Multilevel modeling of categorical outcomes using IBM SPSS:
Gespeichert in:
| Beteiligte Personen: | , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
New York [u.a.]
Routledge
2012
|
| Schriftenreihe: | Quantitative methodology series
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024961629&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XVI, 439 S. zahlr. Ill., graph. Darst. |
| ISBN: | 9781848729551 9781848729568 |
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| 245 | 1 | 0 | |a Multilevel modeling of categorical outcomes using IBM SPSS |c Ronald H. Heck ; Scott L.Thomas ; Lynn N. Tabata |
| 264 | 1 | |a New York [u.a.] |b Routledge |c 2012 | |
| 300 | |a XVI, 439 S. |b zahlr. Ill., graph. Darst. | ||
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-024961629 | |
Datensatz im Suchindex
| _version_ | 1819267738346979328 |
|---|---|
| adam_text | Contents
Quantitative
Methodology Series
xiii
Preface
xv
Chapter
1
Introduction to Multilevel Models With Categorical Outcomes
1
Introduction
1
Our Intent
3
Analysis of Multilevel Data Structures
5
Scales of Measurement
9
Methods of Categorical Data Analysis
10
Sampling Distributions
13
Link Functions
16
Developing a General Multilevel Modeling Strategy
16
Determining the Probability Distribution and Link Function
18
Developing a Null (or No Predictors) Model
19
Selecting the Covariance Structure
20
Analyzing a Level-1 Model With Fixed Predictors
21
Adding the Level-2 Explanatory Variables
23
Examining Whether a Particular Slope Coefficient Varies Between Groups
23
Covariance Structures
24
Adding Cross-Level Interactions to Explain Variation in the Slope
25
Selecting Level-1 and Level-2 Covariance Structures
25
Model Estimation and Other Typical Multilevel Modeling Issues
26
Determining How Well the Model Fits
27
Syntax Versus IBM SPSS Menu Command Formulation
28
Sample Size
28
Power
29
Missing Data
30
Design Effects, Sample Weights, and the Complex Samples Routine in IBM SPSS
33
An Example
35
Differences Between Multilevel Software Programs
36
Summary
37
Chapter
2
Preparing and Examining the Data for Multilevel Analyses
39
Introduction
39
Data Requirements
39
File Layout
40
Getting Familiar With Basic IBM SPSS Data Commands
42
RECODE:
Creating a New Variable Through Recoding
44
COMPUTE: Creating a New Variable That Is a Function of Some Other Variable
47
MATCH FILES: Combining Data From Separate IBM SPSS Files
49
AGGREGATE: Collapsing Data Within Level-2 Units
56
VARSTOCASES: Vertical Versus Horizontal Data Structures
59
Using Rank to
Recode
the Level-1 or Level-2 Data for Nested Models
65
Creating an Identifier Variable
65
Creating an Individual-Level Identifier Using COMPUTE
66
vi
■ Contents
Creating a Group-Level Identifier Using Rank Cases
Creating a Within-Group-Level Identifier Using Rank Cases
69
Centering
Grand-Mean Centering _5
Group-Mean Centering
Checking the Data
Я0
A Note About Model Building 8()
Summary
Chapter
3
Specification of Generalized Linear Models ^
Introduction
Describing Outcomes
Some Differences in Describing a Continuous or Categorical Outcome HI
Measurement Properties of Outcome Variables
К 1
Explanatory Models for Categorical Outcomes H/
Components for Generalized Linear Model
-Ό
Outcome Probability Distributions and Link Functions
91
Continuous Scale Outcome
91
Positive Scale Outcome
92
Dichotomous Outcome or Proportion
У
2
Nominal Outcome
У
7
