Journal article
Classification Consistency and Accuracy for Mixed-Format Tests
Applied measurement in education, Vol.32(2), pp.97-115
04/03/2019
DOI: 10.1080/08957347.2019.1577246
Abstract
This study explores classification consistency and accuracy for mixed-format tests using real and simulated data. In particular, the current study compares six methods of estimating classification consistency and accuracy for seven mixed-format tests. The relative performance of the estimation methods is evaluated using simulated data. Study results from real data analysis showed that the procedures exhibited similar patterns across various exams, but some tended to produce lower estimates of classification consistency and accuracy than others. As data became more multidimensional, unidimensional and multidimensional item response theory (IRT) methods tended to produce different results, with the unidimensional approach yielding lower estimates than the multidimensional approach. Results from simulated data analysis demonstrated smaller estimation error for the multidimensional IRT methods than for the unidimensional IRT method. The unidimensional approach yielded larger error as tests became more multidimensional, whereas a reverse relationship was observed for the multidimensional IRT approach. Among the non-IRT approaches, the normal approximation and Livingston-Lewis methods performed well, whereas the compound multinomial method tended to produce relatively larger error.
Details
- Title: Subtitle
- Classification Consistency and Accuracy for Mixed-Format Tests
- Creators
- Stella Y. Kim - Univ N Carolina, Educ Leadership, Charlotte, NC 28223 USAWon-Chan Lee - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Applied measurement in education, Vol.32(2), pp.97-115
- Publisher
- Routledge
- DOI
- 10.1080/08957347.2019.1577246
- ISSN
- 0895-7347
- eISSN
- 1532-4818
- Number of pages
- 19
- Language
- English
- Date published
- 04/03/2019
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9984371108102771
Metrics
6 Record Views