Model-based recursive partitioning meets item response theory: new statistical methods for the detection of differential item functioning and appropriate anchor selection
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Hochschulschrift/Dissertation Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
München
Dr. Hut
2013
|
| Ausgabe: | 1. Aufl. |
| Schlagwörter: | |
| Links: | http://edoc.ub.uni-muenchen.de/16434/ https://nbn-resolving.org/urn:nbn:de:bvb:19-164348 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027037191&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | III, 187 S. 210 mm x 148 mm, 313 g |
| ISBN: | 9783843913331 |
Internformat
MARC
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| 100 | 1 | |a Kopf, Julia |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Model-based recursive partitioning meets item response theory |b new statistical methods for the detection of differential item functioning and appropriate anchor selection |c Julia Kopf |
| 250 | |a 1. Aufl. | ||
| 264 | 1 | |a München |b Dr. Hut |c 2013 | |
| 300 | |a III, 187 S. |c 210 mm x 148 mm, 313 g | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 502 | |a Zugl.: Universität München, Diss., 2013 | ||
| 650 | 0 | 7 | |a Rasch-Modell |0 (DE-588)4176964-8 |2 gnd |9 rswk-swf |
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Datensatz im Suchindex
| DE-BY-TUM_katkey | 1993242 |
|---|---|
| _version_ | 1821935217499176960 |
| adam_text | CONTENTS
1 SCOPE OF THIS WORK 1
2 MODEL-BASED RECURSIVE PARTITIONING IN THE SOCIAL SCIENCES 9
2.1 INTRODUCTION 9
2.2 CLASSIFICATION AND REGRESSION
TREES 10
2.2.1 BASIC PRINCIPLES OF CLASSIFICATION AND REGRESSION TREES 11
2.2.2 SOME TECHNICAL DETAILS 13
2.3 FROM CLASSIFICATION AND REGRESSION TREES TO MODEL-BASED RECURSIVE
PARTITIONING . IS
2.3.1 BASIC PRINCIPLES OF MODEL-BASED RECURSIVE PARTITIONING 16
2.3.2 SOME TECHNICAL DETAILS 18
2.4 POTENTIAL IN THE SOCIAL SCIENCES 21
2.5 EMPIRICAL EXAMPLE 22
2.6 SOFTWARE 24
2.7 CONCLUDING REMARKS 24
3 THE ISSUE OF DIFFERENTIAL ITEM FUNCTIONING IN THE RASCH MODEL 27
3.1 THE RASCH MODEL 27
3.2 DIFFERENTIAL ITEM FUNCTIONING 31
3.2.1 HISTORICAL DEVELOPMENT AND
TEST FAIRNESS 31
3.2.2 UNIFORM AND NON-UNIFORM DIFFERENTIAL ITEM
FUNCTIONING 34
3.3 METHODS TO DETECT
DIF OR DIF GROUPS 38
3.3.1 GLOBAL MODEL TESTS 38
3.3.2 METHODS TO DETECT ITEM-WISE
UNIFORM DIF 39
3.3.3 METHODS TO DETECT NON-UNIFORM
DIF 41
3.4 RASCH TREES FOR UNIFORM DIF
DETECTION 42
4 DETECTING NON-UNIFORM OIF WITH RASCH TREES 49
4.1 INTRODUCTION 49
4.2 METHODS 50
4.2.1 LOGISTIC REGRESSION 52
4.2.2 RASCH TREES 53
4.2.3 EXTENSIONS OF THE LOGISTIC REGRESSION 57
4.3 SIMULATION STUDY 60
4.3.1 MANIPULATED VARIABLES 61
4.3.2 DATA GENERATING PROCESSES 62
4.3.3 OUTCOME VARIABLES 64
4.4 RESULTS 65
4.4.1 NO DIF 65
4.4.2 UNIFORM DIF 66
4.4.3 NON-UNIFORM DIF: JUMP
CONDITION 68
HTTP://D-NB.INFO/1045688541
II CONTENTS
4.4.4 NON-UNIFORM DIF: DISCRIMINATION CONDITION 70
4.4.5 ON THE CLASSIFICATION ACCURACY 70
4.4.6 ON THE DETECTION OF DIF GROUPS 75
4.5 DISCUSSION 76
5 A FRAMEWORK FOR ANCHOR METHODS 79
5.1 INTRODUCTION 79
5.2 THE ANCHOR PROCESS
FOR THE RASCH MODEL 80
5.2.1 SCALE INDETERMINACY 80
5.2.2 ITEM-WISE WALD TEST 81
5.2.3 ILLUSTRATION OF ARTIFICIAL DIF 82
5.3 A CONCEPTUAL FRAMEWORK FOR
ANCHOR METHODS 84
5.3.1 ANCHOR CLASSES 84
5.3.2 ANCHOR SELECTION STRATEGIES 85
5.3.3 ANCHOR METHODS 86
5.4 APPENDIX: PRELIMINARY SIMULATION STUDY 87
6 ANCHOR CLASSES FOR DIF DETECTION IN THE RASCH MODEL 91
6.1 INTRODUCTION 91
6.2 THE ITERATIVE
FORWARD CLASS AND COMPARISON METHODS 92
6.2.1 THE ITERATIVE FORWARD
ANCHOR CLASS AND COMPARISON ANCHOR
CLASSES ... 92
6.2.2 ANCHOR SELECTION STRATEGIES 93
6.2.3 ANCHOR METHODS 93
6.3 BACKGROUND OF THE SIMULATION STUDY 96
6.4 SIMULATION STUDY 98
6.4.1 DATA GENERATING PROCESS 98
6.4.2 MANIPULATED VARIABLES 99
6.4.3 OUTCOME VARIABLES 99
6.5 RESULTS 100
6.5.1 NULL HYPOTHESIS: NO DIF 100
6.5.2 BALANCED DIF: NO ADVANTAGE FOR
ONE GROUP 102
6.5.3 UNBALANCED DIF: ADVANTAGE
FOR THE FOCAL
GROUP 104
6.6 THE IMPACT OF ANCHOR
CONTAMINATION 106
6.7 CHARACTERISTICS OF THE ANCHOR ITEMS INDUCING ARTIFICIAL
