Introducing multilevel modeling:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London [u.a.]
Sage
2006
|
| Ausgabe: | Reprint. |
| Schriftenreihe: | ISM : introducing statistical methods
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | X, 148 S. graph. Darst. |
| ISBN: | 0761951407 0761951415 9780761951407 9780761951414 |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1817681155419275264 |
|---|---|
| adam_text |
Titel: Introducing multilevel modeling
Autor: Kreft, Ita
Jahr: 2006
CONTENTS
Preface ix
1 INTRODUCTION 1
1.1 Introduction 1
1.1.1 Hierarchies, micro and macro levels 1
1.1.2 Multilevel models 2
1.2 Examples 3
1.2.1 Income of workers in industry 3
1.2.2 Drug prevention research 4
1.2.3 School effectiveness research 4
1.2.4 Clinical therapy 5
1.2.5 Growth curve analysis 6
1.2.6 Geographical information systems 7
1.2.7 Meta-analysis 7
1.2.8 Twin and family studies 7
1.3 Summarizing discussion and definitions 8
1.3.1 Contextual models 8
1.3.2 Intra-class correlation 9
1.3.3 Fixed versus random coefficients 10
1.3.4 Cross-level interactions 12
1.3.5 Prediction 13
1.3.6 Shrinkage estimation and borrowing strength 14
1.4 Brief history 15
1.4.1 Variance components 15
1.4.2 Random coefficients 15
1.4.3 Variable coefficients 16
1.4.4 Changing coefficients 16
1.4.5 Panel data 16
1.4.6 Growth curves and repeated measurements 16
1.4.7 Bayesian linear models and empirical Bayes estimation 17
1.4.8 Moderator variables 17
1.4.9 Slopes as outcomes 17
1.5 Further reading 18
1.6 Software 18
1.6.1 HLM 19
1.6.2 VARCL 19
1.6.3 BMDP5-V 20
vi CONTENTS
1.6.4 Mln 20
1.6.5 PROC MIXED 20
1.6.6 MIXOR and MIXREG 20
1.7 Summary 21
Note 21
2 OVERVIEW OF CONTEXTUAL MODELS 22
2.1 Introduction 22
2.2 Models 22
2.3 Data 23
2.4 Decomposition of variation 25
2.5 Total or pooled regression 26
2.6 Aggregate regression 27
2.7 The contextual model 28
2.8 The Cronbach model 29
2.9 Analysis of covariance 30
2.10 MLn analysis of contextual models 32
2.11 Summary 34
Notes 34
3 VARYING AND RANDOM COEFFICIENT MODELS 35
3.1 Introduction 35
3.2 Separate regressions 35
3.3 Varying coefficients or 'slopes as outcomes' 36
3.4 The random coefficient model 39
3.5 Assumptions of linear models 44
3.6 'Slopes-as-outcoraes' analysis 45
3.7 Random coefficient results 47
3.7.1 Adding a macro-level explanatory variable 48
3.7.2 Posterior means 51
3.8 ANCOVA as alternative 53
3.9 The number of parameters 54
3.10 Summary 55
Notes 56
CONTENTS vii
4 ANALYSES 57
4.1 Introduction 57
4.1.1 Data description 58
4.1.2 The organization of the four sessions 61
4.2 Session 1 63
4.2.1 Notation for models 63
4.2.2 The null model 63
4.2.3 'HomeWork' and 'MathAchievement' 65
4.2.4 Random slope for 'HomeWork' 66
4.2.5 Adding 'ParentEducation' 69
4.2.6 Traditional regression analysis 70
4.3 Session 2 71
4.3.1 Introduction 71
4.3.2 A model with 'SchoolSize' 73
4.3.3 Changing'SchoolSize'to'Public' 74
4.3.4 Adding a cross-level interaction with 'Public' 75
4.3.5 Analyses with NELS-88 79
4.3.6 Deleting 'HomePublic' and adding 'White' using the small
data set again 80
4.3.7 Adding a random part for 'White' 82
4.3.8 Making the coefficient of 'White' fixed and adding 'MeanSES' 84
4.3.9 Deleting the school characteristic 'Public' 85
4.3.10 Adding an interaction between 'HomeWork' and 'MeanSES' 87
