Generalizability theory:
"Generalizability theory offers an extensive conceptual framework and a powerful set of statistical procedures for characterizing and quantifying the fallibility of measurements. It liberalizes classical test theory, in part through the application of analysis of variance procedures that focus...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY [u.a.]
Springer
2001
|
| Schriftenreihe: | Statistics for social science and public policy
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "Generalizability theory offers an extensive conceptual framework and a powerful set of statistical procedures for characterizing and quantifying the fallibility of measurements. It liberalizes classical test theory, in part through the application of analysis of variance procedures that focus on variance components. As such, generalizability theory is perhaps the most broadly defined measurement model currently in existence. It is applicable to virtually any scientific field that attends to measurements and their errors, and it enables a multifaceted perspective on measurement error and its components." "This book provides the most comprehensive and up-to-date treatment of generalizability theory that is currently available. In addition, it provides a synthesis of those parts of the statistical literature that are directly applicable to generalizability theory. The principal intended audience is measurement practitioners and graduate students in the behavioral and social sciences, although a few examples and references are provided from other fields. Readers will benefit from some familiarity with classical test theory and analysis of variance, but the treatment of most topics does not presume a specific background."--BOOK JACKET. |
| Beschreibung: | Literaturverz. S.507 - 519 |
| Beschreibung: | XX, 538 S. graph. Darst. |
| ISBN: | 0387952829 |
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| 245 | 1 | 0 | |a Generalizability theory |c Robert L. Brennan |
| 264 | 1 | |a New York, NY [u.a.] |b Springer |c 2001 | |
| 300 | |a XX, 538 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Statistics for social science and public policy | |
| 500 | |a Literaturverz. S.507 - 519 | ||
| 520 | 1 | |a "Generalizability theory offers an extensive conceptual framework and a powerful set of statistical procedures for characterizing and quantifying the fallibility of measurements. It liberalizes classical test theory, in part through the application of analysis of variance procedures that focus on variance components. As such, generalizability theory is perhaps the most broadly defined measurement model currently in existence. It is applicable to virtually any scientific field that attends to measurements and their errors, and it enables a multifaceted perspective on measurement error and its components." "This book provides the most comprehensive and up-to-date treatment of generalizability theory that is currently available. In addition, it provides a synthesis of those parts of the statistical literature that are directly applicable to generalizability theory. The principal intended audience is measurement practitioners and graduate students in the behavioral and social sciences, although a few examples and references are provided from other fields. Readers will benefit from some familiarity with classical test theory and analysis of variance, but the treatment of most topics does not presume a specific background."--BOOK JACKET. | |
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Datensatz im Suchindex
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|---|---|
| adam_text |
CONTENTS
PREFACE
VII
PRINCIPAL
NOTATIONAL
CONVENTIONS
XIX
1
INTRODUCTION
1
1.1
FRAMEWORK
OF
GENERALIZABILITY
THEORY
.
4
1.1.1
UNIVERSE
OF
ADMISSIBLE
OBSERVATIONS
AND
G
STUDIES
.
5
1.1.2
INFINITE
UNIVERSE
OF
GENERALIZATION
AND
D
STUDIES
.
.
8
1.1.3
DIFFERENT
DESIGNS
AND/OR
UNIVERSES
OF
GENERALIZATION
13
1.1.4
OTHER
ISSUES
AND
APPLICATIONS
.
17
1.2
OVERVIEW
OF
BOOK
.
18
1.3
EXERCISES
.
19
2
SINGLE-FACET
DESIGNS
21
2.1
G
STUDY
P
X
I
DESIGN
.
22
2.2
G
STUDY
VARIANCE
COMPONENTS
FOR
P
X
I
DESIGN
.
24
2.2.1
ESTIMATING
VARIANCE
COMPONENTS
.
25
2.2.2
SYNTHETIC
DATA
EXAMPLE
.
28
2.3
D
STUDIES
FOR
THE
P
X
I
DESIGN
.
29
2.3.1
ERROR
VARIANCES
.
31
2.3.2
COEFFICIENTS
.
