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The Numeracy Understanding in Medicine Instrument

Author

Listed:
  • Marilyn M. Schapira
  • Cindy M. Walker
  • Kevin J. Cappaert
  • Pamela S. Ganschow
  • Kathlyn E. Fletcher
  • Emily L. McGinley
  • Sam Del Pozo
  • Carrie Schauer
  • Sergey Tarima
  • Elizabeth A. Jacobs

Abstract

Background : Health numeracy can be defined as the ability to understand and apply information conveyed with numbers, tables and graphs, probabilities, and statistics to effectively communicate with health care providers, take care of one’s health, and participate in medical decisions. Objective : To develop the Numeracy Understanding in Medicine Instrument (NUMi) using item response theory scaling methods. Design : A 20-item test was formed drawing from an item bank of numeracy questions. Items were calibrated using responses from 1000 participants and a 2-parameter item response theory model. Construct validity was assessed by comparing scores on the NUMi to established measures of print and numeric health literacy, mathematic achievement, and cognitive aptitude. Participants: Community and clinical populations in the Milwaukee and Chicago metropolitan areas. Results : Twenty-nine percent of the 1000 respondents were Hispanic, 24% were non-Hispanic white, and 42% were non-Hispanic black. Forty-one percent had no more than a high school education. The mean score on the NUMi was 13.2 ( s = 4.6) with a Cronbach α of 0.86. Difficulty and discrimination item response theory parameters of the 20 items ranged from −1.70 to 1.45 and 0.39 to 1.98, respectively. Performance on the NUMi was strongly correlated with the Wide Range Achievement Test–Arithmetic (0.73, P

Suggested Citation

  • Marilyn M. Schapira & Cindy M. Walker & Kevin J. Cappaert & Pamela S. Ganschow & Kathlyn E. Fletcher & Emily L. McGinley & Sam Del Pozo & Carrie Schauer & Sergey Tarima & Elizabeth A. Jacobs, 2012. "The Numeracy Understanding in Medicine Instrument," Medical Decision Making, , vol. 32(6), pages 851-865, November.
    • Handle: RePEc:sae:medema:v:32:y:2012:i:6:p:851-865
    DOI: 10.1177/0272989X12447239
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    References listed on IDEAS

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    1. Robin Shealy & William Stout, 1993. "A model-based standardization approach that separates true bias/DIF from group ability differences and detects test bias/DTF as well as item bias/DIF," Psychometrika, Springer;The Psychometric Society, vol. 58(2), pages 159-194, June.
    2. Edward T. Cokely & Mirta Galesic & Eric Schulz & Saima Ghazal & Rocio Garcia-Retamero, 2012. "Measuring risk literacy: The Berlin Numeracy Test," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 7(1), pages 25-47, January.
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    Cited by:

    1. Saima Ghazal & Edward T. Cokely & Rocio Garcia-Retamero, 2014. "Predicting biases in very highly educated samples: Numeracy and metacognition," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(1), pages 15-34, January.
    2. Hsiang-Wen Lin & Elizabeth H. Chang & Yu Ko & Chun-Yu Wang & Yu-Shan Wang & Okti Ratna Mafruhah & Shang-Hua Wu & Yu-Chieh Chen & Yen-Ming Huang, 2020. "Conceptualization, Development and Psychometric Evaluations of a New Medication-Related Health Literacy Instrument: The Chinese Medication Literacy Measurement," IJERPH, MDPI, vol. 17(19), pages 1-17, September.
    3. S. Gatobu & J. F. Arocha & L. Hoffman-Goetz, 2014. "Numeracy and Health Numeracy Among Chinese and Kenyan Immigrants to Canada," SAGE Open, , vol. 4(1), pages 21582440145, February.

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