Multilevel and longitudinal modeling with IBM SPSS:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York [u.a.]
Routledge
2014
|
| Ausgabe: | 2. ed. |
| Schriftenreihe: | Quantitative methodology series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | XVII, 443 S. Ill., graph. Darst. |
| ISBN: | 9780415817103 9780415817110 |
Internformat
MARC
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| 100 | 1 | |a Heck, Ronald H. |e Verfasser |0 (DE-588)14277250X |4 aut | |
| 245 | 1 | 0 | |a Multilevel and longitudinal modeling with IBM SPSS |c Ronald H. Heck ; Scott L. Thomas ; Lynn N. Tabata |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a New York [u.a.] |b Routledge |c 2014 | |
| 300 | |a XVII, 443 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Quantitative methodology series | |
| 650 | 0 | 7 | |a SPSS |0 (DE-588)4056588-9 |2 gnd |9 rswk-swf |
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| 689 | 0 | 0 | |a Statistisches Modell |0 (DE-588)4121722-6 |D s |
| 689 | 0 | 1 | |a SPSS |0 (DE-588)4056588-9 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Thomas, Scott L. |e Verfasser |4 aut | |
| 700 | 1 | |a Tabata, Lynn N. |e Verfasser |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-0-203-70124-9 |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026253704&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 856 | 4 | 2 | |m Digitalisierung UB Passau - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026253704&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |3 Klappentext |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-026253704 | |
Datensatz im Suchindex
| _version_ | 1817681163611799552 |
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| adam_text |
Contents
Preface
xv
Chapter
1
Introduction to Multilevel Modeling with IBM SPSS
1
Our Intent
2
Overview of Topics
4
Analysis of Multilevel Data Structures
4
Partitioning Variation in an Outcome
8
Developing a General Multilevel-Modeling Strategy
9
Illustrating the Steps in Investigating a Proposed Model
10
1.
One-Way AN OVA (No Predictors) Model
11
2.
Analyze a Level
1
Model with Fixed Predictors
12
3.
Add the Level
2
Explanatory Variables
13
4.
Examine Whether a Particular Slope Coefficient Varies Between Groups
14
5.
Adding Cross-Level Interactions to Explain Variation in the Slope
15
Syntax Versus IBM SPSS Menu Command Formulation
16
Model Estimation and Other Typical Multilevel-Modeling Issues
18
Sample Size
20
Power
20
Differences Between Multilevel Software Programs
21
Standardized and Unstandardized Coefficients
21
Missing Data
22
Missing Data at Level
2 26
Missing Data in Vertical Format in IBM SPSS MIXED
28
Design Effects, Sample Weights, and the Complex Samples Routine
in IBM SPSS
30
An Example Using Multilevel Weights
32
Summary
34
Chapter
2
Preparing and Examining the Data for Multilevel Analyses
35
Data Requirements
35
File Layout
36
Getting Familiar with Basic IBM SPSS Data Commands
38
Recode:
Creating a New Variable Through Recoding
39
Receding Old Values to New Values
39
Recoding Old Values to New Values Using "Range"
41
Compute: Creating a New Variable That Is a Function of Some Other Variable
44
Match Files: Combining Data From Separate IBM SPSS Files
46
Aggregate: Collapsing Data Within Level
2
Units
52
VARSTOCASES: Vertical Versus Horizontal Data Structures
53
Using "Compute" and "Rank" to
Recode
the Level
1
or Level
2
Data
for Nested Models
59
Creating an Identifier Variable
59
Creating an Individual-Level Identifier Using "Compute"
60
Creating a Group-Level Identifier Using "Rank Cases"
61
Creating a Within-Group-Level Identifier Using "Rank Cases"
63
Centering
65
viii
■ Contents
Grand-Mean Centering
67
Group-Mean Centering
69
Checking the Data
72
A Note About Model Building
73
Summary
73
Chapter
3
Defining a Basic Two-Level Multilevel Regression Model
75
From Single-Level to Multilevel Analysis
75
Building a Two-Level Model
77
Research Questions
78
The Data
78
Specifying the Model
