Multilevel analysis for applied research: it's just regression!
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York ; London
Guilford Press
2007
|
| Schriftenreihe: | Methodology in the social sciences
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXVII, 355 S. graph. Darst. |
| ISBN: | 159385191X 1593854293 9781593851910 9781593854294 |
Internformat
MARC
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| 100 | 1 | |a Bickel, Robert |e Verfasser |0 (DE-588)1052141951 |4 aut | |
| 245 | 1 | 0 | |a Multilevel analysis for applied research |b it's just regression! |c Robert Bickel |
| 264 | 1 | |a New York ; London |b Guilford Press |c 2007 | |
| 300 | |a XXVII, 355 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Methodology in the social sciences | |
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Datensatz im Suchindex
| _version_ | 1804137232800415744 |
|---|---|
| adam_text | Contents
1 ·
Broadening the Scope of Regression Analysis
1
1.1
Chapter Introduction
1
1.2
Why Use Multilevel Regression Analysis?
2
1.3
Limitations of Available Instructional Material
3
1.4
Multilevel Regression Analysis in Suggestive Historical Context
4
1.5
It s Just Regression under Specific Circumstances
8
1.6
Jumping the Gun to a Multilevel Illustration
9
1.7
Summing Up
12
1.8
Useful Resources
14
2 ·
The Meaning of Nesting
17
2.1
Chapter Introduction
17
2.2
Nesting Illustrated: School Achievement and Neighborhood Quality
20
2.3
Nesting Illustrated: Comparing Public and Private Schools
25
2.4
Cautionary Comment on Residuals in Multilevel Analysis
31
2.5
Nesting and Correlated Residuals
33
2.6
Nesting and Effective Sample Size
41
2.7
Summing Up
47
2.8
Useful Resources
49
3 ·
Contextual Variables
52
3.1
Chapter Introduction
52
3.2
Contextual Variables and Analytical Opportunities
55
3.3
Contextual Variables and Independent Observations
61
3.4
Contextual Variables and Independent Observations: A Nine-Category
68
Dummy Variable
3.5
Contextual Variables, Intraclass Correlation, and Misspecification
74
3.6
Contextual Variables and Varying Parameter Estimates
81
XXIV
Conferite
Contents xxv
3.7
Contextual
Variables
and Covariance Structure
3.8
Contextual Variables and Degrees of Freedom
3.9
Summing Up
3.10
Useful Resources
4 ·
From OLS to Random Coefficient to Multilevel Regression
4.1
Chapter Introduction
4.2
Simple Regression Equation
4.3
Simple Regression with an Individual-Level Variable
4.4
Multiple Regression:
Adding a Contextual Variable
4.5
Nesting (Again!) with a Contextual Variable
4.6
Is There a Problem with Degrees of Freedom?
4.7
Is There a Problem with Dependent Observations?
4.8
Alternatives to OLS Estimators
4.9
The Conceptual Basis of ML Estimators
4.10
Desirable Properties of REML Estimators
4.11
Applying REML Estimators with Random Coefficient
Regression Models
4.12
Fixed Components and Random Components
4.13
Interpreting Random Coefficients: Developing a Cautionary Comment
4.14
Subscript Conventions
4.15
Percentage of Variance Explained for Random Coefficient
and Multilevel Models
4.16
Grand-Mean Centering
4.17
Grand-Mean Centering, Group-Mean Centering,
and Raw Scores Compared
4.18
Summing Up
4.19
Useful Resources
5 ·
Developing the Multilevel Regression Model
5.1
Chapter Introduction
5.2
From Random Coefficient Regression to Multilevel Regression
5.3
Equations for a Random Intercept and Random Slope
5.4
Subscript Conventions for Two-Level Models: Gamma Coefficients
5.5
The Full Equation
5.6
An Implied Cross-Level Interaction Term
5.7
Estimating a Multilevel Model: The Full Equation
5.8
A Multilevel Model with a Random Slope and Fixed Slopes
at Level One
5.9
Complexity and Confusion: Too Many Random Components
5.10
Interpreting Multilevel Regression Equations
5.11
Comparing Interpretations of Alternative Specifications
5.12
What Happened to the Error Term?