4.3.11 Adding another student-level variable 88
4.3.12 Analyses with NELS-88 89
4.4 Session 3 90
4.4.1 'SES' as a student4evel explanatory variable 90
4.4.2 Adding a random slope 91
4.4.3 Adding 'PercentMinorities' 93
4.4.4 Adding'MeanSES' 94
4.4.5 Analyses with NELS-88, models 2 and 3 96
4.5 Session 4 98
4.5.1 Analysis with class size and a cross-level interaction 98
4.5.2 Interaction between 'Ratio' and 'HomeWork' 99
4.5.3 Repealing the modeling session with NELS-88 101
4.6 Discussion 102
Notes 103
5 FREQUENTLY ASKED QUESTIONS 105
5.1 Introduction 105
5.2 The effects of centering 106
5.2.1 Centering in fixed effects regression models 106
5.2.2 Centering in multilevel models 107
5.3.2 5.3.3 5.3.4 Using the null model as a way Using total between variance Conclusions to calculate R'
5.4 Power
5.4.1 5.4.2 5.4.3 Some simple cases Review of simulation studies Conclusions
viii CONTENTS
5.2.3 Grand mean centering 108
5.2.4 Group mean centering 109
5.2.5 An example 110
5.2.6 Cross-level interactions with 'Public' and 'SES' 112
5.3 Modeled variance 115
5.3.1 Random intercept models 116
R2 117
118
119
119
121
124
126
5.5 To be or not to be random 126
5.5.1 ANCOVA versus RANCOVA versus simple regression 127
5.5.2 Fixed versus random slopes 129
5.6 Estimation techniques and algorithms 130
5.6.1 Which is best, FIML or REML? 133
5.6.2 The effect of estimation methods on the fixed coefficients 133
5.6.3 Estimation methods for variance components 134
5.6.4 Conclusions 135
5.7 Multicollinearity 135
Notes 137
Appendix Coding of NELS-88 Data 139
References 142
Index 147 |
| adam_txt |
Titel: Introducing multilevel modeling
Autor: Kreft, Ita
Jahr: 2006
CONTENTS
Preface ix
1 INTRODUCTION 1
1.1 Introduction 1
1.1.1 Hierarchies, micro and macro levels 1
1.1.2 Multilevel models 2
1.2 Examples 3
1.2.1 Income of workers in industry 3
1.2.2 Drug prevention research 4
1.2.3 School effectiveness research 4
1.2.4 Clinical therapy 5
1.2.5 Growth curve analysis 6
1.2.6 Geographical information systems 7
1.2.7 Meta-analysis 7
1.2.8 Twin and family studies 7
1.3 Summarizing discussion and definitions 8
1.3.1 Contextual models 8
1.3.2 Intra-class correlation 9
1.3.3 Fixed versus random coefficients 10
1.3.4 Cross-level interactions 12
1.3.5 Prediction 13
1.3.6 Shrinkage estimation and borrowing strength 14
1.4 Brief history 15
1.4.1 Variance components 15
1.4.2 Random coefficients 15
1.4.3 Variable coefficients 16
1.4.4 Changing coefficients 16
1.4.5 Panel data 16
1.4.6 Growth curves and repeated measurements 16
1.4.7 Bayesian linear models and empirical Bayes estimation 17
1.4.8 Moderator variables 17
1.4.9 Slopes as outcomes 17
1.5 Further reading 18
1.6 Software 18
1.6.1 HLM 19
1.6.2 VARCL 19
1.6.3 BMDP5-V 20
vi CONTENTS
1.6.4 Mln 20
1.6.5 PROC MIXED 20
1.6.6 MIXOR and MIXREG 20
1.7 Summary 21
Note 21
2 OVERVIEW OF CONTEXTUAL MODELS 22
2.1 Introduction 22
2.2 Models 22
2.3 Data 23
2.4 Decomposition of variation 25
2.5 Total or pooled regression 26
2.6 Aggregate regression 27
2.7 The contextual model 28
2.8 The Cronbach model 29
2.9 Analysis of covariance 30
2.10 MLn analysis of contextual models 32
2.11 Summary 34
Notes 34
3 VARYING AND RANDOM COEFFICIENT MODELS 35
3.1 Introduction 35
3.2 Separate regressions 35
