Journal
PSYCHOLOGICAL METHODS
Volume 23, Issue 1, Pages 138-149Publisher
AMER PSYCHOLOGICAL ASSOC
DOI: 10.1037/met0000121
Keywords
item response theory; local dependence; local independence; diagnostics
Categories
Funding
- Ohio State University and the College Board
- Institute of Education Sciences [R305D100039, R305B080016]
- National Institute on Drug Abuse [R01DA026943, R01DA030466]
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Item response theory (IRT) is a widely used measurement model. When considering its use in education, health outcomes, and psychology, it is likely to be one of the most impactful psychometric models in existence. IRT has many advantages over classical test theory-based measurement models. For these advantages to hold in practice, strong assumptions must be satisfied. One of these assumptions, local independence, is the focus of the work described here. Local independence is the assumption that, conditional on the latent variable(s), item responses are unrelated to one another (i.e., independent). Stated another way, local independence implies that the only thing causing items to covary is the modeled latent variable(s). Violations of this assumption, quite aptly titled local dependence, can have serious consequences for the estimated parameters. A new diagnostic is proposed, based on parameter stability in an item-level jackknife resampling procedure. We review the ideas underlying the new diagnostic and how it is computed before covering some simulated and real examples demonstrating its effectiveness.
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