86
98
101
102
105
105
107
107
108
109
110
111
112
114
117
119
125
128
130
131
134
137
144
148
150
150
152
153
154
155
156
156
161
163
167
174
177
5.13
Summing Up
5.14
Useful Resources
6 ·
Giving OLS Regression Its Due
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
6.10
6.11
Chapter Introduction
An Extended Exercise with County-Level Data
Tentative Specification of an OLS Regression Model
Preliminary Regression Results
Surprise Results and Possible Violation of OLS Assumptions
byXB
and XNAII
Curvilinear Relationships:
Quadratic Functional Form
A Respecified OLS Regression Model
Interpreting Quadratic Relationships
Nonadditivity and Interaction Terms
Further Respecification of the Regression Model
6.12
Clarifying OLS Interaction Effects
6.13
Interpreting Results for the Respecified OLS Regression Equation
for County-Level Data
6.14
Summing Up
6.15
Useful Resources
7 ·
Does Multilevel Regression Have Anything to Contribute?
Chapter Introduction
Contextual Effects in OLS Regression
Respecification and Changing Functional Form
Addressing the Limitations of OLS
Counties Nested within States: Intraclass Correlation
Multilevel Regression Model Specification: Learning from OLS
Interpreting the Multilevel Regression Equation for County-Level Data
Knowing When to Stop
Summing Up
7.1
7.2
7.3
7.4
7.5
7.6
7.7
7.8
7.9
7.10
Useful Resources
8 ·
Multilevel Regression Models with Three Levels
8.1
Chapter Introduction
8.2
Students Nested within Schools and within Districts
8.3
Level One: Students
8.4
Level Two: Schools
8.5
Level Three: Districts
8.6
Notation and Subscript Conventions for Specifying
a Three-Level Model
8.7
Estimating a Three-Level Random Coefficient Model
180
181
183
183
184
184
189
190
191
191
192
192
193
196
196
198
199
200
201
201
202
204
206
207
208
211
213
215
217
218
218
219
220
221
222
224
226
xxvi Contents
Contents xxvü
8.8
Adding a Second Level-One Predictor
228
8.9
Adding a Level-Two Predictor
232
8.10
Adding a Second Predictor at Level Two and a Predictor at Level Three
235
8.11
Discretionary Use of Same-Level Interaction Terms
237
8.12
Ongoing Respecification of a Three-Level Model
239
8.13
A Level-Two Random Slope at Level Three
242
8.14
Summing Up
246
8.15
Useful Resources
246
9 ·
Familiar Measures Applied to Three-Level Models
248
9.1
Chapter Introduction
248
9.2
The Intraclass Correlation Coefficient Revisited
249
9.3
Percentage of Variance Explained in a Level-One Dependent Variable
252
9.4
Other Summary Measures Used with Multilevel Regression
257
9.5
Summing Up
264
9.6
Useful Resources
264
11.7
Sample Sizes
300
11.8
The Multilevel Regression Growth Model Respecified
302
11.9
The Multilevel Regression Growth Model: Further Respecification
303
11.10
Residual Covariance Structures
306
11.11
Multilevel Regression Growth Models with Three Levels
311
11.12
Nonlinear Growth Curves
316
11.13
NCLEX Pass Rates with a Time-Dependent Predictor
325
11.14
Summing Up
328
11.15
Useful Resources
329
References
331
Author Index
343
Subject Index
347
10 ·
Determining Sample Sizes for Multilevel Regression
266
10.1
Chapter Introduction
266
10.2
Interest in Sample Size in OLS and Multilevel Regression
268
10.3
Sample Size: Rules of Thumb and Data Constraints
272
10.4
Estimation and Inference for Unstandardized Regression Coefficients
275
10.5
More Than One Level of Analysis Means More Than One Sample Size
276
10.6
An Individual-Level OLS Analysis with a Large Sample
277
10.7
A Group-Level OLS Analysis with a Small Sample
278
10.8
Standard Errors: Corrected and Uncorrected, Individual
279
and Group Levels
10.9
When Output Is Not Forthcoming!
