Component latent trait models for paragraph comprehension tests

Embretson, Susan E.
Wetzel, C. Douglas
1987
Thumbnail Image

View/Download File

v11n2p175.pdf (2.63 MB)

Persistent link to this item

https://hdl.handle.net/11299/103982
Statistics
View Statistics

Journal Title

Journal ISSN

Volume Title

Title

Component latent trait models for paragraph comprehension tests

Authors

Published Date

1987

Publisher

Type

Article

Abstract

The cognitive characteristics of paragraph comprehension items were studied by comparing models that deal with two general processing stages: text representation and response decision. The models that were compared included the prepositional structure of the text (Kintsch & van Dijk, 1978), various counts of surface structure variables and word frequency (Drum et al., 1981), a taxonomy of levels of text questions (Anderson, 1972), and some new models that combine features of these models. Calibrations from the linear logistic latent trait model allowed evaluation of the impact of the cognitive variables on item responses. The results indicate that successful prediction of item difficulty is obtained from models with wide representation of both text and decision processing. This suggests that items can be screened for processing difficulty prior to being administered to examinees. However, the results also have important implications for test validity in that the two processing stages involve two different ability dimensions.

Keywords

Description

Related to

Replaces

License

Collections

Applied Psychological Measurement, Volume 11, 1987

Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Embretson, Susan E & Wetzel, C. Douglas. (1987). Component latent trait models for paragraph comprehension tests. Applied Psychological Measurement, 11, 175-193. doi:10.1177/014662168701100207

Suggested citation

Embretson, Susan E.; Wetzel, C. Douglas. (1987). Component latent trait models for paragraph comprehension tests. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/103982.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.