Model / 128 4.2.9 Summary /134 4.3 The LS Model with Indicator-Specific Residual Factors / 136 4.3.1 Introduction /136 4.3.2 Model Description /136 BOX 4.8. Indicator-Specific Effects in Longitudinal Data / 137 BOX 4.9. Defining the LS-IS Model Based on LST-R Theory / 140 4.3.3 Variance Decomposition and Coefficients /142 BOX 4.10. Correlated Errors: An Alternative Way to Model Indicator Specificity in Longitudinal Data / 144 4.3.4 Mplus Application /145 BOX 4.11. Providing User-Defined Starting Values in Mplus / 147 4.3.5 Summary /151 4.4 Chapter Summary / 151 4.5 Recommended Readings / 153 Notes / 153 5 · Multiple-Indicator Longitudinal Models 5.1 Introduction/155 5.2 Latent State Change Models / 156 5.2.1 Introduction /156 5.2.2 Model Description /157 5.2.3 Variance Decomposition and Coefficients /159 5.2.4 Mplus Application /160 5.2.5 Summary/160 5.3 The Latent Autoregressive/Cross-Lagged States Model / 161 5.3.1 Introduction /161 5.3.2 Model Description /162 BOX 5.1. Defining the LAS Model Based on LST-R Theory / 164 5.3.3 Variance Decomposition and Coefficients /164 5.3.4 Other Features of the Model /165 5.3.5 Multiconstruct Extension /165 5.3.6 Mplus Application /167 5.3.7 Summary /170 5.4 Latent State-Trait Models /171 5.4.1 Introduction /171 5.4.2 The Singletrait-Multistate Model /172 BOX 5.2. Defining the STMS Model Based on LST-R Theory / 175 BOX 5.3. Some Guidelines for the Interpretation of the Con, Osp, and Rei Coefficients / 177 BOX 5.4. Specifying an STMS Model
as a Bifactor Model / 179 BOX 5.5. STMS Models without versus with Autoregressive Effects / 1 85 5.4.3 The STMS Model with Indicator-Specific Residual Factors /186 BOX 5.6. Defining the STMSIS Model Based on LST-R Theory / 188 5.4.4 The Multitrait-Multistate Model /192 BOX 5.7. Defining the MTMS Model Based on LST-R Theory / 194 155
Extended Contente xvü The MTMS Model with Autoregression / 200 5.5 Latent Trait-Change Models / 202 5.5.1 Introduction / 202 5.5.2 The LST Trait-Change Model / 204 BOX 5.9. Defining the LST-TC Model Based on LST-R Theory / 207 BOX 5.10. Alternative Equivalent Ways to Specify the LST-TC Model / 209 5.5.3 Multiple-Indicator Latent Growth Curve Models /212 BOX 5.11. Defining the Linear ISG Model Based on LST-R Theory /216 5.6 Chapter Summary / 223 5.6.1 Advantages of Multiple-Indicator Models / 223 5.6.2 Limitations of Multiple-Indicator Models / 228 5.7 Recommended Readings / 229 Notes /230 BOX 5.8. 6 · Modeling Intensive Longitudinal Data 231 6.1 Introduction/231 6.2 Special Features of Intensive Longitudinal Data / 232 6.2.1 Introduction / 232 6.2.2 Wide- versus Long-Format Data / 232 6.2.3 Imbalanced Time Points / 234 6.2.4 Autoregressive Effects / 235 6.3 Specifying Longitudinal SEMs for Intensive Longitudinal Data / 235 6.3.1 Introduction / 235 6.3.2 The Random Intercept Model as a Multilevel Model / 235 BOX 6.1. Wide-to-Long Data Transformation of Data in Mplus / 238 6.3.3 The Linear Growth Model as a Multilevel Model / 243 6.3.4 The Multitrait-Multistate Model as a Multilevel Model /247 6.3.5 The Indicator-Specific Growth Model as a Multilevel Model / 252 6.3.6 Modeling Autoregressive Effects Using DSEM / 257 6.4 Chapter Summary/276 6.5 Recommended Readings / 277 7 · Missing Data Handling 7.1 Introduction/279 7.2 Missing Data Mechanisms / 280 7.2.1 Missing Completely at Random / 281 7.2.2 Missing at Random / 282 7.2.3 Missing Not at Random / 283 7.3 ML Missing Data
Handling / 285 7.3.1 Introduction / 285 7.3.2 ML Missing Data Analysis in Mplus / 286 7.3.3 Summary / 290 7.4 Multiple Imputation / 290 7.4.1 Introduction / 290 7.4.2 Ml in Mplus/291 7.4.3 Summary / 296 279
xviii Extended Contents 7.5 Planned Missing Data Designs / 296 7.5.1 Introduction / 296 7.5.2 Analysis of Planned Missing Data and Simulations in Mplus/297 7.6 Chapter Summary / 303 7.7 Recommended Readings / 305 Note/306 8 · How to Choose between Models and Report the Results 307 8.1 Model Selection / 307 8.2 Reporting Results /310 8.2.1 General Recommendations / 310 8.2.2 Methods Section / 311 8.2.3 Results Section / 315 8.3. Chapter Summary / 320 8.4 Recommended Readings / 320 Notes/321 References 323 Author Index 329 Subject Index 332 About the Author 344 The companion website www.guHford.com/geiser2-materials features datasets, annotated syntax files, output for all of the examples, as well as color versions of Figures 6.6A, 6.6B, and 6.8.
