Discovering structural equation modeling using Stata:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
College Station, Tex.
Stata Press
2013
|
| Ausgabe: | Rev. ed. |
| Schriftenreihe: | A Stata Press publication
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis Inhaltsverzeichnis |
| Beschreibung: | Literaturverz. S. [297] - 298 |
| Beschreibung: | XXV, 306 S. graph. Darst. |
| ISBN: | 1597181390 9781597181396 |
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Datensatz im Suchindex
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| adam_text | Titel: Discovering structural equation modeling using Stata
Autor: Acock, Alan C
Jahr: 2013
Discovering Structural Equation
Modeling Using Stata
Revised Edition
ALAN C. ACOCK
Oregon State University
A Stata Press Publication
StataCorp LP
College Station, Texas
Contents
Dedication v
List of tables xiii
List of figures xv
List of boxes xix
Preface xxi
Acknowledgments xxvii
1 Introduction to confirmatory factor analysis 1
1.1 Introduction 1
1.2 The do not even think about it approach 2
1.3 The principal component factor analysis approach 3
1.4 Alpha reliability for our nine-item scale 7
1.5 Generating a factor score rather than a mean or summative score . . 9
1.6 What can CFA add? 11
1.7 Fitting a CFA model 14
1.8 Interpreting and presenting CFA results 17
1.9 Assessing goodness of fit 21
1.9.1 Modification indices 26
1.9.2 Final model and estimating scale reliability 29
1.10 A two-factor model 33
1.10.1 Evaluating the depression dimension 34
1.10.2 Estimating a two-factor model 37
1.11 Parceling 45
1.12 Extensions and what is next 45
1.13 Exercises 46
viii
Contents
1.A Using the SEM Builder to run a CPA 49
l.A.l Drawing the model 49
I.A.2 Estimating the model 53
2 Using structural equation modeling for path models 59
2.1 Introduction 59
2.2 Path model terminology 59
2.2.1 Exogenous predictor, endogenous outcome, and endoge
nous mediator variables 60
2.2.2 A hypothetical path model 61
2.3 A substantive example of a path model 65
2.4 Estimating a model with correlated residuals 72
2.4.1 Estimating direct, indirect, and total effects 73
2.4.2 Strengthening our path model and adding covariates .... 80
2.5 Auxiliary variables 83
2.6 Testing equality of coefficients 84
2.7 A cross-lagged panel design 86
2.8 Moderation 89
2.9 Nonrecursive models 95
2.9.1 Worked example of a nonrecursive model 97
2.9.2 Stability of a nonrecursive model 100
2.9.3 Model constraints 101
2.9.4 Equality constraints 103
2.10 Exercises 105
2.A Using the SEM Builder to run path models 108
3 Structural equation modeling 115
3.1 Introduction 115
3.2 The classic example of a structural equation model 115
3.2.1 Identification of a full structural equation model 117
3.2.2 Fitting a full structural equation model 119
3.2.3 Modifying our model 125
Contents ix
3.2.4 Indirect effects 128
3.3 Equality constraints 129
3.4 Programming constraints 131
3.5 Structural model with formative indicators 141
3.5.1 Identification and estimation of a composite latent variable 143
3.5.2 Multiple indicators, multiple causes model 147
3.6 Exercises 150
4 Latent growth curves 153
4.1 Discovering growth curves 153
4.2 A simple growth curve model 154
4.3 Identifying a growth curve model 157
4.3.1 An intuitive idea of identification 157
4.3.2 Identifying a quadratic growth curve 158
4.4 An example of a linear latent growth curve 159
4.4.1 A latent growth curve model for BMI 160
4.4.2 Graphic representation of individual trajectories (optional) . 161
4.4.3 Intraclass correlation (ICC) (optional) 165
4.4.4 Fitting a latent growth curve 167
4.4.5 Adding correlated adjacent error terms 180
4.4.6 Adding a quadratic latent slope growth factor 181
4.4.7 Adding a quadratic latent slope and correlating adjacent
error terms 181
4.5 How can we add time-invariant covariates to our model? 188
4.5.1 Interpreting a model with time-invariant covariates 192
4.6 Explaining the random effects--time-varying covariates 194
4.6.1 Fitting a model with time-invariant and time-varying co
variates 195
4.6.2 Interpreting a model with time-invariant and
time-varying covariates 202
4.7 Constraining variances of error terms to be equal (optional) 203
4.8 Exercises 207
Contents
5 Group comparisons 209
5.1 Interaction as a traditional approach to multiple-group comparisons 209
5.2 The range of applications of Stata s multiple-group
comparisons with sem 211
5.2.1 A multiple indicators, multiple causes model 211
5.2.2 A measurement model 212