280
10.10
Sample Sizes and OLS-Based Commonsense in Multilevel Regression
281
10.11
Sample Size Generalizations Peculiar to Multilevel Regression
282
10.12
Level-One Sample Size and Level-Two Statistical Power
282
10.13
The Importance of Sample Size at Higher Levels
282
10.14
Summing Up
283
10.15
Useful Resources
283
11·
Multilevel Regression Growth Models
11.1
Chapter Introduction
11.2
Analyzing Longitudinal Data:
Pretest-Posttest
11.3
Nested Measures: Growth in Student Vocabulary Achievement
11.4
Nested Measures: Growth in NCLEX Pass Rates
11.5
Developing Multilevel Regression Growth Models
11.6
Summary Statistics with Growth Models
285
285
286
287
290
292
297
|
| adam_txt |
Contents
1 ·
Broadening the Scope of Regression Analysis
1
1.1
Chapter Introduction
1
1.2
Why Use Multilevel Regression Analysis?
2
1.3
Limitations of Available Instructional Material
3
1.4
Multilevel Regression Analysis in Suggestive Historical Context
4
1.5
It's Just Regression under Specific Circumstances
8
1.6
Jumping the Gun to a Multilevel Illustration
9
1.7
Summing Up
12
1.8
Useful Resources
14
2 ·
The Meaning of Nesting
17
2.1
Chapter Introduction
17
2.2
Nesting Illustrated: School Achievement and Neighborhood Quality
20
2.3
Nesting Illustrated: Comparing Public and Private Schools
25
2.4
Cautionary Comment on Residuals in Multilevel Analysis
31
2.5
Nesting and Correlated Residuals
33
2.6
Nesting and Effective Sample Size
41
2.7
Summing Up
47
2.8
Useful Resources
49
3 ·
Contextual Variables
52
3.1
Chapter Introduction
52
3.2
Contextual Variables and Analytical Opportunities
55
3.3
Contextual Variables and Independent Observations
61
3.4
Contextual Variables and Independent Observations: A Nine-Category
68
Dummy Variable
3.5
Contextual Variables, Intraclass Correlation, and Misspecification
74
3.6
Contextual Variables and Varying Parameter Estimates
81
XXIV
Conferite
Contents xxv
3.7
Contextual
Variables
and Covariance Structure
3.8
Contextual Variables and Degrees of Freedom
3.9
Summing Up
3.10
Useful Resources
4 ·
From OLS to Random Coefficient to Multilevel Regression
4.1
Chapter Introduction
4.2
Simple Regression Equation
4.3
Simple Regression with an Individual-Level Variable
4.4
Multiple Regression:
Adding a Contextual Variable
4.5
Nesting (Again!) with a Contextual Variable
4.6
Is There a Problem with Degrees of Freedom?
4.7
Is There a Problem with Dependent Observations?
4.8
Alternatives to OLS Estimators
4.9
The Conceptual Basis of ML Estimators
4.10
Desirable Properties of REML Estimators
4.11
Applying REML Estimators with Random Coefficient
Regression Models
4.12
Fixed Components and Random Components
4.13
Interpreting Random Coefficients: Developing a Cautionary Comment
4.14
Subscript Conventions
4.15
Percentage of Variance Explained for Random Coefficient
and Multilevel Models
4.16
Grand-Mean Centering
4.17
Grand-Mean Centering, Group-Mean Centering,
and Raw Scores Compared
4.18
Summing Up
4.19
Useful Resources
5 ·
Developing the Multilevel Regression Model
5.1
Chapter Introduction
5.2
From Random Coefficient Regression to Multilevel Regression
5.3
Equations for a Random Intercept and Random Slope
5.4
Subscript Conventions for Two-Level Models: Gamma Coefficients
5.5
The Full Equation
5.6
An Implied Cross-Level Interaction Term
5.7
Estimating a Multilevel Model: The Full Equation
5.8
A Multilevel Model with a Random Slope and Fixed Slopes
at Level One
5.9
Complexity and Confusion: Too Many Random Components
5.10
Interpreting Multilevel Regression Equations
5.11
Comparing Interpretations of Alternative Specifications
5.12
What Happened to the Error Term?