|
| adam_txt |
Extended Contents List of Abbreviations xix Guide to Statistical Symbols xxi 1 · A Measurement Theoretical Framework for Longitudinal Data: Introduction to Latent State-Trait Theory 1 1.1 Introduction /1 1.2 Latent State-Trait Theory/3 1.2.1 1.2.2 1.2.3 1.2.4 Introduction / 3 Basic Idea/3 Random Experiment / 5 Variables in LST-R Theory / 5 BOX 1.1. Key Concepts and Definitions in CTT / 7 1.2.5 Properties /10 1.2.6 Coefficients 111 BOX 1.2. Properties of the Latent Variables in LST-R Theory / 12 1.3 Chapter Summary / 14 1.4 Recommended Readings/ 15 2 · Single-Factor Longitudinal Models for Single-Indicator Data 1 ó 2.1 Introduction/ 16 2.2 The Random Intercept Model /17 2.2.1 Introduction /17 2.2.2 Model Description /17 XIII
xiv Extended Contents BOX 2.1. Available Information, Model Degrees of Freedom, and Model Identification in Single-Indicator Longitudinal Designs / 19 BOX 2.2. Defining the Random Intercept Model Based on LST-R Theory / 20 2.2.3 Variance Decomposition and Reliability Coefficient / 21 2.2.4 Mplus Application / 22 BOX 2.3. Model Fit Assessment and Model Comparisons / 24 2.2.5 Summary/26 2.3 The Random and Fixed Intercepts Model / 28 2.3.1 Introduction / 28 2.3.2 Model Description / 28 BOX 2.4. Means of Linear Combinations / 28 BOX 2.5. Defining the Random and Fixed Intercepts Model Based on LST-R Theory / 31 2.3.3 Variance Decomposition and Reliability Coefficient / 32 2.3.4 Mplus Application / 32 2.3.5 Summary/34 2.4 The ^-Congeneric Model / 34 2.4.1 Introduction / 34 2.4.2 Model Description / 35 BOX 2.6. Defining the /-Congeneric Model Based on LST Theory / 37 2.4.3 Variance Decomposition and Reliability Coefficient / 38 2.4.4 Mplus Application / 38 BOX 2.7. The MODEL CONSTRAINT and MODEL TEST Options in Mplus / 40 2.4.5 Summary / 43 2.5 Chapter Summary / 43 2.6 Recommended Reading / 44 Note / 44 3 · Multifactor Longitudinal Models for Single-Indicator Data 3.1 Introduction / 45 3.2 The Simplex Model / 45 3.2.1 Introduction / 45 3.2.2 Model Description / 46 BOX 3.1. Defining the Simplex Model Based on LST-R Theory / 48 BOX 3.2. Should a Researcher Constrain State Residual or Measurement Error Variances in the Simplex Model? / 51 3.2.3 Variance Decomposition and Coefficients /51 3.2.4 Assessing Stability and Change in the Simplex Model/53 BOX 3.3. Endogenous versus Exogenous
Variables in Structural Equation Models and Mplus / 54 3.2.5 Mplus Application / 56 BOX 3.4. Specifying the Simplex Model with Equal State Residual Factor Variances / 57 BOX 3.5. Direct versus Indirect (Mediated) Variable Effects in the Simplex Model /61 3.2.6 Summary/62 45
Extended Contents xv 3.3 The Latent Change Score Model / 62 3.3.1 Introduction / 62 3.3.2 Model Description / 63 3.3.3 Variance Decomposition and Coefficients /64 3.3.4 Mplus Application / 65 3.3.5 Summary / 68 3.4 The Trait-State-Error Model / 69 3.4.1 Introduction / 69 3.4.2 Model Description / 69 BOX 3.6. Defining the TSE Model Based on LST-R Theory / 72 3.4.3 Variance Decomposition and Coefficients / 75 BOX 3.7. The Mean Structure in the TSE Model / 76 3.4.4 Mplus Application / 79 BOX 3.8. Estimation Problems and Bias in the TSE Model / 83 3.4.5 Computing the Con(rt), TCon(rt), SCon(rt), and Osp(rt) Coefficients in Mplus / 85 3.4.6 Summary/86 3.5 Latent Growth Curve Models / 88 3.5.1 Introduction / 88 3.5.2 The Linear LGC Model/88 BOX 3.9. Defining the Linear LGC Model Based on LST-R Theory / 91 3.5.3 The LGC Model with Unspecified Growth Pattern / 97 BOX 3.10. Defining the LGC Model with Unspecified Growth Pattern Using the Concepts of LST-R Theory / 99 3.6 Chapter Summary / 105 BOX 3.11. Using Ordered Categorical Observed Variables as Indicators / 109 3.7 Recommended Readings / 110 Notes/110 4 · Latent State Models and Measurement Equivalence Testing in Longitudinal Studies 4.1 Introduction/113 4.2 The Latent State Model /114 4.2.1 Introduction /114 4.2.2 Model Description /114 4.2.3 Scale Setting /116 BOX 4.1. Relationships Between the LS and CTT Models /116 BOX 4.2. Available Information and Model Degrees of Freedom in Multiple-Indicator Longitudinal Designs /117 BOX 4.3. Alternative Methods of Defining the Scale of Latent State Variables / 120 4.2.4 Model
