PubMed Health⌕ Search

PubMed · 17060836

Classical test theory.

Abstract

Classical test theory (CTT) comprises a set of concepts and methods that provide a basis for many of the measurement tools currently used in health research. The assumptions and concepts underlying CTT are discussed. These include item and scale characteristics that derive from CTT as well as types of reliability and validity. Procedures commonly used in the development of scales under CTT are summarized, including factor analysis and the creation of scale scores. The advantages and disadvantages of CTT, its use across populations, and its continued use in the face of more recent measurement models are also discussed.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Robert F DeVellis. 2006. Classical test theory.. https://doi.org/10.1097/01.mlr.0000245426.10853.30

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Probability estimation when some observations are grouped.

This paper considers the use of additional questions for decreasing survey non-response rates and an approach for estimating a probability based on the results obtained. In a survey, the respondents are asked to answer an original question and follow-up questions, where the answers for the follow-up questions are grouped answers for the original question. For example, respondents are asked to provide an exact number of incidents, but in cases of 'Do not know' or 'Refuse' responses, they are subsequently asked to pick an answer from a less specific categorical scale. The new estimator obtains smaller variance asymptotically and does not depend on a distribution family. This method is applied to income questions in a survey regarding injury prevention and behaviours. Another application is survey data on intimate partner violence, where some amendments were applied for incorporating post-stratification weights and for using non-random grouping. For additional illustration, an example of parameter estimation on artificially generated data is presented.

Data Collection↗