3.3 Varying coefficients or 'slopes as outcomes' 36
3.4 The random coefficient model 39
3.5 Assumptions of linear models 44
3.6 'Slopes-as-outcoraes' analysis 45
3.7 Random coefficient results 47
3.7.1 Adding a macro-level explanatory variable 48
3.7.2 Posterior means 51
3.8 ANCOVA as alternative 53
3.9 The number of parameters 54
3.10 Summary 55
Notes 56
CONTENTS vii
4 ANALYSES 57
4.1 Introduction 57
4.1.1 Data description 58
4.1.2 The organization of the four sessions 61
4.2 Session 1 63
4.2.1 Notation for models 63
4.2.2 The null model 63
4.2.3 'HomeWork' and 'MathAchievement' 65
4.2.4 Random slope for 'HomeWork' 66
4.2.5 Adding 'ParentEducation' 69
4.2.6 Traditional regression analysis 70
4.3 Session 2 71
4.3.1 Introduction 71
4.3.2 A model with 'SchoolSize' 73
4.3.3 Changing'SchoolSize'to'Public' 74
4.3.4 Adding a cross-level interaction with 'Public' 75
4.3.5 Analyses with NELS-88 79
4.3.6 Deleting 'HomePublic' and adding 'White' using the small
data set again 80
4.3.7 Adding a random part for 'White' 82
4.3.8 Making the coefficient of 'White' fixed and adding 'MeanSES' 84
4.3.9 Deleting the school characteristic 'Public' 85
4.3.10 Adding an interaction between 'HomeWork' and 'MeanSES' 87
4.3.11 Adding another student-level variable 88
4.3.12 Analyses with NELS-88 89
4.4 Session 3 90
4.4.1 'SES' as a student4evel explanatory variable 90
4.4.2 Adding a random slope 91
4.4.3 Adding 'PercentMinorities' 93
4.4.4 Adding'MeanSES' 94
4.4.5 Analyses with NELS-88, models 2 and 3 96
4.5 Session 4 98
4.5.1 Analysis with class size and a cross-level interaction 98
4.5.2 Interaction between 'Ratio' and 'HomeWork' 99
4.5.3 Repealing the modeling session with NELS-88 101
4.6 Discussion 102
Notes 103
5 FREQUENTLY ASKED QUESTIONS 105
5.1 Introduction 105
5.2 The effects of centering 106
5.2.1 Centering in fixed effects regression models 106
5.2.2 Centering in multilevel models 107
5.3.2 5.3.3 5.3.4 Using the null model as a way Using total between variance Conclusions to calculate R'
5.4 Power
5.4.1 5.4.2 5.4.3 Some simple cases Review of simulation studies Conclusions
viii CONTENTS
5.2.3 Grand mean centering 108
5.2.4 Group mean centering 109
5.2.5 An example 110
5.2.6 Cross-level interactions with 'Public' and 'SES' 112
5.3 Modeled variance 115
5.3.1 Random intercept models 116
R2 117
118
119
119
121
124
126
5.5 To be or not to be random 126
5.5.1 ANCOVA versus RANCOVA versus simple regression 127
5.5.2 Fixed versus random slopes 129
5.6 Estimation techniques and algorithms 130
5.6.1 Which is best, FIML or REML? 133
5.6.2 The effect of estimation methods on the fixed coefficients 133
5.6.3 Estimation methods for variance components 134
5.6.4 Conclusions 135
5.7 Multicollinearity 135
Notes 137
Appendix Coding of NELS-88 Data 139
References 142
Index 147 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Kreft, Ita Leeuw, Jan de |
| author_facet | Kreft, Ita Leeuw, Jan de |
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| discipline | Soziologie |
| discipline_str_mv | Soziologie |
| edition | Reprint. |
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| genre | 1\p (DE-588)4151278-9 Einführung gnd-content |
| genre_facet | Einführung |
| id | DE-604.BV023022010 |
| illustrated | Illustrated |
| index_date | 2024-07-02T19:13:31Z |
