The theory and practice of item response theory:
Item response theory (IRT) is a latent variable modeling approach used to minimize bias and optimize the measurement power of educational and psychological tests and other psychometric applications. Designed for advanced students, researchers, and psychometric professionals, this book clearly presen...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York [u. a.]
Guilford Press
2009
|
| Schriftenreihe: | Methodology in the social sciences
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | Item response theory (IRT) is a latent variable modeling approach used to minimize bias and optimize the measurement power of educational and psychological tests and other psychometric applications. Designed for advanced students, researchers, and psychometric professionals, this book clearly presents both the 'how-to' and the 'why' of IRT. It describes simple and more complex IRT models and shows how they are applied with the help of widely available software packages. The chapters follow a consistent format and build sequentially, taking the reader from model development through the fit analysis and interpretation phases that one would perform in practice. The use of common empirical data sets across the chapters facilitates understanding of the various models and how they relate to one another. |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XV, 448 S. graph. Darst. |
| ISBN: | 9781593858698 |
Internformat
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| 520 | |a Item response theory (IRT) is a latent variable modeling approach used to minimize bias and optimize the measurement power of educational and psychological tests and other psychometric applications. Designed for advanced students, researchers, and psychometric professionals, this book clearly presents both the 'how-to' and the 'why' of IRT. It describes simple and more complex IRT models and shows how they are applied with the help of widely available software packages. The chapters follow a consistent format and build sequentially, taking the reader from model development through the fit analysis and interpretation phases that one would perform in practice. The use of common empirical data sets across the chapters facilitates understanding of the various models and how they relate to one another. | ||
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| 650 | 4 | |a Sozialwissenschaften | |
| 650 | 4 | |a Item response theory | |
| 650 | 4 | |a Social sciences |x Mathematical models | |
| 650 | 4 | |a Social sciences |x Statistical methods | |
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Datensatz im Suchindex
| _version_ | 1804138525908533248 |
|---|---|
| adam_text | IMAGE 1
CONTENTS
SYMBOLS AND ACRONYMS XIII
1 * INTRODUCTION TO MEASUREMENT 1
MEASUREMENT 1 SOME MEASUREMENT ISSUES 3 ITEM RESPONSE THEORY 4 CLASSICAL
TEST THEORY 5 LATENT CLASS ANALYSIS 7
SUMMARY 9
2 * THE ONE-PARAMETER MODEL 11
CONCEPTUAL DEVELOPMENT OF THE RASCH MODEL 11 THE ONE-PARAMETER MODEL 16
THE ONE-PARAMETER LOGISTIC MODEL AND THE RASCH MODEL 19 ASSUMPTIONS