5.2.3 A full structural equation model 212
5.3 A measurement model application 213
5.3.1 Step 1: Testing for invariance comparing women and men . 215
5.3.2 Step 2: Testing for invariant loadings 219
5.3.3 Step 3: Testing for an equal loadings and equal error-
variances model 224
5.3.4 Testing for equal intercepts 226
5.3.5 Comparison of models 227
5.3.6 Step 4: Comparison of means 230
5.3.7 Step 5: Comparison of variances and covariance of latent
variables 238
5.4 Multiple-group path analysis 240
5.4.1 What parameters are different? 245
5.4.2 Fitting the model with the SEM Builder 250
5.4.3 A standardized solution 251
5.4.4 Constructing tables for publications 252
5.5 Multiple-group comparisons of structural equation models 254
5.6 Exercises 263
6 Epilogue-what now? 265
6.1 What is next? 266
A The graphical user interface 269
A.l Introduction 269
A.2 Menus for Windows, Unix, and Mac 270
A.2.1 The menus, explained 270
A.2.2 The vertical drawing toolbar 271
Contents xi
A.3 Designing a structural equation model 272
A.4 Drawing an SEM model 276
A.5 Fitting a structural equation model 281
A.6 Postestimation commands 284
A.7 Clearing preferences and restoring the defaults 285
B Entering data from summary statistics 287
References 297
Author index 299
Subject index 301
Titel: Discovering structural equation modeling using Stata
Autor: Acock, Alan C
Jahr: 2013
Discovering Structural Equation
Modeling Using Stata
Revised Edition
ALAN C. ACOCK
Oregon State University
A Stata Press Publication
StataCorp LP
College Station, Texas
Contents
Dedication v
List of tables xiii
List of figures xv
List of boxes xix
Preface xxi
Acknowledgments xxvii
1 Introduction to confirmatory factor analysis 1
1.1 Introduction 1
1.2 The do not even think about it approach 2
1.3 The principal component factor analysis approach 3
1.4 Alpha reliability for our nine-item scale 7
1.5 Generating a factor score rather than a mean or summative score . . 9
1.6 What can CFA add? 11
1.7 Fitting a CFA model 14
1.8 Interpreting and presenting CFA results 17
1.9 Assessing goodness of fit 21
1.9.1 Modification indices 26
1.9.2 Final model and estimating scale reliability 29
1.10 A two-factor model 33
1.10.1 Evaluating the depression dimension 34
1.10.2 Estimating a two-factor model 37
1.11 Parceling 45
1.12 Extensions and what is next 45
1.13 Exercises 46
viii
Contents
1.A Using the SEM Builder to run a CPA 49
l.A.l Drawing the model 49
I.A.2 Estimating the model 53
2 Using structural equation modeling for path models 59
2.1 Introduction 59
2.2 Path model terminology 59
2.2.1 Exogenous predictor, endogenous outcome, and endoge
nous mediator variables 60
2.2.2 A hypothetical path model 61
2.3 A substantive example of a path model 65
2.4 Estimating a model with correlated residuals 72
2.4.1 Estimating direct, indirect, and total effects 73
2.4.2 Strengthening our path model and adding covariates .... 80
2.5 Auxiliary variables 83
2.6 Testing equality of coefficients 84
2.7 A cross-lagged panel design 86
2.8 Moderation 89
2.9 Nonrecursive models 95
2.9.1 Worked example of a nonrecursive model 97
2.9.2 Stability of a nonrecursive model 100
2.9.3 Model constraints 101
2.9.4 Equality constraints 103
2.10 Exercises 105
2.A Using the SEM Builder to run path models 108
3 Structural equation modeling 115
3.1 Introduction 115
3.2 The classic example of a structural equation model 115
3.2.1 Identification of a full structural equation model 117
3.2.2 Fitting a full structural equation model 119
3.2.3 Modifying our model 125
Contents ix
3.2.4 Indirect effects 128
3.3 Equality constraints 129
3.4 Programming constraints 131
3.5 Structural model with formative indicators 141
3.5.1 Identification and estimation of a composite latent variable 143
3.5.2 Multiple indicators, multiple causes model 147
3.6 Exercises 150
4 Latent growth curves 153
4.1 Discovering growth curves 153
4.2 A simple growth curve model 154
4.3 Identifying a growth curve model 157
4.3.1 An intuitive idea of identification 157
4.3.2 Identifying a quadratic growth curve 158
4.4 An example of a linear latent growth curve 159
4.4.1 A latent growth curve model for BMI 160
4.4.2 Graphic representation of individual trajectories (optional) . 161
4.4.3 Intraclass correlation (ICC) (optional) 165
4.4.4 Fitting a latent growth curve 167
4.4.5 Adding correlated adjacent error terms 180
4.4.6 Adding a quadratic latent slope growth factor 181
4.4.7 Adding a quadratic latent slope and correlating adjacent
error terms 181