78
Graphing the Relationship Between
SES
and Math Test Scores
with IBM SPSS Menu Commands
80
Graphing the Subgroup Relationships Between
SES
and Math Test Scores
with IBM SPSS Menu Commands
86
Building a Multilevel Model with IBM SPSS MIXED
88
Step
1:
Examining Variance Components Using the Null Model
89
Defining Model
1
(Null) with IBM SPSS Menu Commands
90
Interpreting the Output From Model
1
(Null)
93
Step
2:
Building the Individual-Level (or Level
1)
Random Intercept Model
95
Defining Model
2
with IBM SPSS Menu Commands
96
Interpreting the Output From Model
2 98
Step
3:
Building the Group-Level (or Level
2)
Random Intercept Model
101
Defining Model
3
with IBM SPSS Menu Commands
102
Interpreting the Output From Model
3 104
Defining Model
ЗА
{Public as Covariate) with IBM SPSS
Menu Commands
108
Step
4:
Adding a Randomly Varying Slope (the Random Slope and
Intercept Model)
110
Defining Model
4
with IBM SPSS Menu Commands
111
Interpreting the Output From Model
4 113
Step
5:
Explaining Variability in the Random Slope (More Complex
Random Slopes and Intercept Models)
115
Defining Model
5
with IBM SPSS Menu Commands
116
Add First Interaction to Model
5:
ses_mean*ses
118
Add Second Interaction to Model
5:
pro4yrc*ses
118
Add Third Interaction to Model S\ public*
ses
118
Interpreting the Output From Model
5 119
Defining Model 5A with IBM SPSS Menu Commands
121
Graphing a Cross-Level Interaction (SES-Achievement Relationships
in High- and Low-Achieving Schools) with IBM SPSS Menu Commands
123
Centering Predictors
126
Centering Predictors in Models with Random Slopes
128
Summary
130
Chapter
4
Three-Level Univariate Regression Models
131
Three-Level Univariate Model
131
Research Questions
131
The Data
132
Contents
IX
Defining the Three-Level Multilevel Model
133
The Null Model (No Predictors)
134
Defining Model
1
(Null) with IBM SPSS Menu Commands
135
Interpreting the Output From Model
1
(Null)
138
Model
2:
Defining Predictors at Each Level
139
Defining Model
2
with IBM SPSS Menu Commands
142
Interpreting the Output From Model
2 144
Model
3:
Group-Mean Centering
145
Defining Model
3
with IBM SPSS Menu Commands
145
Interpreting the Output From Model
3 147
Covariance Estimates
148
Model
4:
Does the Slope Vary Randomly Across Schools?
149
Defining Model
4
with IBM SPSS Menu Commands
150
Interpreting the Output From Model
4 153
Developing an Interaction Term
154
Preliminary Investigation of the Interaction
155
Defining Models A and
В
(Preliminary Testing of Interactions)
with IBM SPSS Menu Commands
156
Model A Test Interaction: teacbeffect*classlowses_mean
158
Model
В
Test Interaction: gmteacheffeet*gmclasslowses_mean
159
Model
5:
Examining a Level
2
Interaction
161
Defining Model
5
with IBM SPSS Menu Commands
161
Add Interaction to Model
5:
gmclmslowses_mean*gmieachejfeci
163
Interpreting the Output From Model
5 163
Comparing the Fit or' Successive Models
164
Summary
166
ChapterS Examining Individual Change with Repeated Measures Data
167
Ways to Examine Repeated Observations on Individuals
167
Considerations in Specifying a Linear Mixed Model
168
An Example Study
171
Research Questions
171
The Data
171
Examining the Shape
oí
Students' Growth Trajectories
173
Graphing the Linear and Nonlinear Growth Trajectories
with IBM SPSS Menu Commands
175
Select Subset of Individuals
176
Generate Figure
5.3
(Linear Trajectory)
178
Generate Figure
5.4
(Nonlinear Quadratic Trajectory)
180
Coding the Time-Related Variables
181
Coding Time Interval Variables {time to quaJtime)
with IBM SPSS Menu Commands
182
Coding Time Interval Variables (time to orthtime, orthquad)
with IBM SPSS Menu Commands
184
Specifying the Two-Level Model ot Individual Change
186
Level
1
Covariance Structure
188
Repeated Covariance Dialog Box
188
Л
Iodei
1.1 :
Л
Iodei
with No Predictors
191
Defining Model
1.1
(Null) with IBM SPSS Menu Commands
192
Interpreting the Output From Model
1.1
(Null)
195
Model
1.1
A: What Is the Shape of the Trajectory?