Definition Based on LST-R Theory /120 4.2.5 Variance Decomposition and Reliability Coefficient /120 BOX 4.4. Defining the LS Model Based on LST-R Theory /121 4.2.6 Testing ME across Time /122 BOX 4.5. Levels of ME According to Widaman and Reise (1997) /123 BOX 4.6. Nested Models and Chi-Square Difference Testing /124 4.2.7 Other Features of the LS Model /125 4.2.8 Mplus Application /126 113
xvi Extended Contents BOX 4.7. Different Ways to Specify and Analyze the Mean Structure in the LS Model / 128 4.2.9 Summary /134 4.3 The LS Model with Indicator-Specific Residual Factors / 136 4.3.1 Introduction /136 4.3.2 Model Description /136 BOX 4.8. Indicator-Specific Effects in Longitudinal Data / 137 BOX 4.9. Defining the LS-IS Model Based on LST-R Theory / 140 4.3.3 Variance Decomposition and Coefficients /142 BOX 4.10. Correlated Errors: An Alternative Way to Model Indicator Specificity in Longitudinal Data / 144 4.3.4 Mplus Application /145 BOX 4.11. Providing User-Defined Starting Values in Mplus / 147 4.3.5 Summary /151 4.4 Chapter Summary / 151 4.5 Recommended Readings / 153 Notes / 153 5 · Multiple-Indicator Longitudinal Models 5.1 Introduction/155 5.2 Latent State Change Models / 156 5.2.1 Introduction /156 5.2.2 Model Description /157 5.2.3 Variance Decomposition and Coefficients /159 5.2.4 Mplus Application /160 5.2.5 Summary/160 5.3 The Latent Autoregressive/Cross-Lagged States Model / 161 5.3.1 Introduction /161 5.3.2 Model Description /162 BOX 5.1. Defining the LAS Model Based on LST-R Theory / 164 5.3.3 Variance Decomposition and Coefficients /164 5.3.4 Other Features of the Model /165 5.3.5 Multiconstruct Extension /165 5.3.6 Mplus Application /167 5.3.7 Summary /170 5.4 Latent State-Trait Models /171 5.4.1 Introduction /171 5.4.2 The Singletrait-Multistate Model /172 BOX 5.2. Defining the STMS Model Based on LST-R Theory / 175 BOX 5.3. Some Guidelines for the Interpretation of the Con, Osp, and Rei Coefficients / 177 BOX 5.4. Specifying an STMS Model
as a Bifactor Model / 179 BOX 5.5. STMS Models without versus with Autoregressive Effects / 1 85 5.4.3 The STMS Model with Indicator-Specific Residual Factors /186 BOX 5.6. Defining the STMSIS Model Based on LST-R Theory / 188 5.4.4 The Multitrait-Multistate Model /192 BOX 5.7. Defining the MTMS Model Based on LST-R Theory / 194 155
Extended Contente xvü The MTMS Model with Autoregression / 200 5.5 Latent Trait-Change Models / 202 5.5.1 Introduction / 202 5.5.2 The LST Trait-Change Model / 204 BOX 5.9. Defining the LST-TC Model Based on LST-R Theory / 207 BOX 5.10. Alternative Equivalent Ways to Specify the LST-TC Model / 209 5.5.3 Multiple-Indicator Latent Growth Curve Models /212 BOX 5.11. Defining the Linear ISG Model Based on LST-R Theory /216 5.6 Chapter Summary / 223 5.6.1 Advantages of Multiple-Indicator Models / 223 5.6.2 Limitations of Multiple-Indicator Models / 228 5.7 Recommended Readings / 229 Notes /230 BOX 5.8. 