DIF 109
6.8 SUMMARY AND DISCUSSION 112
6.9 APPENDIX 116
7 ANCHOR SELECTION STRATEGIES FOR DIF ANALYSIS IN THE RASCH MODEL 123
7.1 INTRODUCTION 123
7.2 ANCHOR METHODS 125
7.2.1 THE ANCHOR PROCESS 125
7.2.2 ANCHOR CLASSES 126
CONTENTS III
7.2.3 ANCHOR SELECTION STRATEGIES 128
7.3 SIMULATION STUDY 133
7.3.1 DATA GENERATING PROCESSES 133
7.3.2 MANIPULATED VARIABLES 134
7.3.3 OUTCOME VARIABLES 135
7.4 RESULTS . 135
7.4.1 ANCHOR SELECTION FOR THE
CONSTANT FOUR
ANCHOR CLASS 136
7.4.2 ANCHOR SELECTION FOR
THE ITERATIVE FORWARD ANCHOR
CLASS 138
7.4.3 COMPARISON OF THE MEAN TEST
STATISTIC AND P-VALUE THRESHOLD SELECTION . 140
7.4.4 COMPARISON OF THE BEST PERFORMING METHODS 141
7.4.5 FURTHER SIMULATED SETTINGS 143
7.5 DISCUSSION AND PRACTICAL RECOMMENDATIONS 145
8 OUTLOOK ON ANCHOR SELECTION STRATEGIES FOR MULTIPLE GROUP COMPARISONS
149
8.1 INTRODUCTION 149
8.2 ALTERNATIVE I: SELECTION OF AN ANCHOR SET FOR
EACH PAIRED COMPARISON 150
8.3 ALTERNATIVE II:
SELECTION OF A COMMON ANCHOR SET 151
8.4 DISCUSSION AND FUTURE RESEARCH
QUESTIONS 153
9 ALTERNATIVE IDEAS 155
9.1 INTRODUCTION 155
9.2 QUASI-VARIANCES 155
9.2.1 BASIC IDEA OF QUASI-VARIANCES 156
9.2.2 QUASI-VARIANCES IN DIF
DETECTION 156
9.2.3 SIMULATION STUDY 157
9.2.4 RESULTS: QUASI-WALD TESTS VERSUS WALD TESTS 159
9.2.5 RESULTS: CLASSIFICATION OF THE FIRST ANCHOR ITEM 161
9.3 DISCUSSION 163
9.4 APPENDIX 164
10 CONCLUDING REMARKS AND OUTLOOK 169
10.1 CONCLUDING SUMMARY AND OVERVIEW 169
10.1.1 SELECTION OF SCIENTIFIC FINDINGS 170
10.1.2 LIMITS OF THIS WORK 171
10.2 FUTURE RESEARCH 172
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| physical | III, 187 S. 210 mm x 148 mm, 313 g |
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| publishDate | 2013 |
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| spellingShingle | Kopf, Julia Model-based recursive partitioning meets item response theory new statistical methods for the detection of differential item functioning and appropriate anchor selection Rasch-Modell (DE-588)4176964-8 gnd Psychometrie (DE-588)4176236-8 gnd |
| subject_GND | (DE-588)4176964-8 (DE-588)4176236-8 (DE-588)4113937-9 |
| title | Model-based recursive partitioning meets item response theory new statistical methods for the detection of differential item functioning and appropriate anchor selection |
| title_auth | Model-based recursive partitioning meets item response theory new statistical methods for the detection of differential item functioning and appropriate anchor selection |
| title_exact_search | Model-based recursive partitioning meets item response theory new statistical methods for the detection of differential item functioning and appropriate anchor selection |
| title_full | Model-based recursive partitioning meets item response theory new statistical methods for the detection of differential item functioning and appropriate anchor selection Julia Kopf |
| title_fullStr | Model-based recursive partitioning meets item response theory new statistical methods for the detection of differential item functioning and appropriate anchor selection Julia Kopf |
| title_full_unstemmed | Model-based recursive partitioning meets item response theory new statistical methods for the detection of differential item functioning and appropriate anchor selection Julia Kopf |
| title_short | Model-based recursive partitioning meets item response theory |
| title_sort | model based recursive partitioning meets item response theory new statistical methods for the detection of differential item functioning and appropriate anchor selection |
| title_sub | new statistical methods for the detection of differential item functioning and appropriate anchor selection |
| topic | Rasch-Modell (DE-588)4176964-8 gnd Psychometrie (DE-588)4176236-8 gnd |
| topic_facet | Rasch-Modell Psychometrie Hochschulschrift |
| url | http://edoc.ub.uni-muenchen.de/16434/ https://nbn-resolving.org/urn:nbn:de:bvb:19-164348 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027037191&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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