Why item response theory should be used for longitudinal questionnaire data analysis in medical research

BMC Med Res Methodol. 2015 Jul 30;15:55. doi: 10.1186/s12874-015-0050-x.

Abstract

Background: Multi-item questionnaires are important instruments for monitoring health in epidemiological longitudinal studies. Mostly sum-scores are used as a summary measure for these multi-item questionnaires. The objective of this study was to show the negative impact of using sum-score based longitudinal data analysis instead of Item Response Theory (IRT)-based plausible values.

Methods: In a simulation study (varying the number of items, sample size, and distribution of the outcomes) the parameter estimates resulting from both modeling techniques were compared to the true values. Next, the models were applied to an example dataset from the Amsterdam Growth and Health Longitudinal Study (AGHLS).

Results: The results show that using sum-scores leads to overestimation of the within person (repeated measurement) variance and underestimation of the between person variance.

Conclusions: We recommend using IRT-based plausible value techniques for analyzing repeatedly measured multi-item questionnaire data.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Biomedical Research / methods*
  • Computer Simulation
  • Health Surveys / methods*
  • Humans
  • Longitudinal Studies
  • Models, Theoretical
  • Netherlands
  • Research Design / standards*
  • Sample Size
  • Surveys and Questionnaires / standards*