| indexdate | 2024-12-06T09:03:52Z |
| institution | BVB |
| isbn | 0761951407 0761951415 9780761951407 9780761951414 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016226050 |
| oclc_num | 263425362 |
| open_access_boolean | |
| physical | X, 148 S. graph. Darst. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Sage |
| record_format | marc |
| series2 | ISM : introducing statistical methods |
| spelling | Kreft, Ita Verfasser aut Introducing multilevel modeling Ita Kreft ; Jan de Leeuw Reprint. London [u.a.] Sage 2006 X, 148 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier ISM : introducing statistical methods Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Empirische Sozialforschung (DE-588)4014606-6 gnd rswk-swf Multi-level-Verfahren (DE-588)4344428-3 gnd rswk-swf Multiple Regression (DE-588)4170720-5 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Statistisches Modell (DE-588)4121722-6 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf 1\p (DE-588)4151278-9 Einführung gnd-content Sozialwissenschaften (DE-588)4055916-6 s Statistisches Modell (DE-588)4121722-6 s DE-188 Multi-level-Verfahren (DE-588)4344428-3 s Multiple Regression (DE-588)4170720-5 s Mathematisches Modell (DE-588)4114528-8 s 2\p DE-604 Statistik (DE-588)4056995-0 s 3\p DE-604 Empirische Sozialforschung (DE-588)4014606-6 s 4\p DE-604 Leeuw, Jan de Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016226050&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 4\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Kreft, Ita Leeuw, Jan de Introducing multilevel modeling Sozialwissenschaften (DE-588)4055916-6 gnd Empirische Sozialforschung (DE-588)4014606-6 gnd Multi-level-Verfahren (DE-588)4344428-3 gnd Multiple Regression (DE-588)4170720-5 gnd Mathematisches Modell (DE-588)4114528-8 gnd Statistisches Modell (DE-588)4121722-6 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4055916-6 (DE-588)4014606-6 (DE-588)4344428-3 (DE-588)4170720-5 (DE-588)4114528-8 (DE-588)4121722-6 (DE-588)4056995-0 (DE-588)4151278-9 |
| title | Introducing multilevel modeling |
| title_auth | Introducing multilevel modeling |
| title_exact_search | Introducing multilevel modeling |
| title_exact_search_txtP | Introducing multilevel modeling |
| title_full | Introducing multilevel modeling Ita Kreft ; Jan de Leeuw |
| title_fullStr | Introducing multilevel modeling Ita Kreft ; Jan de Leeuw |
| title_full_unstemmed | Introducing multilevel modeling Ita Kreft ; Jan de Leeuw |
| title_short | Introducing multilevel modeling |
| title_sort | introducing multilevel modeling |
| topic | Sozialwissenschaften (DE-588)4055916-6 gnd Empirische Sozialforschung (DE-588)4014606-6 gnd Multi-level-Verfahren (DE-588)4344428-3 gnd Multiple Regression (DE-588)4170720-5 gnd Mathematisches Modell (DE-588)4114528-8 gnd Statistisches Modell (DE-588)4121722-6 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Sozialwissenschaften Empirische Sozialforschung Multi-level-Verfahren Multiple Regression Mathematisches Modell Statistisches Modell Statistik Einführung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016226050&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT kreftita introducingmultilevelmodeling AT leeuwjande introducingmultilevelmodeling |