Ordinal Outcome
98
Count Outcome
101
Negative Binomial Distribution for Count Data
102
Events-in-Trial Outcome
103
Other Types of Outcomes
103
Estimating Categorical Models With GENLIN
104
GENLIN Model-Building Features
106
Type of Model Command Tab
107
Distribution and Log Link Function
107
Custom Distribution and Link Function
107
The Response Command Tab
107
Dependent Variable
107
Reference Category
108
Number of Events Occurring in a Set of Trials
108
The Predictors Command Tab
108
Predictors HO
Offset n0
The Model Command Tab
Ц0
Main Effects HO
Interactions HO
The Estimation Command Tab
Ш
Parameter Estimation HI
The Statistics Command Tab
113
Model Effects
ИЗ
Additional GENLIN Command Tabs
114
Estimated Marginal (EM) Means
115
Save n5
Export
*лґ
Building a Single-Level Model
^5
Research Questions
л л с
Contents ■
vii
The Data US
Specifying the Model
116
Defining Model
1.1
With IBM SPSS Menu Commands
117
Interpreting the Output of Model
1.1 120
Adding Gender to the Model
121
Defining Model
1.2
With IBM SPSS Menu Commands
122
Obtaining Predicted Probabilities for Males and Females
127
Adding Additional Background Predictors
127
Defining Model
1.3
With IBM SPSS Menu Commands
128
Interpreting the Output of Model
1.3 129
Testing an Interaction
131
Limitations of Single-Level Analysis
132
Summary
133
Note
133
Chapter
4
Multilevel Models With Dichotomous Outcomes
135
Introduction
135
Components for Generalized Linear Mixed Models
135
Specifying a Two-Level Model
136
Specifying a Three-Level Model
136
Model Estimation
137
Building Multilevel Models With GENLIN MIXED
137
Data Structure Command Tab
139
Fields and Effects Command Tab
140
Target Main Screen
140
Fixed Effects Main Screen
141
Random Effects Main Screen
143
Weight and Offset Main Screen
144
Build Options Command Tab
145
Selecting the Sort Order
145
Stopping Rules
147
Confidence Intervals
147
Degrees of Freedom
147
Tests of Fixed Effects
147
Tests of Variance Components
148
Model Options Command Tab
148
Estimating Means and Contrasts
148
Save Fields
149
Examining Variables That Explain Student Proficiency in Reading
149
Research Questions
149
The Data
150
The Unconditional (Null) Model
150
Defining Model
1.1
with IBM SPSS Menu Commands
152
Interpreting the Output of Model
1.1 155
Defining the Within-School Variables
157
Defining Model
1.2
With IBM SPSS Menu Commands
158
Interpreting the Output of Model
1.2 159
Examining Whether a Level-1 Slope Varies Between Schools
162
Defining Model
1.3
with IBM SPSS Menu Commands
164
Interpreting the Output of Model
1.3 165
Contents
165
167
168
Adding Level-2 Predictors to Explain Variability in Intercepts
Defining Model
1.4
with IBM SPSS Menu Commands
Interpreting the Output of Model
1.4
Adding Level-2 Variables to Explain Variation in Level-1 Slopes (Cross-Level
Interaction)
Defining Model
1.5
with IBM SPSS Menu Commands
1/1
Interpreting the Output of Model
1.5
¿
Estimating Means
177
Saving Output
Probit Link
Function
Defining Model
1.6
with IBM SPSS Menu Commands
179
Interpreting
Probit
Coefficients
1™
Interpreting the Output of Model
1.6
1K1
Examining the Effects of Predictors on Probability of Being Proficient
Ш
Extending the Two-Level Model to Three Levels
1 ^2
The Unconditional Model
1 ^3
Defining Model
2.1
with IBM SPSS Menu Commands
185
Interpreting the Output of Model
2.1 189
Defining the Three-Level Model
1
W
Defining Model
2.2
with IBM SPSS Menu Commands
