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Hidden item variance in multiple mini-interview scores.

Nikki L Bibler Zaidi1, Christopher M Swoboda2, Benjamin M Kelcey2

  • 15117 Taubman Health Sciences Library, Office of Medical Student Education, University of Michigan Medical School, Ann Arbor, MI, 48109-5726, USA. bibler@med.umich.edu.

Advances in Health Sciences Education : Theory and Practice
|August 22, 2016
PubMed
Summary
This summary is machine-generated.

Hiding rating items in multiple mini-interview (MMI) score analysis can bias results. A person-by-station-by-item model is recommended over the hidden item model for accurate generalizability estimates.

Keywords:
Estimated variance componentsGeneralizability (G) theory, medical school admissionsMonte Carlo simulationMultiple mini-interview (MMI)

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Medical Education

Background:

  • Multiple mini-interview (MMI) scores are crucial for medical school admissions.
  • Generalizability (G) theory is commonly used to assess MMI reliability.
  • Existing G theory models may overlook variance from rating items.

Purpose of the Study:

  • To investigate the impact of omitting the item facet in G theory models for MMI scores.
  • To compare variance component estimates from a "hidden item" model versus a full model.
  • To determine if omitting items biases reliability estimates in MMI evaluations.

Main Methods:

  • Conducted an extensive Monte Carlo simulation study.
  • Compared a person-by-station-by-item (p × s × i) model with a hidden item person-by-station (p × s|i) model.
  • Simulated scenarios with true item-level effects to assess bias.

Main Results:

  • The "hidden item" model (p × s|i) produced biased variance components when item effects were present.
  • Biased variance components led to inaccurate reliability estimates.
  • The full model (p × s × i) provided more accurate estimates of variance.

Conclusions:

  • Failure to model the item facet can significantly bias MMI generalizability studies.
  • Researchers should consider the person-by-station-by-item (p × s × i) model for MMI evaluations.
  • Accurate assessment of MMI reliability requires accounting for item-level variance.