Latent variable path modeling with partial least squares:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Heidelberg
Physica-Verl.
1989
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | 283 S. graph. Darst. |
| ISBN: | 3790804371 0387913637 |
Internformat
MARC
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| 100 | 1 | |a Lohmöller, Jan-Bernd |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Latent variable path modeling with partial least squares |c Jan-Bernd Lohmöller |
| 264 | 1 | |a Heidelberg |b Physica-Verl. |c 1989 | |
| 300 | |a 283 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 502 | |a Zugl.: München, Hochsch. der Bundeswehr, Diss., 1983 | ||
| 650 | 7 | |a Modellen |2 gtt | |
| 650 | 7 | |a Statistiek |2 gtt | |
| 650 | 4 | |a Statistik | |
| 650 | 4 | |a Latent structure analysis | |
| 650 | 4 | |a Latent variables | |
| 650 | 4 | |a Least squares | |
| 650 | 4 | |a Path analysis (Statistics) | |
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents
Preface 5
Summary 6
Table of Contents 7
List of Tables 11
1 Basic Principles of Model Building 13
1.1 Empirical and Theoretical Goncepts 13
1.1.1 Levels of Theory and Data 13
1.2 Causation and Prediction 16
1.3 Data Modeling vs. Covariance Modeling 22
1.4 Notation 23
2 The Basic and the Extended PLS Method 27
2.1 Wold s Basic Method of Soft Modeling 28
2.1.1 Model Specification 28
2.1.2 The Basic PLS Algorithm 30
2.1.3 Extensions and Properties 31
2.2 The Extended Method: Specification and Properties 31
2.2.1 Formal Specification 31
2.2.2 Deductive Properties of the LVP Model 32
2.2.3 Inductive Properties of the LVP Model 33
2.2.4 Specification of an LVP Model 35
2.3 Estimation in the Extended Method 37
2.3.1 Partial Least Squares 37
2.3.2 LS Modules for PLS Algorithms 39
2.3.3 The Inner Weighting Modes 41
2.3.4 Patterned Orthogonalization of LVs 43
2.3.5 Weights and Loadings 46
2.3.6 Three Solutions for Conflicting Constraints 47
2.4 Assessment of Results 49
2.4.1 Information from the Model 49
2.4.2 Five Predictions, Five Residuais 50
2.4.3 Fit Indices 52
8 Table of Contents
2.4.4 Reliability Indices 53
2.4.5 Some Advice 55
2.5 Application: Home Environment and Intelligence 56
3 Foundations of Partial Least Squares 63
3.1 Conditional Expectation and Predictor Specification 63
3.1.1 The Notion of Conditional Expectation 64
3.1.2 Properties of the Conditional Expectation 67
3.1.3 The Linear Expectation 69
3.1.4 Predictor Specification 72
3.1.5 Estimation 73
3.1.6 Interlocking Conditional Expectation 75
3.1.7 Eigenvalue Problem and Power Method 77
3.1.8 Convergence of Power Algorithm 80
3.2 Principal and Other Components 81
3.2.1 Concepts and Notations 82
3.2.2 Aggregates, Composites 86
3.2.3 Factors 86
3.2.4 Components 88
3.2.5 Principal Components 91
3.2.6 The Principal Components Model 94
3.3 Factor Score Estimation 99
3.3.1 Properties of Factor Scores 99
3.3.2 Factor Estimation ModeB 102
3.3.3 Factor Estimation ModeB, Reestimated Loadings 104
3.3.4 Factor Estimation ModeA 106
3.3.5 Factor Estimation ModeA, Reestimated Loadings 108
3.3.6 Summary of Factor Estimation 109
3.4 Predictive Two Block Models 110
3.4.1 The Two Block Factor Model Ill
3.4.2 The Canonical Correlation Model 112
3.4.3 The Principal Predictor Model 117
3.4.4 The Interbattery Factor Model 121
3.4.5 Fortier s Simultaneous Linear Prediction Model 124
3.4.6 The MIMIC Model 125
3.4.7 Discussion 127
3.5 Split Principal Components 128
3.5.1 Hierarchical Component Model 128
3.5.2 Splitting Principal Components 133
3.5.3 Horst s Maximum Variance Algorithm 137
3.5.4 The Principle of Constant Proportionality 137
3.5.5 Principal Component, One Variable Omitted 139
3.5.6 Hierarchical Component Model, One Block Omitted .... 141
3.5.7 Applications of the Split PC Theorem 144
3.6 Split Multiple Regression 147
3.6.1 Split Multiple Regression and PLS Approach 147
Table of Contents 9
3.6.2 How Great is the Loss in R2? 149
3.6.3 Conclusions and Recommendations 152
3.7 Uncorrelated Dimensions in Generalized Canonical Correlation Anal¬
ysis 153
4 Mixed Measurement Level Multivariate Data 155
4.1 Categorical Variables and LS Methods 155
4.1.1 Super Contingency Tables 156
4.1.2 Canonical Analysis of Contingency Tables 162
4.1.3 Principal Components of Contingency Tables 165
4.1.4 Categorical Scaling 167
4.1.5 LV Path Analysis of Super Contingency Tables 169
4.2 Mixture of Categorical and Interval Scaled Variables 172
4.2.1 The Mixed Product Moment Matrix 172
4.2.2 One Categorical Predictor 173
4.2.3 Two Categorical Predictors 175
4.2.4 One Categorical Predictand 177
4.2.5 More Variables 180
4.3 Application: SES and Educational Aspiration 181
4.4 Different Slopes in Different Groups 185
4.4.1 MV Path Models with Product Variables 185
4.4.2 LV Path Models with Product Variables 189
4.4.3 Metric of Product Variables 190
4.5 Application: TV Consumption and Fear of Crime 193
4.6 Conclusion 197
5 Predictive vs. Structural Modeling: PLS vs. ML 199
5.1 Covariance vs. Data Structure Models 200