34
2.3.3
SYNTHETIC
DATA
EXAMPLE
.
35
XII
CONTENTS
2.3.4
REAL-DATA
EXAMPLES
.
37
2.4
NESTED
DESIGNS
.
39
2.4.1
NESTING
IN
BOTH
THE
G
AND
D
STUDIES
.
40
2.4.2
NESTING
IN
THE
D
STUDY,
ONLY
.
44
2.5
SUMMARY
AND
OTHER
ISSUES
.
45
2.5.1
OTHER
INDICES
AND
COEFFICIENTS
.
47
2.5.2
TOTAL
SCORE
METRIC
.
48
2.6
EXERCISES
.
50
3
MULTIFACET
UNIVERSES
OF
ADMISSIBLE
OBSERVATIONS
AND
G
STUDY
DESIGNS
53
3.1
TWO-FACET
UNIVERSES
AND
DESIGNS
.
54
3.1.1
VENN
DIAGRAMS
.
54
3.1.2
ILLUSTRATIVE
DESIGNS
AND
UNIVERSES
.
57
3.2
LINEAR
MODELS,
SCORE
EFFECTS,
AND
MEAN
SCORES
.
60
3.2.1
NOTATIONAL
SYSTEM
FOR
MAIN
AND
INTERACTION
EFFECTS
61
3.2.2
LINEAR
MODELS
.
63
3.2.3
EXPRESSING
A
SCORE
EFFECT
IN
TERMS
OF
MEAN
SCORES
.
66
3.3
TYPICAL
ANOVA
COMPUTATIONS
.
67
3.3.1
OBSERVED
MEAN
SCORES
AND
SCORE
EFFECTS
.
67
3.3.2
SUMS
OF
SQUARES
AND
MEAN
SQUARES
.
69
3.3.3
SYNTHETIC
DATA
EXAMPLES
.
70
3.4
RANDOM
EFFECTS
VARIANCE
COMPONENTS
.
74
3.4.1
DEFINING
AND
INTERPRETING
VARIANCE
COMPONENTS
.
.
74
3.4.2
EXPECTED
MEAN
SQUARES
.
76
3.4.3
ESTIMATING
VARIANCE
COMPONENTS
USING
THE
EMS
PROCEDURE
.
79
3.4.4
ESTIMATING
VARIANCE
COMPONENTS
DIRECTLY
FROM
MEAN
SQUARES
.
80
3.4.5
SYNTHETIC
DATA
EXAMPLES
.
83
3.4.6
NEGATIVE
ESTIMATES
OF
VARIANCE
COMPONENTS
.
84
3.5
VARIANCE
COMPONENTS
FOR
OTHER
MODELS
.
85
3.5.1
MODEL
RESTRICTIONS
AND
DEFINITIONS
OF
VARIANCE
COM
PONENTS
.
86
3.5.2
EXPECTED
MEAN
SQUARES
.
89
3.5.3
OBTAINING
R
2
(A|M)
FROM
O
2
(A)
.
89
3.5.4
EXAMPLE:
APL
PROGRAM
.
90
3.6
EXERCISES
.
92
4
MULTIFACET
UNIVERSES
OF
GENERALIZATION
AND
D
STUDY
DESIGNS
95
4.1
RANDOM
MODELS
AND
INFINITE
UNIVERSES
OF
GENERALIZATION
.
.
96
4.1.1
UNIVERSE
SCORE
AND
ITS
VARIANCE
.
97
4.1.2
D
STUDY
VARIANCE
COMPONENTS
.
100
4.1.3
ERROR
VARIANCES
.
100
CONTENTS
XIII
4.1.4
COEFFICIENTS
AND
SIGNAL-NOISE
RATIOS
.
104
4.1.5
VENN
DIAGRAMS
.
107
4.1.6
RULES
AND
EQUATIONS
FOR
ANY
OBJECT
OF
MEASUREMENT
108
4.1.7
D
STUDY
DESIGN
STRUCTURES
DIFFERENT
FROM
THE
G
STUDY
.
109
4.2
RANDOM
MODEL
EXAMPLES
.
