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Multidimensional Extension of Multiple Indicators Multiple Causes Models to Detect DIF.

Soo Lee1, Okan Bulut2, Youngsuk Suh3

  • 1American Institutes for Research, Washington, DC, USA.

Educational and Psychological Measurement
|July 24, 2018
PubMed
Summary
This summary is machine-generated.

The multidimensional MIMIC-interaction model effectively detects uniform differential item functioning (DIF) in item response theory (IRT) models. Its power to detect nonuniform DIF requires further investigation, especially with larger sample sizes and test lengths.

Keywords:
MIMICdifferential item functioningmultidimensional IRTnonsimple structure

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Multiple Indicators Multiple Causes (MIMIC) models are established for detecting uniform differential item functioning (DIF).
  • The MIMIC-interaction model extends this to detect both uniform and nonuniform DIF within unidimensional item response theory (IRT).

Purpose of the Study:

  • To extend the MIMIC-interaction model to multidimensional IRT (MIRT) for DIF detection.
  • To evaluate the performance of the MIRT MIMIC-interaction model under various simulation conditions, focusing on Type I error and power rates.

Main Methods:

  • Simulated data were generated under varying conditions: DIF pattern/magnitude, test length, latent trait correlation, sample size, and latent mean differences.
  • The performance of the multidimensional MIMIC-interaction model was assessed using Type I error and power rates.

Main Results:

  • The model demonstrated higher power for detecting uniform DIF compared to nonuniform DIF.
  • Increased anchor item length and sample size positively impacted DIF detection power.
  • Equal latent mean conditions yielded higher power rates than conditions with differing latent means.

Conclusions:

  • The multidimensional MIMIC-interaction model shows promise for identifying uniform DIF in MIRT.
  • Its effectiveness in detecting nonuniform DIF warrants further research and validation.