191
Interpreting the Output of Model
2.2 193
Summary
194
Multilevel Models With a Categorical Repeated Measures Outcome
195
Introduction
195
Generalized Estimating Equations
197
GEE Model Estimation
197
An Example Study
198
Research Questions
198
The Data
199
Defining the Model
199
Model Specifying the Intercept and Time
201
Correlation and Covariance Matrices
202
Standard Errors
203
Defining Model
1.1
With IBM SPSS Menu Commands
203
Interpreting the Output of Model
1.1 208
Alternative Coding of the Time Variable
210
Defining Model
1.2
With IBM SPSS Menu Commands
211
Interpreting the Output of Model
1.2 215
Defining Model
1.3
With IBM SPSS Menu Commands
218
Interpreting the Output of Model
1.3 219
Adding a Predictor 2iQ
Defining Model
1.4
With IBM SPSS Menu Commands
219
Interpreting the Output of Model
1.4 221
Adding an Interaction Between Female and the Time Parameter
222
Adding an Interaction to Model
1.5 223
Interpreting the Output of Model
1.5 224
Categorical Longitudinal Models Using GENLIN MIXED
224
Specifying a^GEE Model Within GENLIN MIXED
224
225
229
3
Defining Model
2.1
With IBM SPSS Menu Command^
Interpreting the Output of Model
2.1
Contents
ш
ix
Examining a Random Intercept at the Between-Student Level
229
Defining Model
2.2
With IBM SPSS Menu Commands
231
Interpreting the Output of Model
2.2 234
What Variables Affect Differences in Proficiency Across Individuals?
235
Defining Model
2.3
With IBM SPSS Menu Commands
236
Adding Two Interactions to Model
2.3 237
Interpreting the Output of Model
2.3 237
Building a Three-Level Model in GENLIN MIXED
239
The Beginning Model
239
Defining Model
3.1
With IBM SPSS Menu Commands
241
Interpreting the Output of Model
3.1 246
Adding Student and School Predictors
248
Defining Model
3.2
With IBM SPSS Menu Commands
249
Adding Two Interactions to Model
3.2 250
Adding Two More Interactions to Model
3.2 251
Interpreting the Output of Model
3.2 252
An Example Experimental Design
252
Defining Model
4.1
With IBM SPSS Menu Commands
255
Summary
259
Chapter
6
Two-Level Models With Multinomial and Ordinal Outcomes
261
Introduction
261
Building a Model to Examine a Multinomial Outcome
262
Research Questions
262
The Data
262
Defining the Multinomial Model
262
Defining a Preliminary Single-Level Model
264
Defining Model
1.1
With IBM SPSS Menu Commands
266
Interpreting the Output of Model
1.1 269
Developing a Multilevel Multinomial Model
269
Unconditional Two-Level Model
270
Defining Model
2.1
With IBM SPSS Menu Commands
271
Interpreting the Output of Model
2.1 273
Computing Predicted Probabilities
273
Level-1 Model
275
Defining Model
2.2
With IBM SPSS Menu Commands
276
Interpreting the Output of Model
2.2 277
Adding School-Level Predictors
279
Defining Model
2.3
With IBM SPSS Menu Commands
280
Interpreting the Output of Model
2.3 281
Investigating a Random Slope
282
Defining Model
2.4
With IBM SPSS Menu Commands
283
Interpreting the Output of Model
2.4
Model Results
285
Developing a Model With an Ordinal Outcome
285
The Data
290
Developing a Single-Level Model
290
Preliminary Analyses
294
Defining Model
3.1
with IBM SPSS Menu Commands
295
Interpreting the Output of Model
3.1 298
Contents
Adding Student Background Predictors
;
Defining Model
3.2
with IBM SPSS Menu Commands -™
Interpreting the Output of Model
3.2 ;
Testing an Interaction
305
305
Following Up With a Smaller Random Sample i(h
Defining Model
3.3