110
4.2.1
P
X
I
X
O
DESIGN
WITH
SYNTHETIC
DATA
SET
NO.
3
.
.
110
4.2.2
P
X
(I
:
O)
DESIGN
WITH
SYNTHETIC
DATA
SET
NO.
3
.
.
113
4.2.3
P
X
(7?:
T)
DESIGN
WITH
SYNTHETIC
DATA
SET
NO.
4
.
.
115
4.2.4
PERFORMANCE
ASSESSMENTS
.
117
4.3
SIMPLIFIED
PROCEDURES
FOR
MIXED
MODELS
.
120
4.3.1
RULES
.
121
4.3.2
VENN
DIAGRAMS
.
122
4.4
MIXED
MODEL
EXAMPLES
.
125
4.4.1
P
X
I
X
O
DESIGN
WITH
ITEMS
FIXED
.
125
4.4.2
P
X
(R'.T)
DESIGN
WITH
TASKS
FIXED
.
125
4.4.3
PERSPECTIVES
ON
TRADITIONAL
RELIABILITY
COEFFICIENTS
.
127
4.4.4
GENERALIZABILITY
OF
CLASS
MEANS
.
130
4.4.5
A
PERSPECTIVE
ON
VALIDITY
.
132
4.5
SUMMARY
AND
OTHER
ISSUES
.
135
4.6
EXERCISES
.
136
5
ADVANCED
TOPICS
IN
UNIVARIATE
GENERALIZABILITY
THEORY
141
5.1
GENERAL
PROCEDURES
FOR
D
STUDIES
.
141
5.1.1
D
STUDY
VARIANCE
COMPONENTS
.
142
5.1.2
UNIVERSE
SCORE
VARIANCE
AND
ERROR
VARIANCES
.
144
5.1.3
EXAMPLES
.
145
5.1.4
HIDDEN
FACETS
.
149
5.2
STRATIFIED
OBJECTS
OF
MEASUREMENT
.
153
5.2.1
RELATIONSHIPS
AMONG
VARIANCE
COMPONENTS
.
155
5.2.2
COMMENTS
.
156
5.3
CONVENTIONAL
WISDOM
ABOUT
GROUP
MEANS
.
157
5.3.1
TWO
RANDOM
FACETS
.
157
5.3.2
ONE
RANDOM
FACET
.
159
5.4
CONDITIONAL
STANDARD
ERRORS
OF
MEASUREMENT
.
159
5.4.1
SINGLE-FACET
DESIGNS
.
160
5.4.2
MULTIFACET
RANDOM
DESIGNS
.
164
5.5
OTHER
ISSUES
.
165
5.5.1
COVARIANCES
AS
ESTIMATORS
OF VARIANCE
COMPONENTS
.
166
5.5.2
ESTIMATORS
OF
UNIVERSE
SCORES
.
168
5.5.3
RANDOM
SAMPLING
ASSUMPTIONS
.
171
5.5.4
GENERALIZABILITY
AND
OTHER
THEORIES
.
174
5.6
EXERCISES
.
175
XIV
CONTENTS
6
VARIABILITY
OF
STATISTICS
IN
GENERALIZABILITY
THEORY
179
6.1
STANDARD
ERRORS
OF
ESTIMATED
VARIANCE
COMPONENTS
.
180
6.1.1
NORMAL
PROCEDURE
.
181
6.1.2
JACKKNIFE
PROCEDURE
.
182
6.1.3
BOOTSTRAP
PROCEDURE
.
185
6.2
CONFIDENCE
INTERVALS
FOR
ESTIMATED
VARIANCE
COMPONENTS
.
190
6.2.1
NORMAL
PROCEDURE
.
190
6.2.2
SATTERTHWAITE
PROCEDURE
.
190
6.2.3
TING
ET
AL.
PROCEDURE
.
191
6.2.4
JACKKNIFE
PROCEDURE
.
195
6.2.5
BOOTSTRAP
PROCEDURE
.
196
6.3
VARIABILITY
OF
D
STUDY
STATISTICS
.
196
6.3.1
ABSOLUTE
ERROR
VARIANCE
.