UNDERLYING THE MODEL 20 AN EMPIRICAL DATA SET: THE MATHEMATICS DATA SET
21
CONCEPTUALLY ESTIMATING AN INDIVIDUAL S LOCATION 22 SOME PRAGMATIC
CHARACTERISTICS OF MAXIMUM LIKELIHOOD ESTIMATES 26 THE STANDARD ERROR OF
ESTIMATE AND INFORMATION 27 AN INSTRUMENT S ESTIMATION CAPACITY 31
SUMMARY 34
3 * JOINT MAXIMUM LIKELIHOOD PARAMETER ESTIMATION 39
JOINT MAXIMUM LIKELIHOOD ESTIMATION 39 INDETERMINACY OF PARAMETER
ESTIMATES 41 HOW LARGE A CALIBRATION SAMPLE? 42 EXAMPLE: APPLICATION OF
THE RASCH MODEL TO THE MATHEMATICS DATA, JMLE 43
SUMMARY 64
4 * MARGINAL MAXIMUM LIKELIHOOD PARAMETER ESTIMATION 68
MARGINAL MAXIMUM LIKELIHOOD ESTIMATION 68 ESTIMATING AN INDIVIDUAL S
LOCATION: EXPECTED A POSTERIORI 75 EXAMPLE: APPLICATION OF THE RASCH
MODEL TO THE MATHEMATICS DATA, MMLE 80 METRIC TRANSFORMATION AND THE
TOTAL CHARACTERISTIC FUNCTION 92 SUMMARY 96
IMAGE 2
5 * THE TWO-PARAMETER MODEL 99
CONCEPTUAL DEVELOPMENT OF THE TWO-PARAMETER MODEL 99 INFORMATION FOR THE
TWO-PARAMETER MODEL 101 CONCEPTUAL PARAMETER ESTIMATION FOR THE 2PL
MODEL 103 HOW LARGE A CALIBRATION SAMPLE? 104 METRIC TRANSFORMATION, 2PL
MODEL 106 EXAMPLE: APPLICATION OF THE 2PL MODEL TO THE MATHEMATICS DATA,
MMLE 107
FIT ASSESSMENT: AN ALTERNATIVE APPROACH FOR ASSESSING INVARIANCE 110
INFORMATION AND RELATIVE EFFICIENCY 114 SUMMARY 118
6 * THE THREE-PARAMETER MODEL 123
CONCEPTUAL DEVELOPMENT OF THE THREE-PARAMETER MODEL 123 ADDITIONAL
COMMENTS ABOUT THE PSEUDO-GUESSING PARAMETER, /; 126 CONCEPTUAL
PARAMETER ESTIMATION FOR THE 3PL MODEL 127 HOW LARGE A CALIBRATION
SAMPLE? 130 ASSESSING CONDITIONAL INDEPENDENCE 131
EXAMPLE: APPLICATION OF THE 3PL MODEL TO THE MATHEMATICS DATA, MMLE 134
ASSESSING PERSON FIT: APPROPRIATENESS MEASUREMENT 142 INFORMATION FOR
THE THREE-PARAMETER MODEL 144 METRIC TRANSFORMATION, 3PL MODEL 147
HANDLING MISSING RESPONSES 148 ISSUES TO CONSIDER IN SELECTING AMONG THE
1PL, 2PL, AND 3PL MODELS 152 SUMMARY 154
7 * RASCH MODELS FOR ORDERED POLYTOMOUS DATA 162
CONCEPTUAL DEVELOPMENT OF THE PARTIAL CREDIT MODEL 163 CONCEPTUAL
PARAMETER ESTIMATION OF THE PC MODEL 169 EXAMPLE: APPLICATION OF THE PC
MODEL TO A REASONING ABILITY INSTRUMENT, MMLE 169
THE RATING SCALE MODEL 179 CONCEPTUAL ESTIMATION OF THE RS MODEL 184
EXAMPLE: APPLICATION OF THE RS MODEL TO AN ATTITUDES TOWARDS CONDOMS
SCALE, JMLE 184
HOW LARGE A CALIBRATION SAMPLE? 198 INFORMATION FOR THE PC AND RS MODELS
200 METRIC TRANSFORMATION, PC AND RS MODELS 201 SUMMARY 202
8 * NON-RASCH MODELS FOR ORDERED POLYTOMOUS DATA 209
THE GENERALIZED PARTIAL CREDIT MODEL 209 EXAMPLE: APPLICATION OF THE GPC
MODEL TO A REASONING ABILITY INSTRUMENT, MMLE 214
CONCEPTUAL DEVELOPMENT OF THE GRADED RESPONSE MODEL 217 HOW LARGE A
CALIBRATION SAMPLE? 223 EXAMPLE: APPLICATION OF THE GR MODEL TO AN
ATTITUDES TOWARDS CONDOMS SCALE, MMLE 224
INFORMATION FOR GRADED DATA 230 METRIC TRANSFORMATION, GPC AND GR MODELS
233 SUMMARY 234
9 * MODELS FOR NOMINAL POLYTOMOUS DATA 237
CONCEPTUAL DEVELOPMENT OF THE NOMINAL RESPONSE MODEL 238 HOW LARGE A
CALIBRATION SAMPLE? 246 EXAMPLE: APPLICATION OF THE NR MODEL TO A
SCIENCE TEST, MMLE 248
IMAGE 3
EXAMPLE: MIXED MODE] CALIBRATION OF THE SCIENCE TEST-NR AND PC MODELS,
MMLE 251