4.5 How can we add time-invariant covariates to our model? 188
4.5.1 Interpreting a model with time-invariant covariates 192
4.6 Explaining the random effects--time-varying covariates 194
4.6.1 Fitting a model with time-invariant and time-varying co
variates 195
4.6.2 Interpreting a model with time-invariant and
time-varying covariates 202
4.7 Constraining variances of error terms to be equal (optional) 203
4.8 Exercises 207
Contents
5 Group comparisons 209
5.1 Interaction as a traditional approach to multiple-group comparisons 209
5.2 The range of applications of Stata s multiple-group
comparisons with sem 211
5.2.1 A multiple indicators, multiple causes model 211
5.2.2 A measurement model 212
5.2.3 A full structural equation model 212
5.3 A measurement model application 213
5.3.1 Step 1: Testing for invariance comparing women and men . 215
5.3.2 Step 2: Testing for invariant loadings 219
5.3.3 Step 3: Testing for an equal loadings and equal error-
variances model 224
5.3.4 Testing for equal intercepts 226
5.3.5 Comparison of models 227
5.3.6 Step 4: Comparison of means 230
5.3.7 Step 5: Comparison of variances and covariance of latent
variables 238
5.4 Multiple-group path analysis 240
5.4.1 What parameters are different? 245
5.4.2 Fitting the model with the SEM Builder 250
5.4.3 A standardized solution 251
5.4.4 Constructing tables for publications 252
5.5 Multiple-group comparisons of structural equation models 254
5.6 Exercises 263
6 Epilogue-what now? 265
6.1 What is next? 266
A The graphical user interface 269
A.l Introduction 269
A.2 Menus for Windows, Unix, and Mac 270
A.2.1 The menus, explained 270
A.2.2 The vertical drawing toolbar 271
Contents xi
A.3 Designing a structural equation model 272
A.4 Drawing an SEM model 276
A.5 Fitting a structural equation model 281
A.6 Postestimation commands 284
A.7 Clearing preferences and restoring the defaults 285
B Entering data from summary statistics 287
References 297
Author index 299
Subject index 301
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| language | English |
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| series2 | A Stata Press publication |
| spelling | Acock, Alan C. 1944- Verfasser (DE-588)1037993705 aut Discovering structural equation modeling using Stata Alan C. Acock Rev. ed. College Station, Tex. Stata Press 2013 XXV, 306 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier A Stata Press publication Literaturverz. S. [297] - 298 Stata Structural equation modeling Statistics--Computer programs Strukturgleichungsmodell (DE-588)4252999-2 gnd rswk-swf Stata (DE-588)4617285-3 gnd rswk-swf Models, Structural Statistics as Topic Software Strukturgleichungsmodell (DE-588)4252999-2 s Stata (DE-588)4617285-3 s DE-604 DE-601 pdf/application http://www.gbv.de/dms/zbw/769116183.pdf Inhaltsverzeichnis HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027549639&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027549639&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Acock, Alan C. 1944- Discovering structural equation modeling using Stata Stata Structural equation modeling Statistics--Computer programs Strukturgleichungsmodell (DE-588)4252999-2 gnd Stata (DE-588)4617285-3 gnd |
| subject_GND | (DE-588)4252999-2 (DE-588)4617285-3 |
| title | Discovering structural equation modeling using Stata |
| title_auth | Discovering structural equation modeling using Stata |
| title_exact_search | Discovering structural equation modeling using Stata |
| title_full | Discovering structural equation modeling using Stata Alan C. Acock |
| title_fullStr | Discovering structural equation modeling using Stata Alan C. Acock |
| title_full_unstemmed | Discovering structural equation modeling using Stata Alan C. Acock |
| title_short | Discovering structural equation modeling using Stata |
| title_sort | discovering structural equation modeling using stata |
| topic | Stata Structural equation modeling Statistics--Computer programs Strukturgleichungsmodell (DE-588)4252999-2 gnd Stata (DE-588)4617285-3 gnd |
| topic_facet | Stata Structural equation modeling Statistics--Computer programs Strukturgleichungsmodell |
| url | http://www.gbv.de/dms/zbw/769116183.pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027549639&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027549639&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT acockalanc discoveringstructuralequationmodelingusingstata |
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