197
6.3.2
FELDT
CONFIDENCE
INTERVAL
FOR
EP
2
.
198
6.3.3
ARTEAGA
ET
AL.
CONFIDENCE
INTERVAL
FOR
$
.
200
6.4
SOME
SIMULATION
STUDIES
.
201
6.4.1
G
STUDY
VARIANCE
COMPONENTS
.
201
6.4.2
D
STUDY
STATISTICS
.
205
6.5
DISCUSSION
AND
OTHER
ISSUES
.
208
6.6
EXERCISES
.
211
7
UNBALANCED
RANDOM
EFFECTS
DESIGNS
215
7.1
G
STUDY
ISSUES
.
216
7.1.1
ANALOGOUS-ANOVA
PROCEDURE
.
217
7.1.2
UNBALANCED
I:P
DESIGN
.
220
7.1.3
UNBALANCED
P
X
(I
:
H)
DESIGN
.
222
7.1.4
MISSING
DATA
IN
THE
P
X
I
DESIGN
.
225
7.2
D
STUDY
ISSUES
.
227
7.2.1
UNBALANCED
I:P
DESIGN
.
228
7.2.2
UNBALANCED
P
X
(I:H)
DESIGN
.
231
7.2.3
UNBALANCED
(P:C)
X
I
DESIGN
.
233
7.2.4
MISSING
DATA
IN
THE
P
X
I
DESIGN
.
235
7.2.5
METRIC
MATTERS
.
237
7.3
OTHER
TOPICS
.
240
7.3.1
ESTIMATION
PROCEDURES
.
241
7.3.2
COMPUTER
PROGRAMS
.
245
7.4
EXERCISES
.
247
8
UNBALANCED
RANDOM
EFFECTS
DESIGNS
-
EXAMPLES
249
8.1
ACT
SCIENCE
REASONING
.
249
8.2
DISTRICT
MEANS
FOR
ITED
VOCABULARY
.
251
8.3
CLINICAL
CLERKSHIP
PERFORMANCE
.
257
8.4
TESTLETS
.
262
8.5
EXERCISES
.
265
CONTENTS
XV
9
MULTIVARIATE
G
STUDIES
267
9.1
INTRODUCTION
.
268
9.2 G
STUDY
DESIGNS
.
273
9.2.1
SINGLE-FACET
DESIGNS
.
275
9.2.2
TWO-FACET
CROSSED
DESIGNS
.
278
9.2.3
TWO-FACET
NESTED
DESIGNS
.
281
9.3
DEFINING
COVARIANCE
COMPONENTS
.
284
9.4
ESTIMATING
COVARIANCE
COMPONENTS
FOR
BALANCED
DESIGNS
.
286
9.4.1
AN
ILLUSTRATIVE
DERIVATION
.
287
9.4.2
GENERAL
EQUATIONS
.
289
9.4.3
STANDARD
ERRORS
OF
ESTIMATED
COVARIANCE
COMPONENTS
.
293
9.5
DISCUSSION
AND
OTHER
TOPICS
.
295
9.5.1
INTERPRETING
COVARIANCE
COMPONENTS
.
295
9.5.2
COMPUTER
PROGRAMS
.
297
9.6
EXERCISES
.
298
10
MULTIVARIATE
D
STUDIES
301
10.1
UNIVERSES
OF
GENERALIZATION
AND
D
STUDIES
.
301
10.1.1
VARIANCE-COVARIANCE
MATRICES
FOR
D
STUDY
EFFECTS
.
302
10.1.2
VARIANCE-COVARIANCE
MATRICES
FOR
UNIVERSE
SCORES
AND
ERRORS
OF
MEASUREMENT
.
303
10.1.3
COMPOSITES
AND
A
PRIORI
WEIGHTS
.
305
10.1.4
COMPOSITES
AND
EFFECTIVE
WEIGHTS
.
306
10.1.5
COMPOSITES
AND
ESTIMATION
WEIGHTS
.
307
10.1.6
SYNTHETIC
DATA
EXAMPLE
.
308
10.2
OTHER
TOPICS
.
