A new adaptive testing algorithm for shortening health literacy assessments. Academic Article uri icon

Overview

MeSH

  • Adult
  • Aged
  • Educational Measurement
  • Educational Status
  • Female
  • Humans
  • Male
  • Middle Aged

MeSH Major

  • Algorithms
  • Decision Theory
  • Health Literacy

abstract

  • Low health literacy has a detrimental effect on health outcomes, as well as ability to use online health resources. Good health literacy assessment tools must be brief to be adopted in practice; test development from the perspective of item-response theory requires pretesting on large participant populations. Our objective was to develop a novel classification method for developing brief assessment instruments that does not require pretesting on large numbers of research participants, and that would be suitable for computerized adaptive testing. We present a new algorithm that uses principles of measurement decision theory (MDT) and Shannon's information theory. As a demonstration, we applied it to a secondary analysis of data sets from two assessment tests: a study that measured patients' familiarity with health terms (52 participants, 60 items) and a study that assessed health numeracy (165 participants, 8 items). In the familiarity data set, the method correctly classified 88.5% of the subjects, and the average length of test was reduced by about 50%. In the numeracy data set, for a two-class classification scheme, 96.9% of the subjects were correctly classified with a more modest reduction in test length of 35.7%; a three-class scheme correctly classified 93.8% with a 17.7% reduction in test length. MDT-based approaches are a promising alternative to approaches based on item-response theory, and are well-suited for computerized adaptive testing in the health domain.

publication date

  • August 6, 2011

has subject area

  • Adult
  • Aged
  • Algorithms
  • Decision Theory
  • Educational Measurement
  • Educational Status
  • Female
  • Health Literacy
  • Humans
  • Male
  • Middle Aged

Research

keywords

  • Journal Article

Identity

Language

  • eng

PubMed Central ID

  • PMC3178473

Digital Object Identifier (DOI)

  • 10.1186/1472-6947-11-52

PubMed ID

  • 21819614

Additional Document Info

start page

  • 52

volume

  • 11