196
χ
■ Contents
Defining Model 1.1A with IBM SPSS Menu Commands
197
Interpreting the Output From Model 1.1A
199
Does the Time-Related Slope Vary Across Groups?
200
Level
2
Covariance Structure
201
Defining Model LIB with IBM SPSS Menu Commands
201
Interpreting the Output From Model LIB
203
Examining Orthogonal Components
204
Defining Model
1.2
with IBM SPSS Menu Commands
205
Interpreting the Output From Model
1.2 207
Specifying the Level
1
Covariance Structure
208
Investigating Other Level
1
Covariance Structures
209
Defining Other Level
1
Covariance Structures Using IBM SPSS
Menu Commands
210
Model
1:
ID (Level
1),
UN (Level
2) 210
Scaled Identity Covariance Matrix at Level
1 210
Unstructured Covariance Matrix at Level
2 211
Model
2:
DIAG (Level
1),
DIAG (Level
2) 211
Diagonal Covariance Matrix at Level
1 211
Diagonal Covariance Matrix at Level
2 212
Model
3:
DIAG (Level
1),
UN (Level
2) 212
Diagonal Covariance Matrix at Level
1 212
Unstructured Covariance Matrix at Level
2 213
Model
4:
ARI
(Level
1),
DIAG (Level
2) 213
Autoregressive
Errors
(ARI)
Covariance Matrix at Level
1 213
Diagonal Covariance Matrix at Level
2 214
Model
1.3:
Adding the Between-Subjects Predictors
214
Defining Model
1.3
with IBM SPSS Menu Commands
215
Add First Cross-Level Interaction to Model
1.3:
ses*orthtime
218
Add Second Cross-Level Interaction to Model
1.3:
effective^orthtime
218
Interpreting the Output From Model
1.3 219
Graphing the Results
222
Graphing the Growth Rate Trajectories with SPSS Menu Commands
223
Examining Growth Using an Alternative Specification of the
Time-Related Variable
224
Coding Time Interval Variables {time to timenonlin Variations)
with IBM SPSS Menu Commands
225
Estimating the Final Time-Related Model
227
Defining Model
2.1
with IBM SPSS Menu Commands
227
Adding the Two Predictors
229
Defining Model
2.2
with IBM SPSS Menu Commands
229
Add First Interaction to Model
2.2:
ses*timenonlin
230
Add Second Interaction to Model
2.2: eßective*iimenonlin 231
Interpreting the Output From Model
2.2 231
An Example Experimental Design
233
Summary
238
Chapter
6
Applications of Mixed Models for Longitudinal Data
239
Examining Growth in Undergraduate Graduation Rates
239
Research Questions
240
Contents ■ xi
The Data
240
Defining the Model
242
Level
1
Model
243
Level
2
Model
244
Level
3
Model
244
The Null Model: No Predictors
245
Level
1
Error Structures
246
Defining Model
1.1
(Null) with IBM SPSS Menu Commands
248
Interpreting the Output From Model
1.1
(Null)
252
Model
1.2:
Adding Growth Rates
252
Level
1
Model
253
Coding the Time Variable
254
Defining Model
1.2
with IBM SPSS Menu Commands
256
Interpreting the Output From Model
1.2 259
Model
1.3:
Adding Time-Varying Covariates
260
Defining Model
1.3
with IBM SPSS Menu Commands
261
Interpreting the Output From Model
1.3 263
Model
1.4:
Explaining Differences in Growth Trajectories Between Institutions
263
Defining Model
1.4
with IBM SPSS Menu Commands
264
Add First Interaction to Model
1.4:
timel^nathselect
266
Add Second Interaction to Model
1.4:
ämeľperam/FTfamhy
266
Interpreting the Output From Model
1.4 267
Model
1.5:
Adding a Model to Examine Growth Rates at Level
3 268
Defining Model
1.5
with IBM SPSS Menu Commands
269
Add First Interaction to Model
1.5:
limel*aveFamilyshare
270
Add Second
Interaction
to Model
1.5:
tìmel*aveRetention
270
Add 'Third Interaction to Model
1.5:
timeVmathselect
270
Add Fourth Interaction to Model
1.5:
timeľpercentFTjaculty
271
Interpreting the Output From Model
1.5 271
A Regression Discontinuity Analysis of a Math Treatment
272
The Data and Design
273
Assumptions of the Design
274
Steps in the Regression Discontinuity Analysis
275
Predictors in the Models
275
Specifying the Model
275
Regression Discontinuity Models to Explain I .earning Differences
277
Defining Model
2.1
with IBM SPSS Menu Commands
277
Interpreting the Output From Model
2.1 280
Adding Explanatory Variables at Level
2 282
Defining Model
І2
with IBM SPSS Menu Commands
282
Add First Interaction to Model
2.2:
teachqiiaľtreatmmt
283
Add Second Interaction to Model
2.2:
dasscomftreatment
283
Interpreting the Output From Model
2.2 284
Investigating a Change Due to Policy Implementation
284
The Data
286
Model
3.1:
Establishing the Prepolicy and Policy Trends
287
Defining Model
3.1
with IBM SPSS Menu Commands
288
Interpreting the Output From Model
3.1 291
Final Model with Covariates Added
292
Defining Model
3.2
with IBM SPSS Menu Commands
293
xii ■ Contents
Add
First
Interaction to
Aiodel
3.2:
imple
tmntO*pr
ivate
29'4
Add Second Interaction to Model
3.2:
impkmentO*prestige.
294
Add Third Interaction to Model
3.2:
imple.me,ntl*private.
294
Add Fourth Interaction to Model
3.2:
implementi*
prestige
29
A
Interpreting the Output From Model
3.2 295
Summary
295
Chapter
7
Multivariate Multilevel Models
297
Multilevel Latent-Outcome Model
297
The Data
298
Research Questions
299
Defining the Constructs
299
Organizing the Data Set
300
Specifying the Model
301
Model
1.1:
The Null or "No-Predictors" Model
302
Defining the Model
1.1
(Null) with IBM SPSS Menu Commands
304
Interpreting the Output From Model
1.1
(Null)
309
Conducting a Likelihood Ratio Test
311
Defining" Model
1.2
(Final Null Model) with IBM SPSS Menu Commands
312
Model
1.3:
Adding Level
2
Predictors
315
Defining Model
1.3
with IBM SPSS Menu Commands
316
Add First Interaction to Model
1.3:
stability*assessjob
318
Add Second Interaction to Model
1.3:
female*as$essjob
318
Interpreting the Output From Model
1.3 319
Model
1.4:
Adding the Organizational Predictors
320
Defining Model
1.4
with IBM SPSS Menu Commands
321
Add
Hrst
Interaction to Model
1.4:
gmorgprod*assessjob
323
Add Second Interaction to Model
1.4:
gmresources*assessjob
323
Add Third Interaction to Model
1.4:
stability*assessjob
323
Add Fourth Interaction to Model AA:female*assessjob
323
Interpreting the Output From Model
1.4 325
Examining Equality Constraints
326
Defining Model
1.5
with IBM SPSS Menu Commands
326
Investigating a Random Level
2
Slope
328
Defining Models
1.6
and
1.7
with IBM SPSS Menu Commands
329
Model
1.6 329
Model
1.7 330
Add First Interaction to Model
1.7:
gmorprod*assessjob
330
Add Second Interaction to Model
1.7:
gmresources*assessjob
331
Add Third Interaction to Model
1.7:
stability*assessjob
331
Add Fourth Interaction to Model 1.7:female*assessjob
331
Multivariate Multilevel Model for Correlated Observed Outcomes
331
The Data
331
Research Questions
331
Formulating the Basic Model
332
Model
2.1:
Null Model (No Predictors)
334
Defining Model