310
10.2.1
STANDARD
ERRORS
OF
ESTIMATED
COVARIANCE
COMPONENTS
.
310
10.2.2
OPTIMALITY
ISSUES
.
312
10.2.3
CONDITIONAL
STANDARD
ERRORS
OF
MEASUREMENT
FOR
COMPOSITES
.
314
10.2.4
PROFILES
AND
OVERLAPPING
CONFIDENCE
INTERVALS
.
317
10.2.5
EXPECTED
WITHIN-PERSON
PROFILE
VARIABILITY
.
320
10.2.6
HIDDEN
FACETS
.
324
10.2.7
COLLAPSED
FIXED
FACETS
IN
MULTIVARIATE
ANALYSES
.
.
326
10.2.8
COMPUTER
PROGRAMS
.
328
10.3
EXAMPLES
.
328
10.3.1
ACT
ASSESSMENT
MATHEMATICS
.
328
10.3.2
PAINTING
ASSESSMENT
.
334
10.3.3
LISTENING
AND
WRITING
ASSESSMENT
.
339
10.4
EXERCISES
.
343
XVI
CONTENTS
11
MULTIVARIATE
UNBALANCED
DESIGNS
347
11.1
ESTIMATING
G
STUDY
COVARIANCE
COMPONENTS
.
347
11.1.1
OBSERVED
COVARIANCE
.
349
11.1.2
ANALOGOUS
TP
TERMS
.
349
11.1.3
CP
TERMS
.
353
11.1.4
COMPOUND
MEANS
.
359
11.1.5
VARIANCE
OF
A
SUM
.
360
11.1.6
MISSING
DATA
.
363
11.1.7
CHOOSING
A
PROCEDURE
.
366
11.2
EXAMPLES
OF
G
STUDY
AND
D
STUDY
ISSUES
.
367
11.2.1
ITBS
MAPS
AND
DIAGRAMS
TEST
.
367
11.2.2
ITED
LITERARY
MATERIALS
TEST
.
373
11.2.3
DISTRICT
MEAN
DIFFERENCE
SCORES
.
380
11.3
DISCUSSION
AND
OTHER
TOPICS
.
388
11.4
EXERCISES
.
388
12
MULTIVARIATE
REGRESSED
SCORES
391
12.1
MULTIPLE
LINEAR
REGRESSION
.
392
12.2
ESTIMATING
PROFILES
THROUGH
REGRESSION
.
395
12.2.1
TWO
INDEPENDENT
VARIABLES
.
395
12.2.2
SYNTHETIC
DATA
EXAMPLE
.
401
12.2.3
VARIANCES
AND
COVARIANCES
OF
REGRESSED
SCORES
.
.
.
404
12.2.4
STANDARD
ERRORS
OF
ESTIMATE
AND
TOLERANCE
INTERVALS
407
12.2.5
DIFFERENT
SAMPLE
SIZES
AND/OR
DESIGNS
.
409
12.2.6
EXPECTED
WITHIN-PERSON
PROFILE
VARIABILITY
.
412
12.3
PREDICTED
COMPOSITES
.
415
12.3.1
DIFFERENCE
SCORES
.
417
12.3.2
SYNTHETIC
DATA
EXAMPLE
.
419
12.3.3
DIFFERENT
SAMPLE
SIZES
AND/OR
DESIGNS
.
421
12.3.4
RELATIONSHIPS
WITH
ESTIMATED
PROFILES
.
422
12.3.5
OTHER
ISSUES
.
424
12.4
COMMENTS
.
426
12.5
EXERCISES
.
427
APPENDICES
431
A.
DEGREES
OF
FREEDOM
AND
SUMS
OF
SQUARES
FOR
SELECTED
BAL
ANCED
DESIGNS
.
431
B.
EXPECTED
MEAN
SQUARES
AND
ESTIMATORS
OF
RANDOM
EFFECTS
VARIANCE
COMPONENTS
FOR
SELECTED
BALANCED
DESIGNS
.
435
C.
MATRIX
PROCEDURES
FOR
ESTIMATING
VARIANCE
COMPONENTS
AND
THEIR
VARIABILITY
.