EXAMPLE: NR AND PC MIXED MODEL CALIBRATION OF THE SCIENCE TEST,
COLLAPSED OPTIONS, MMLE 254
INFORMATION FOR THE NR MODEL 259 METRIC TRANSFORMATION, NR MODEL 261
CONCEPTUAL DEVELOPMENT OF THE MULTIPLE-CHOICE MODEL 261
EXAMPLE: APPLICATION OF THE MC MODEL TO A SCIENCE TEST, MMLE 263
EXAMPLE: APPLICATION OF THE BS MODEL TO A SCIENCE TEST, MMLE 269 SUMMARY
272
10 * MODELS FOR MULTIDIMENSIONAL DATA 275
CONCEPTUAL DEVELOPMENT OF A MULTIDIMENSIONAL 1RT MODEL 275
MULTIDIMENSIONAL ITEM LOCATION AND DISCRIMINATION 281 ITEM VECTORS AND
VECTOR GRAPHS 285 THE MULTIDIMENSIONAL THREE-PARAMETER LOGISTIC MODEL
288 ASSUMPTIONS OF THE MIRT MODEL 288
ESTIMATION OF THE M2PL MODEL 289 INFORMATION FOR THE M2PL MODEL 290
INDETERMINACY IN MIRT 291 METRIC TRANSFORMATION, M2PL MODEL 294 EXAMPLE:
APPLICATION OF THE M2PL MODEL, NORMAL-OGIVE HARMONIC ANALYSIS ROBUST
METHOD 296
OBTAINING PERSON LOCATION ESTIMATES 302 SUMMARY 303
11 * LINKING AND EQUATING 306
EQUATING DEFINED 306 EQUATING: DATA COLLECTION PHASE 307 EQUATING:
TRANSFORMATION PHASE 309 EXAMPLE: APPLICATION OF THE TOTAL
CHARACTERISTIC FUNCTION EQUATING METHOD 316
SUMMARY 318
12 * DIFFERENTIAL ITEM FUNCTIONING 323
DIFFERENTIAL ITEM FUNCTIONING AND ITEM BIAS 324 MANTEL-HAENSZEL
CHI-SQUARE 327 THE TSW LIKELIHOOD RATIO TEST 330
LOGISTIC REGRESSION 331 EXAMPLE: DIF ANALYSIS 334 SUMMARY 343
APPENDIX A. MAXIMUM LIKELIHOOD ESTIMATION OF PERSON LOCATIONS 347
ESTIMATING AN INDIVIDUAL S LOCATION: EMPIRICAL MAXIMUM LIKELIHOOD
ESTIMATION 347 ESTIMATING AN INDIVIDUAL S LOCATION: NEWTON S METHOD FOR
MLE 348 REVISITING ZERO VARIANCE BINARY RESPONSE PATTERNS 354
APPENDIX B. MAXIMUM LIKELIHOOD ESTIMATION OF ITEM LOCATIONS 356
APPENDIX C. THE NORMAL OGIVE MODELS 360
CONCEPTUAL DEVELOPMENT OF THE NORMAL OGIVE MODEL 360 THE RELATIONSHIP
BETWEEN IRT STATISTICS AND TRADITIONAL ITEM ANALYSIS INDICES 365
RELATIONSHIP OF THE TWO-PARAMETER NORMAL OGIVE AND LOGISTIC MODELS 368
EXTENDING THE TWO-PARAMETER NORMAL OGIVE MODEL TO A MULTIDIMENSIONAL
SPACE 370
IMAGE 4
APPENDIX D. COMPUTERIZED ADAPTIVE TESTING 373
A BRIEF HISTORY 373 FIXED-BRANCHING TECHNIQUES 374 VARIABLE-BRANCHING
TECHNIQUES 375 ADVANTAGES OF VARIABLE-BRANCHING OVER FIXED-BRANCHING
METHODS 375
IRT-BASED VARIABLE-BRANCHING ADAPTIVE TESTING ALGORITHM 376
APPENDIX E. MISCELLANEA 382
LINEAR LOGISTIC TEST MODEL (LLTM) 382 USING PRINCIPAL AXIS FOR
ESTIMATING ITEM DISCRIMINATION 384 INFINITE ITEM DISCRIMINATION
PARAMETER ESTIMATES 385 EXAMPLE: NOHARM UNIDIMENSIONAL CALIBRATION 387
AN APPROXIMATE CHI-SQUARE STATISTIC FOR NOHARM 389 MIXTURE MODELS 391
RELATIVE EFFICIENCY, MONOTONICITY, AND INFORMATION 393 FORTRAN FORMATS
395 EXAMPLE: MIXED MODEL CALIBRATION OF THE SCIENCE TEST-NR AND 2PL
MODELS,
MMLE 396 EXAMPLE: MIXED MODEL CALIBRATION OF THE SCIENCE TEST-NR AND GR
MODELS, MMLE 399 ODDS, ODDS RATIOS, AND LOGITS 399 THE PERSON RESPONSE
FUNCTION 403 LINKING: A TEMPERATURE ANALOGY EXAMPLE 405 SHOULD DIF
ANALYSES BE BASED ON LATENT CLASSES? 407 THE SEPARATION AND RELIABILITY
INDICES 408
DEPENDENCY IN TRADITIONAL ITEM STATISTICS AND OBSERVED SCORES 409
REFERENCES 419
AUTHOR INDEX 439
SUBJECT INDEX 444
ABOUT THE AUTHOR 448