6 · Modeling Intensive Longitudinal Data 231 6.1 Introduction/231 6.2 Special Features of Intensive Longitudinal Data / 232 6.2.1 Introduction / 232 6.2.2 Wide- versus Long-Format Data / 232 6.2.3 Imbalanced Time Points / 234 6.2.4 Autoregressive Effects / 235 6.3 Specifying Longitudinal SEMs for Intensive Longitudinal Data / 235 6.3.1 Introduction / 235 6.3.2 The Random Intercept Model as a Multilevel Model / 235 BOX 6.1. Wide-to-Long Data Transformation of Data in Mplus / 238 6.3.3 The Linear Growth Model as a Multilevel Model / 243 6.3.4 The Multitrait-Multistate Model as a Multilevel Model /247 6.3.5 The Indicator-Specific Growth Model as a Multilevel Model / 252 6.3.6 Modeling Autoregressive Effects Using DSEM / 257 6.4 Chapter Summary/276 6.5 Recommended Readings / 277 7 · Missing Data Handling 7.1 Introduction/279 7.2 Missing Data Mechanisms / 280 7.2.1 Missing Completely at Random / 281 7.2.2 Missing at Random / 282 7.2.3 Missing Not at Random / 283 7.3 ML Missing Data
Handling / 285 7.3.1 Introduction / 285 7.3.2 ML Missing Data Analysis in Mplus / 286 7.3.3 Summary / 290 7.4 Multiple Imputation / 290 7.4.1 Introduction / 290 7.4.2 Ml in Mplus/291 7.4.3 Summary / 296 279
xviii Extended Contents 7.5 Planned Missing Data Designs / 296 7.5.1 Introduction / 296 7.5.2 Analysis of Planned Missing Data and Simulations in Mplus/297 7.6 Chapter Summary / 303 7.7 Recommended Readings / 305 Note/306 8 · How to Choose between Models and Report the Results 307 8.1 Model Selection / 307 8.2 Reporting Results /310 8.2.1 General Recommendations / 310 8.2.2 Methods Section / 311 8.2.3 Results Section / 315 8.3. Chapter Summary / 320 8.4 Recommended Readings / 320 Notes/321 References 323 Author Index 329 Subject Index 332 About the Author 344 The companion website www.guHford.com/geiser2-materials features datasets, annotated syntax files, output for all of the examples, as well as color versions of Figures 6.6A, 6.6B, and 6.8. |
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| discipline_str_mv | Psychologie |
| format | Book |
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| id | DE-604.BV047262785 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T17:11:44Z |
| indexdate | 2024-07-10T09:07:09Z |
| institution | BVB |
| isbn | 9781462538782 9781462544240 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032666611 |
| oclc_num | 1227087652 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-11 DE-188 DE-824 |
| owner_facet | DE-473 DE-BY-UBG DE-11 DE-188 DE-824 |
| physical | xxiii, 344 Seiten Diagramme 23,5 cm |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | The Guilford Press |
| record_format | marc |
| series2 | Methodology in the social sciences |
| spelling | Geiser, Christian 1978- Verfasser (DE-588)13650082X aut Longitudinal structural equation modeling with Mplus a latent state-trait perspective Christian Geiser New York ; London The Guilford Press [2021] © 2021 xxiii, 344 Seiten Diagramme 23,5 cm txt rdacontent n rdamedia nc rdacarrier Methodology in the social sciences 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. Strukturgleichungsmodell (DE-588)4252999-2 gnd rswk-swf Mplus (DE-588)7716737-5 gnd rswk-swf Strukturgleichungsmodell (DE-588)4252999-2 s Mplus (DE-588)7716737-5 s DE-604 Erscheint auch als Online-Ausgabe 978-1-4625-4426-4 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032666611&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Geiser, Christian 1978- Longitudinal structural equation modeling with Mplus a latent state-trait perspective Strukturgleichungsmodell (DE-588)4252999-2 gnd Mplus (DE-588)7716737-5 gnd |
| subject_GND | (DE-588)4252999-2 (DE-588)7716737-5 |
| 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_exact_search_txtP | 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 | Strukturgleichungsmodell (DE-588)4252999-2 gnd Mplus (DE-588)7716737-5 gnd |
| topic_facet | Strukturgleichungsmodell Mplus |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032666611&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT geiserchristian longitudinalstructuralequationmodelingwithmplusalatentstatetraitperspective |