2.1
(Null) with IBM SPSS Menu Commands
334
Examining the Syntax Commands
339
Interpreting the Output From Model
2.1 339
Model
2.2:
Building a Complete Model (Predictors and Cross-Level
Interactions)
340
Contents ■ xiii
Defining
Model 2.2
with IBM SPSS Menu Commands
340
Add First Interaction to Model
2.2:
Indexťgmacadpress
342
Add Second Interaction to Model
2.2:
IndexlJ'emak
342
Interpreting the Output From Model
2.2 342
Testing the Hypotheses
344
Correlations Between Tests at Each Level
344
Defining Model
2.3
with IBM SPSS Menu Commands
345
Investigating a Random Slope
346
Defining a Parallel Growth Process
346
lhe
Data
346
Research Questions
347
Preparing the Data
347
Model
3.1:
Specifying the Time Model
348
Defining Model
3.1
with IBM SPSS Menu Commands
350
Add First Interaction to Model
3.1:
tnath*orthtime
352
Add Second Interaction to Model
3.1:
math*orthquadtime
352
Interpreting the Output From Model
3.1 354
Model
3.2:
Adding the Predictors
355
Defining Model
3.2
with IBM SPSS Menu Commands
355
Add
Hrst
Interaction to Model
3.2:
matbrsehcontext
356
Add Second Interaction to Model
3.2:
mathjemak
357
Add Third Interaction to Model
3.2:
math*arthtime
357
Add Fourth Interaction to Model
3.2:
matìrorthquadtìme
357
Add Fifth Interaction to Model
3.2:
schconiexrmattfbrthtitne
357
Add Sixth Interaction to Model
3.2:
female*matb*orthtime
358
Interpreting the Output From Model
3.2 358
Further Considerations
359
Defining Model
3.3
with IBM SPSS Menu Commands
360
Summary
361
Chapters Cross-Classified Multilevel Models
363
Students Cross-Classified in High Schools and Postsecondary
Institutions
363
Research Questions
364
The Data
364
Descriptive Statistics
366
Defining Models in IBM SPSS
367
Model
1.1:
Adding a Set of Level
1
and Level
2
Predictors
369
Defining Model
1.1
with IBM SPSS Menu Commands
370
Interpreting the Output From Model
1.1 374
Model
1.2:
Investigating a Random Slope
375
Defining Model
1.2
with IBM SPSS Menu Commands
376
Interpreting the Output From Model
1.2 378
Model
1.3:
Explaining Variation Between Variables
378
Defining Model
1.3
with IBM SPSS Menu Commands
379
Add Interaction to Model
1.3:
gmlo4a$ES_niearrgmfemak
380
Interpreting the Output From Model
1.3 382
Developing a Cross-Classified Teacher Effectiveness Model
383
Tlie
Data Structure and Model
383
Research Questions
384
Model
2.1:
Intercept-Only Model (Null)
385
xiv ■ Contents
Defining
Model 2.1 (Null)
with IBM SPSS Menu Commands
386
Interpreting
Output
From
Model 2.1 (Null) 390
Model 2.2:
Defining the Cross-Classified
Model
with
Previous Achievement
390
Defining Model
2.2
with IBM SPSS Menu Commands
391
Interpreting the Output From Model
2.2 393
Model
2.3:
Adding Teacher Effectiveness and a Student Background
Control
394
Defining Model
2.3
with IBM SPSS Menu Commands
395
Interpreting the Output From Model
2.3 397
Model
2.4:
Adding a School-Level Predictor and a Random Slope
398
Defining Model
2.4
with IBM SPSS Menu Commands
398
Interpreting the Output From Model
2.4 401
Model
2.5:
Examining Level
3
Differences Between Institutions
401
Defining Model
2.5
with IBM SPSS Menu Commands
402
Interpreting the Output From Model
2.5 405
Model
2.6:
Adding a Level
3
Cross-Level Interaction
405
Defining Model
2.6
with IBM SPSS Menu Commands