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| dewey-ones | 150 - Psychology |
| dewey-raw | 150.28/7 150.287 |
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| dewey-tens | 150 - Psychology |
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| language | English |
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| spelling | Ayala, Rafael Verfasser aut The theory and practice of item response theory R. J. de Ayala New York [u. a.] Guilford Press 2009 XV, 448 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Methodology in the social sciences Includes bibliographical references and index Item response theory (IRT) is a latent variable modeling approach used to minimize bias and optimize the measurement power of educational and psychological tests and other psychometric applications. Designed for advanced students, researchers, and psychometric professionals, this book clearly presents both the 'how-to' and the 'why' of IRT. It describes simple and more complex IRT models and shows how they are applied with the help of widely available software packages. The chapters follow a consistent format and build sequentially, taking the reader from model development through the fit analysis and interpretation phases that one would perform in practice. The use of common empirical data sets across the chapters facilitates understanding of the various models and how they relate to one another. Item response theory Mathematisches Modell Sozialwissenschaften Social sciences Mathematical models Social sciences Statistical methods Psychometrics Probabilistische Testtheorie (DE-588)4496586-2 gnd rswk-swf Probabilistische Testtheorie (DE-588)4496586-2 s DE-604 SWB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017053848&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ayala, Rafael The theory and practice of item response theory Item response theory Mathematisches Modell Sozialwissenschaften Social sciences Mathematical models Social sciences Statistical methods Psychometrics Probabilistische Testtheorie (DE-588)4496586-2 gnd |
| subject_GND | (DE-588)4496586-2 |
| title | The theory and practice of item response theory |
| title_auth | The theory and practice of item response theory |
| title_exact_search | The theory and practice of item response theory |
| title_full | The theory and practice of item response theory R. J. de Ayala |
| title_fullStr | The theory and practice of item response theory R. J. de Ayala |
| title_full_unstemmed | The theory and practice of item response theory R. J. de Ayala |
| title_short | The theory and practice of item response theory |
| title_sort | the theory and practice of item response theory |
| topic | Item response theory Mathematisches Modell Sozialwissenschaften Social sciences Mathematical models Social sciences Statistical methods Psychometrics Probabilistische Testtheorie (DE-588)4496586-2 gnd |
| topic_facet | Item response theory Mathematisches Modell Sozialwissenschaften Social sciences Mathematical models Social sciences Statistical methods Psychometrics Probabilistische Testtheorie |
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