86
98
101
102
105
105
107
107
108
109
110
111
112
114
117
119
125
128
130
131
134
137
144
148
150
150
152
153
154
155
156
156
161
163
167
174
177
5.13
Summing Up
5.14
Useful Resources
6 ·
Giving OLS Regression Its Due
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
6.10
6.11
Chapter Introduction
An Extended Exercise with County-Level Data
Tentative Specification of an OLS Regression Model
Preliminary Regression Results
Surprise Results and Possible Violation of OLS Assumptions
byXB
and XNAII
Curvilinear Relationships:
Quadratic Functional Form
A Respecified OLS Regression Model
Interpreting Quadratic Relationships
Nonadditivity and Interaction Terms
Further Respecification of the Regression Model
6.12
Clarifying OLS Interaction Effects
6.13
Interpreting Results for the Respecified OLS Regression Equation
for County-Level Data
6.14
Summing Up
6.15
Useful Resources
7 ·
Does Multilevel Regression Have Anything to Contribute?
Chapter Introduction
Contextual Effects in OLS Regression
Respecification and Changing Functional Form
Addressing the Limitations of OLS
Counties Nested within States: Intraclass Correlation
Multilevel Regression Model Specification: Learning from OLS
Interpreting the Multilevel Regression Equation for County-Level Data
Knowing When to Stop
Summing Up
7.1
7.2
7.3
7.4
7.5
7.6
7.7
7.8
7.9
7.10
Useful Resources
8 ·
Multilevel Regression Models with Three Levels
8.1
Chapter Introduction
8.2
Students Nested within Schools and within Districts
8.3
Level One: Students
8.4
Level Two: Schools
8.5
Level Three: Districts
8.6
Notation and Subscript Conventions for Specifying
a Three-Level Model
8.7
Estimating a Three-Level Random Coefficient Model
180
181
183
183
184
184
189
190
191
191
192
192
193
196
196
198
199
200
201
201
202
204
206
207
208
211
213
215
217
218
218
219
220
221
222
224
226
xxvi Contents
Contents xxvü
8.8
Adding a Second Level-One Predictor
228
8.9
Adding a Level-Two Predictor
232
8.10
Adding a Second Predictor at Level Two and a Predictor at Level Three
235
8.11
Discretionary Use of Same-Level Interaction Terms
237
8.12
Ongoing Respecification of a Three-Level Model
239
8.13
A Level-Two Random Slope at Level Three
242
8.14
Summing Up
246
8.15
Useful Resources
246
9 ·
Familiar Measures Applied to Three-Level Models
248
9.1
Chapter Introduction
248
9.2
The Intraclass Correlation Coefficient Revisited
249
9.3
Percentage of Variance Explained in a Level-One Dependent Variable
252
9.4
Other Summary Measures Used with Multilevel Regression
257
9.5
Summing Up
264
9.6
Useful Resources
264
11.7
Sample Sizes
300
11.8
The Multilevel Regression Growth Model Respecified
302
11.9
The Multilevel Regression Growth Model: Further Respecification
303
11.10
Residual Covariance Structures
306
11.11
Multilevel Regression Growth Models with Three Levels
311
11.12
Nonlinear Growth Curves
316
11.13
NCLEX Pass Rates with a Time-Dependent Predictor
325
11.14
Summing Up
328
11.15
Useful Resources
329
References
331
Author Index
343
Subject Index
347
10 ·
Determining Sample Sizes for Multilevel Regression
266
10.1
Chapter Introduction
266
10.2
Interest in Sample Size in OLS and Multilevel Regression
268
10.3
Sample Size: Rules of Thumb and Data Constraints
272
10.4
Estimation and Inference for Unstandardized Regression Coefficients
275
10.5
More Than One Level of Analysis Means More Than One Sample Size
276
10.6
An Individual-Level OLS Analysis with a Large Sample
277
10.7
A Group-Level OLS Analysis with a Small Sample
278
10.8
Standard Errors: Corrected and Uncorrected, Individual
279
and Group Levels
10.9
When Output Is Not Forthcoming!