406
Add Interaction to Model
2.6:
effmath2*schqual
407
Interpreting the Output From Model
2.6 408
Summary
408
Chapter
9
Concluding Thoughts
409
References
413
Appendices
Appendix A: Syntax Statements
417
Appendix B: Model Comparisons Across Software Applications
435
Appendix C: Syntax Routine to Estimate Rho From Model's Variance
Components
437
Author Index
439
Subject Index
441
STATISTICS
"Ronald
H.
Heck and his colleagues have provided academics, graduate students, and practitioners with a resource that few can
surpass. This book contains excellent details for users with varying degrees of proficiency in multilevel modeling. It should be on the
bookshelf of anyone who claims to use this technique."
-Timothy Teo, The University of Auckland, New Zealand
"This book serves, not only as an introduction to using IBM SPSS for multilevel models, but as a wonderful introduction to multilevel
models through empirical example. It is a wonderful resource for an undergraduate or graduate course on multilevel modeling,
"
-Kevin Grimm, University of California, Davis, USA
"This book is ideal for individuals interested in learning about how to analyze different types of multilevel and longitudinal models using
the MIXED procedure in IBM SPPS. The book methodically progresses from the simplest of models and designs to the more advanced
ones. The presentation of statistical concepts is easy to follow, the data analysis examples are excellent, and the screen shots of the
I scripts and outputted results are thoroughly and effectively annotated.
"
і
-George A. Marcouiides, University of California, Riverside, USA
I
!
This book demonstrates how to use multilevel- and longitudinal-modeling techniques available in the IBM SPSS mixed-effects program
(MIXED). Annotated screen shots provide readers with a step-by-step understanding of each technique and navigating the program.
Readers learn how to set up, run, and interpret a variety of models. Diagnostic tools, data management issues, and related graphics are
introduced throughout. Annotated syntax is also available for those who prefer this approach. Extended examples illustrate the logic of
model development to show readers the rationale of the research questions and the steps around which the analyses are structured.
The data used in the text and syntax examples are available at www.routledge.CQm/978Q41
5817110.
Highlights
of the new edition include the following:
•
Updated throughout to reflect IBM SPSS Version
21.
•
Further coverage of growth trajectories, coding time-related variables, covariance structures, individual change, and longitudinal
experimental designs.
•
Extended discussion of other types of research designs for examining change (e.g., regression discontinuity, quasi-experimental)
over time.
•
New examples specifying multiple latent constructs and parallel growth processes.
•
Discussion of alternatives for dealing with missing data and the use of sample weights within multilevel data structures.
The book opens with the conceptual and methodological issues associated with multilevel and longitudinal modeling, followed by a
discussion of SPSS data management techniques that facilitate working with multilevel, longitudinal, and cross-classified data sets.