With IBM SPSS Menu Commands ^
Adding Interactions to Model
3.3 ■
Interpreting the Output of Model
3.3
Developing a Multilevel Ordinal Model
Levei-i Model
; ;
Unconditional Model
Defining Model
4.1
With IBM SPSS Menu Commands
309
Interpreting the Output of Model
4.1 1
л
Within-School Predictor
4
Defining Model
4.2
With IBM SPSS Menu Commands
31 5
Interpreting the Output of Model
4.2
ł rl
Adding the School-Level Predictors
■>
(l
Defining Model
4.3
With IBM SPSS Menu Commands
31
S
Interpreting the Output of Model
4.3 319
Using Complementary Log-Log Link
320
Interpreting a Categorical Predictor
320
Other Possible Analyses
^22
Examining a Mediating Effect at Level
1 ^22
Defining Model
4.4
With IBM SPSS Menu Commands
324
Interpreting the Output of Model
4.4 325
Estimating the Mediated Effect
326
Summary
327
Note
327
Chapter
7
Two-Level Models With Count Data
329
Introduction
329
A Poisson
Regression Model With Constant Exposure
329
Trie Data
329
Preliminary Single-Level Models
331
Defining Model
1.1
With IBM SPSS Menu Commands
334
Interpreting the Output Results of Model
1.1 337
Defining Model
1.2
With IBM SPSS Menu Commands
338
Interpreting the Output Results of Model
1.2 340
Considering Possible Overdispersion
343
Defining Model
1.3
with IBM SPSS Menu Commands
345
Interpreting the Output Results of Model
1.3 346
Defining Model
1.4
with IBM SPSS Menu Commands
347
Interpreting the Output Results of Model
1.4 348
Defining Model
1.5
with IBM SPSS Menu Commands
349
Interpreting the Output Results of Model
1.5 350
Comparing the Fit ^S0
Estimating Two-Level Count Data With GENLIN MIXED
350
Defining Model
2.1
With IBM SPSS Menu Commands
351
Interpreting the Output Results of Model
2.1 354
Contents ■ xi
Building
a Two-Level
Model 354
Defining
Model 2.2
with IBM SPSS Menu Commands
355
Interpreting the Output Results of Model
2.2 357
Within-Schools Model
358
Defining Model
2.3
with IBM SPSS Menu Commands
359
Interpreting the Output Results of Model
2.3 360
Examining Whether the Negative Binomial Distribution Is a Better Choice
361
Defining Model
2.4
With IBM SPSS Menu Commands
362
Interpreting the Output Results of Model
2.4 363
Does the SES-Failure Slope Vary Across Schools?
363
Defining Model
2.5
With IBM SPSS Menu Commands
364
Interpreting the Output Results of Model
2.5 366
Modeling Variability at Level
2 366
Defining Model
2.6
With IBM SPSS Menu Commands
367
Interpreting the Output Results of Model
2.6 368
Adding the Cross-Level Interactions
369
Defining Model
2.7
With IBM SPSS Menu Commands
370
Adding Two Interactions to Model
2.7 370
Interpreting the Output Results of Model
2.7 372
Developing a Two-Level Count Model With an Offset Variable
373
The Data
374
Research Questions
374
Offset Variable
375
Specifying a Single-Level Model
376
Defining Model
3.1
With IBM SPSS Menu Commands
377
Interpreting the Output Results of Model
3.1 380
Adding the Offset
381
Defining Model
3.2
With IBM SPSS Menu Commands
383
Interpreting the Output Results of Model
3.2 384
Defining Model
3.3
With IBM SPSS Menu Commands
385
Interpreting the Output Results of Model
3.3 386
Defining Model
3.4
With IBM SPSS Menu Commands
387
Interpreting the Output Results of Model
3.4 389
Estimating the Model With GENLIN MIXED
390
Defining Model
4.1
With IBM SPSS Menu Commands
390
Interpreting the Output Results of Model
4.1 395
Defining Model
4.2
With IBM SPSS Menu Commands
396