280
10.10
Sample Sizes and OLS-Based Commonsense in Multilevel Regression
281
10.11
Sample Size Generalizations Peculiar to Multilevel Regression
282
10.12
Level-One Sample Size and Level-Two Statistical Power
282
10.13
The Importance of Sample Size at Higher Levels
282
10.14
Summing Up
283
10.15
Useful Resources
283
11·
Multilevel Regression Growth Models
11.1
Chapter Introduction
11.2
Analyzing Longitudinal Data:
Pretest-Posttest
11.3
Nested Measures: Growth in Student Vocabulary Achievement
11.4
Nested Measures: Growth in NCLEX Pass Rates
11.5
Developing Multilevel Regression Growth Models
11.6
Summary Statistics with Growth Models
285
285
286
287
290
292
297 |
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| author | Bickel, Robert |
| author_GND | (DE-588)1052141951 |
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| ctrlnum | (OCoLC)184969329 (DE-599)BVBBV023015820 |
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| discipline_str_mv | Allgemeines Informatik Politologie Soziologie Psychologie Mathematik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV023015820 |
| illustrated | Illustrated |
| index_date | 2024-07-02T19:11:16Z |
| indexdate | 2024-07-09T21:09:02Z |
| institution | BVB |
| isbn | 159385191X 1593854293 9781593851910 9781593854294 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016219981 |
| oclc_num | 184969329 |
| open_access_boolean | |
| owner | DE-19 DE-BY-UBM DE-11 DE-20 DE-384 DE-188 DE-703 DE-473 DE-BY-UBG DE-355 DE-BY-UBR DE-29 DE-N2 DE-N32 DE-29T |
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| physical | XXVII, 355 S. graph. Darst. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Guilford Press |
| record_format | marc |
| series2 | Methodology in the social sciences |
| spelling | Bickel, Robert Verfasser (DE-588)1052141951 aut Multilevel analysis for applied research it's just regression! Robert Bickel New York ; London Guilford Press 2007 XXVII, 355 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Methodology in the social sciences Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 s Multivariate Analyse (DE-588)4040708-1 s DE-604 Digitalisierung UB Augsburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016219981&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bickel, Robert Multilevel analysis for applied research it's just regression! Multivariate Analyse (DE-588)4040708-1 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| subject_GND | (DE-588)4040708-1 (DE-588)4129903-6 |
| title | Multilevel analysis for applied research it's just regression! |
| title_auth | Multilevel analysis for applied research it's just regression! |
| title_exact_search | Multilevel analysis for applied research it's just regression! |
| title_exact_search_txtP | Multilevel analysis for applied research it's just regression! |
| title_full | Multilevel analysis for applied research it's just regression! Robert Bickel |
| title_fullStr | Multilevel analysis for applied research it's just regression! Robert Bickel |
| title_full_unstemmed | Multilevel analysis for applied research it's just regression! Robert Bickel |
| title_short | Multilevel analysis for applied research |
| title_sort | multilevel analysis for applied research it s just regression |
| title_sub | it's just regression! |
| topic | Multivariate Analyse (DE-588)4040708-1 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| topic_facet | Multivariate Analyse Regressionsanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016219981&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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