Buch, Englisch, 354 Seiten, Format (B × H): 167 mm x 241 mm, Gewicht: 1510 g
Buch, Englisch, 354 Seiten, Format (B × H): 167 mm x 241 mm, Gewicht: 1510 g
Reihe: Statistics for Social and Behavioral Sciences
ISBN: 978-0-387-89975-6
Verlag: Springer
Multidimensional Item Response Theory is the first book to give thorough coverage to this emerging area of psychometrics. The book describes the commonly used multidimensional item response theory (MIRT) models and the important methods needed for their practical application. These methods include ways to determine the number of dimensions required to adequately model data, procedures for estimating model parameters, ways to define the space for a MIRT model, and procedures for transforming calibrations from different samples to put them in the same space. A full chapter is devoted to methods for multidimensional computerized adaptive testing.
The text is appropriate for an advanced course in psychometric theory or as a reference work for those interested in applying MIRT methodology. A working knowledge of unidimensional item response theory and matrix algebra is assumed. Knowledge of factor analysis is also helpful.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Sozialwissenschaften Psychologie Allgemeine Psychologie Differentielle Psychologie, Persönlichkeitspsychologie Psychologische Diagnostik, Testpsychologie
- Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
Weitere Infos & Material
Unidimensional Item Response Theory Models.- Historical Background for Multidimensional Item Response Theory (MIRT).- Multidimensional Item Response Theory Models.- Statistical Descriptions of Item and Test Functioning.- Estimation of Item and Person Parameters.- Analyzing the Structure of Test Data.- Transforming Parameter Estimates to a Specified Coordinate System.- Linking and Scaling.- Computerized Adaptive Testing Using MIRT.