439
D.
TABLE
FOR
SIMPLIFIED
USE
OF
SATTERTHWAITE
'
S
PROCEDURE
.
445
E.
FORMULAS
FOR
SELECTED
UNBALANCED
RANDOM
EFFECTS
DESIGNS
449
F.
MINI-MANUAL
FOR
GENOVA
.
453
G.
URGENOVA
.
471
CONTENTS
XVII
H.
MGENOVA
.
473
I.
ANSWERS
TO
SELECTED
EXERCISES
.
475
REFERENCES
507
AUTHOR
INDEX
521
SUBJECT
INDEX
525 |
| any_adam_object | 1 |
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| dewey-search | 150/.1/5195 |
| dewey-sort | 3150 11 45195 |
| dewey-tens | 150 - Psychology |
| discipline | Soziologie Psychologie Mathematik |
| format | Book |
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| id | DE-604.BV014038768 |
| illustrated | Illustrated |
| indexdate | 2024-08-23T00:42:34Z |
| institution | BVB |
| isbn | 0387952829 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-009613103 |
| oclc_num | 46866316 |
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| owner_facet | DE-824 DE-20 DE-11 DE-188 DE-91 DE-BY-TUM |
| physical | XX, 538 S. graph. Darst. |
| publishDate | 2001 |
| publishDateSearch | 2001 |
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| publisher | Springer |
| record_format | marc |
| series2 | Statistics for social science and public policy |
| spelling | Brennan, Robert L. Verfasser (DE-588)1047378590 aut Generalizability theory Robert L. Brennan New York, NY [u.a.] Springer 2001 XX, 538 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Statistics for social science and public policy Literaturverz. S.507 - 519 "Generalizability theory offers an extensive conceptual framework and a powerful set of statistical procedures for characterizing and quantifying the fallibility of measurements. It liberalizes classical test theory, in part through the application of analysis of variance procedures that focus on variance components. As such, generalizability theory is perhaps the most broadly defined measurement model currently in existence. It is applicable to virtually any scientific field that attends to measurements and their errors, and it enables a multifaceted perspective on measurement error and its components." "This book provides the most comprehensive and up-to-date treatment of generalizability theory that is currently available. In addition, it provides a synthesis of those parts of the statistical literature that are directly applicable to generalizability theory. The principal intended audience is measurement practitioners and graduate students in the behavioral and social sciences, although a few examples and references are provided from other fields. Readers will benefit from some familiarity with classical test theory and analysis of variance, but the treatment of most topics does not presume a specific background."--BOOK JACKET. Generalisatie gtt Psychometrie gtt Variantieanalyse gtt Analysis of variance Psychology Statistical methods Psychometrics Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Psychometrie (DE-588)4176236-8 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Statistik (DE-588)4056995-0 s Sozialwissenschaften (DE-588)4055916-6 s DE-604 Psychometrie (DE-588)4176236-8 s DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009613103&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Brennan, Robert L. Generalizability theory Generalisatie gtt Psychometrie gtt Variantieanalyse gtt Analysis of variance Psychology Statistical methods Psychometrics Sozialwissenschaften (DE-588)4055916-6 gnd Psychometrie (DE-588)4176236-8 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4055916-6 (DE-588)4176236-8 (DE-588)4056995-0 |
| title | Generalizability theory |
| title_auth | Generalizability theory |
| title_exact_search | Generalizability theory |
| title_full | Generalizability theory Robert L. Brennan |
| title_fullStr | Generalizability theory Robert L. Brennan |
| title_full_unstemmed | Generalizability theory Robert L. Brennan |
| title_short | Generalizability theory |
| title_sort | generalizability theory |
| topic | Generalisatie gtt Psychometrie gtt Variantieanalyse gtt Analysis of variance Psychology Statistical methods Psychometrics Sozialwissenschaften (DE-588)4055916-6 gnd Psychometrie (DE-588)4176236-8 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Generalisatie Psychometrie Variantieanalyse Analysis of variance Psychology Statistical methods Psychometrics Sozialwissenschaften Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009613103&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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