5.2 Scored and Unscored LVs 204
5.3 Consistency and Bias in a Two Block Model 209
5.4 The Interpretation of Consistency at Large 213
5.5 Some PLS LISREL Comparisons 216
5.6 The PLS Solution of the Identification Problem 222
5.6.1 Restriction for Scale Unambiguity (RSU) 222
5.6.2 Restrictions for Identifiability 225
5.6.3 Identifiability in PLS Model 225
6 Latent Variables Three Mode Path (LVP3) Analysis 227
6.1 Three Way Data Models 227
6.2 The Kronecker Principal Component (KPC) Model 228
6.3 The Three Mode LV Path (LVP3) Model 232
6.4 Special Cases and Properties 233
6.5 The PLS Estimation of LVP3 Models 235
6.6 Application: Longitudinal Data 236
6.7 Concluding Remarks 239
10 Table of Contents
7 PLS Programs and Applications 241
7.1 PLS Programs 241
7.2 Applications 242
7.2.1 Applications to Non Individual Data 242
7.2.2 Applications in Psychological and Educational Research . . 245
Bibliography 249
Author Index 273
Subject Index 277
|
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| author | Lohmöller, Jan-Bernd |
| author_facet | Lohmöller, Jan-Bernd |
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| dewey-full | 519.5/35 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/35 |
| dewey-search | 519.5/35 |
| dewey-sort | 3519.5 235 |
| dewey-tens | 510 - Mathematics |
| discipline | Soziologie Mathematik Wirtschaftswissenschaften |
| format | Thesis Book |
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| id | DE-604.BV001848180 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T15:36:33Z |
| institution | BVB |
| isbn | 3790804371 0387913637 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-001225226 |
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| owner_facet | DE-19 DE-BY-UBM DE-706 DE-11 DE-188 DE-N2 DE-83 |
| physical | 283 S. graph. Darst. |
| publishDate | 1989 |
| publishDateSearch | 1989 |
| publishDateSort | 1989 |
| publisher | Physica-Verl. |
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| spelling | Lohmöller, Jan-Bernd Verfasser aut Latent variable path modeling with partial least squares Jan-Bernd Lohmöller Heidelberg Physica-Verl. 1989 283 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Zugl.: München, Hochsch. der Bundeswehr, Diss., 1983 Modellen gtt Statistiek gtt Statistik Latent structure analysis Latent variables Least squares Path analysis (Statistics) Methode der partiellen kleinsten Quadrate (DE-588)4591652-4 gnd rswk-swf Latente Variable (DE-588)4166860-1 gnd rswk-swf Pfadanalyse (DE-588)4440597-2 gnd rswk-swf Strukturmodell (DE-588)4183810-5 gnd rswk-swf Pfadmodell (DE-588)4238807-7 gnd rswk-swf Varianzanalyse (DE-588)4187413-4 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Pfadmodell (DE-588)4238807-7 s Latente Variable (DE-588)4166860-1 s Methode der partiellen kleinsten Quadrate (DE-588)4591652-4 s DE-604 Pfadanalyse (DE-588)4440597-2 s Strukturmodell (DE-588)4183810-5 s Varianzanalyse (DE-588)4187413-4 s HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=001225226&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Lohmöller, Jan-Bernd Latent variable path modeling with partial least squares Modellen gtt Statistiek gtt Statistik Latent structure analysis Latent variables Least squares Path analysis (Statistics) Methode der partiellen kleinsten Quadrate (DE-588)4591652-4 gnd Latente Variable (DE-588)4166860-1 gnd Pfadanalyse (DE-588)4440597-2 gnd Strukturmodell (DE-588)4183810-5 gnd Pfadmodell (DE-588)4238807-7 gnd Varianzanalyse (DE-588)4187413-4 gnd |
| subject_GND | (DE-588)4591652-4 (DE-588)4166860-1 (DE-588)4440597-2 (DE-588)4183810-5 (DE-588)4238807-7 (DE-588)4187413-4 (DE-588)4113937-9 |
| title | Latent variable path modeling with partial least squares |
| title_auth | Latent variable path modeling with partial least squares |
| title_exact_search | Latent variable path modeling with partial least squares |
| title_full | Latent variable path modeling with partial least squares Jan-Bernd Lohmöller |
| title_fullStr | Latent variable path modeling with partial least squares Jan-Bernd Lohmöller |
| title_full_unstemmed | Latent variable path modeling with partial least squares Jan-Bernd Lohmöller |
| title_short | Latent variable path modeling with partial least squares |
| title_sort | latent variable path modeling with partial least squares |
| topic | Modellen gtt Statistiek gtt Statistik Latent structure analysis Latent variables Least squares Path analysis (Statistics) Methode der partiellen kleinsten Quadrate (DE-588)4591652-4 gnd Latente Variable (DE-588)4166860-1 gnd Pfadanalyse (DE-588)4440597-2 gnd Strukturmodell (DE-588)4183810-5 gnd Pfadmodell (DE-588)4238807-7 gnd Varianzanalyse (DE-588)4187413-4 gnd |
| topic_facet | Modellen Statistiek Statistik Latent structure analysis Latent variables Least squares Path analysis (Statistics) Methode der partiellen kleinsten Quadrate Latente Variable Pfadanalyse Strukturmodell Pfadmodell Varianzanalyse Hochschulschrift |
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