Chapters
3
and
4
introduce the basics of multilevel modeling: developing a multilevel model, interpreting output, and trouble-shooting
common programming and modeling problems. Models for investigating individual and organizational change are presented in Chapters
5
and
6,
followed by models with multivariate outcomes in Chapter
7.
Chapter
8
provides an illustration of multilevel models with cross-
classified data structures. The book concludes with ways to expand on the various multilevel- and longitudinal-modeling techniques
and issues when conducting multilevel analyses.
Ideal as a supplementary text for graduate courses on multilevel and longitudinal modeling, multivariate statistics, and research design
taught in education, psychology, business, and sociology, this book's practical approach also appeals to researchers in these fields. The
book provides an excellent supplement to Heck and Thomas's An introduction to Multilevel Modeling Techniques
(2nd ed.);
however, it
can also be used with any multilevel- and/or longitudinal-modeling book or as a stand-alone text. |
| any_adam_object | 1 |
| author | Heck, Ronald H. Thomas, Scott L. Tabata, Lynn N. |
| author_GND | (DE-588)14277250X |
| 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 | BV041280255 |
| classification_rvk | CM 4400 MR 2100 MR 2200 ST 601 |
| classification_tum | SOZ 720f QH 230 - QH 239 DAT 307f |
| ctrlnum | (OCoLC)859417254 (DE-599)BVBBV041280255 |
| discipline | Informatik Soziologie Psychologie |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV041280255 |
| illustrated | Illustrated |
| indexdate | 2024-12-06T09:04:00Z |
| institution | BVB |
| isbn | 9780415817103 9780415817110 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-026253704 |
| oclc_num | 859417254 |
| open_access_boolean | |
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| physical | XVII, 443 S. Ill., graph. Darst. |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Routledge |
| record_format | marc |
| series2 | Quantitative methodology series |
| spelling | Heck, Ronald H. Verfasser (DE-588)14277250X aut Multilevel and longitudinal modeling with IBM SPSS Ronald H. Heck ; Scott L. Thomas ; Lynn N. Tabata 2. ed. New York [u.a.] Routledge 2014 XVII, 443 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Quantitative methodology series SPSS (DE-588)4056588-9 gnd rswk-swf Statistisches Modell (DE-588)4121722-6 gnd rswk-swf Statistisches Modell (DE-588)4121722-6 s SPSS (DE-588)4056588-9 s DE-604 Thomas, Scott L. Verfasser aut Tabata, Lynn N. Verfasser aut Erscheint auch als Online-Ausgabe 978-0-203-70124-9 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026253704&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026253704&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Heck, Ronald H. Thomas, Scott L. Tabata, Lynn N. Multilevel and longitudinal modeling with IBM SPSS SPSS (DE-588)4056588-9 gnd Statistisches Modell (DE-588)4121722-6 gnd |
| subject_GND | (DE-588)4056588-9 (DE-588)4121722-6 |
| title | Multilevel and longitudinal modeling with IBM SPSS |
| title_auth | Multilevel and longitudinal modeling with IBM SPSS |
| title_exact_search | Multilevel and longitudinal modeling with IBM SPSS |
| title_full | Multilevel and longitudinal modeling with IBM SPSS Ronald H. Heck ; Scott L. Thomas ; Lynn N. Tabata |
| title_fullStr | Multilevel and longitudinal modeling with IBM SPSS Ronald H. Heck ; Scott L. Thomas ; Lynn N. Tabata |
| title_full_unstemmed | Multilevel and longitudinal modeling with IBM SPSS Ronald H. Heck ; Scott L. Thomas ; Lynn N. Tabata |
| title_short | Multilevel and longitudinal modeling with IBM SPSS |
| title_sort | multilevel and longitudinal modeling with ibm spss |
| topic | SPSS (DE-588)4056588-9 gnd Statistisches Modell (DE-588)4121722-6 gnd |
| topic_facet | SPSS Statistisches Modell |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026253704&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=026253704&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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