Interpreting the Output Results of Model
4.2 397
Summary
398
Chapter
8
Concluding Thoughts
399
References
405
Appendices
A: Syntax Statements
409
B: Model Comparisons Across Software Applications
431
Author Index 433
Subject Index
435
|
| any_adam_object | 1 |
| author | Heck, Ronald H. Thomas, Scott L. Tabata, Lynn N. |
| author_GND | (DE-588)14277250X (DE-588)142773425 |
| author_facet | Heck, Ronald H. Thomas, Scott L. Tabata, Lynn N. |
| author_role | aut aut aut |
| author_sort | Heck, Ronald H. |
| author_variant | r h h rh rhh s l t sl slt l n t ln lnt |
| building | Verbundindex |
| bvnumber | BV040105164 |
| callnumber-first | B - Philosophy, Psychology, Religion |
| callnumber-label | BF39 |
| callnumber-raw | BF39 |
| callnumber-search | BF39 |
| callnumber-sort | BF 239 |
| callnumber-subject | BF - Psychology |
| classification_rvk | CM 4400 MR 2200 SK 850 ST 601 |
| ctrlnum | (OCoLC)799119067 (DE-599)BVBBV040105164 |
| dewey-full | 300.285/555 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 300 - Social sciences |
| dewey-raw | 300.285/555 |
| dewey-search | 300.285/555 |
| dewey-sort | 3300.285 3555 |
| dewey-tens | 300 - Social sciences |
| discipline | Informatik Soziologie Psychologie Mathematik |
| format | Book |
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| id | DE-604.BV040105164 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T16:08:10Z |
| institution | BVB |
| isbn | 9781848729551 9781848729568 |
| language | English |
| lccn | 2012004179 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024961629 |
| oclc_num | 799119067 |
| open_access_boolean | |
| owner | DE-11 DE-N2 DE-19 DE-BY-UBM DE-473 DE-BY-UBG DE-188 DE-355 DE-BY-UBR DE-92 DE-20 DE-824 |
| owner_facet | DE-11 DE-N2 DE-19 DE-BY-UBM DE-473 DE-BY-UBG DE-188 DE-355 DE-BY-UBR DE-92 DE-20 DE-824 |
| physical | XVI, 439 S. zahlr. Ill., graph. Darst. |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Routledge |
| record_format | marc |
| series2 | Quantitative methodology series |
| spellingShingle | Heck, Ronald H. Thomas, Scott L. Tabata, Lynn N. Multilevel modeling of categorical outcomes using IBM SPSS Kategoriale Daten (DE-588)4327512-6 gnd SPSS (DE-588)4056588-9 gnd Kontextanalyse (DE-588)4129240-6 gnd |
| subject_GND | (DE-588)4327512-6 (DE-588)4056588-9 (DE-588)4129240-6 |
| title | Multilevel modeling of categorical outcomes using IBM SPSS |
| title_auth | Multilevel modeling of categorical outcomes using IBM SPSS |
| title_exact_search | Multilevel modeling of categorical outcomes using IBM SPSS |
| title_full | Multilevel modeling of categorical outcomes using IBM SPSS Ronald H. Heck ; Scott L.Thomas ; Lynn N. Tabata |
| title_fullStr | Multilevel modeling of categorical outcomes using IBM SPSS Ronald H. Heck ; Scott L.Thomas ; Lynn N. Tabata |
| title_full_unstemmed | Multilevel modeling of categorical outcomes using IBM SPSS Ronald H. Heck ; Scott L.Thomas ; Lynn N. Tabata |
| title_short | Multilevel modeling of categorical outcomes using IBM SPSS |
| title_sort | multilevel modeling of categorical outcomes using ibm spss |
| topic | Kategoriale Daten (DE-588)4327512-6 gnd SPSS (DE-588)4056588-9 gnd Kontextanalyse (DE-588)4129240-6 gnd |
| topic_facet | Kategoriale Daten SPSS Kontextanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024961629&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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