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Automating Simulation Research for Item Response Theory using R

Year 2018, , 682 - 700, 16.12.2018
https://doi.org/10.21449/ijate.472185

Abstract

A simulation study is a useful tool in examining how validly item response theory (IRT) models can be applied in various settings. Typically, a large number of replications are required to obtain the desired precision. However, many standard software packages in IRT, such as MULTILOG and BILOG, are not well suited for a simulation study requiring a large number of replications because they were developed as a stand-alone software package that is best suited for a single run. This article demonstrated how built-in R functions can be used to automate the simulation study using the stand-alone software packages in IRT. For a demonstration purpose, MULTILOG was used in the example codes in the appendices, but the overall framework of a simulation study and the built-in R functions used in this article can be applied for a simulation study using other stand-alone software packages as well.

References

  • Bandalos, D. L. (2006). The use of monte carlo studies in structural equation modeling research. In Structural equation modeling: A second course (pp. 385–426).
  • Greenwich, CT: Information Age.
  • De Ayala, R. J. (2009). Theory and practice of item response theory. New York, NY: The Guilford Press. Finch, H. (2008). Estimation of item response theory parameters in the presence of missing data. Journal of Educational Measurement, 45, 225–245.
  • Friedl, J. (2006). Mastering regular expressions. Sebastopol, CA: O’Reilly Media, Inc.
  • Harwell, M., Stone, C. A., Hsu, T.-C., & Kirisci, L. (1996). Monte carlo studies in item response theory. Applied Psychological Measurement, 20, 101–125.
  • Kim, H. J., Brennan, R. L., & Lee, W. C. (2017). Structural Zeros and Their Implications With Log‐Linear Bivariate Presmoothing Under the Internal‐Anchor Design. Journal of Educational Measurement, 54, 145-164.
  • Kim, K. Y., & Lee, W. C. (2017). The Impact of Three Factors on the Recovery of Item Parameters for the Three-Parameter Logistic Model. Applied Measurement in Education, 30, 228-242.
  • Kim, S., & Lee, W. C. (2006). An Extension of Four IRT Linking Methods for Mixed‐Format Tests. Journal of Educational Measurement, 43, 53-76.
  • Nader, I. W., Tran, U. S., & Voracek, M. (2015). Effects of Initial Values and Convergence Criterion in the Two-Parameter Logistic Model When Estimating the Latent Distribution in BILOG-MG 3. PloS one, 10, e0140163.
  • Partchev, I. (2009). irtoys: Simple interface to the estimation and plotting of irt models. R package version 0.1, 2.
  • R Core Team. (2015). R: A language and environment for statistical computing [Computer software manual]. Vienna, Austria. Retrieved from http://www.R-project.org/ (ISBN 3-900051-07-0)
  • Reckase, M. D. (1979). Unifactor latent trait models applied to multifactor tests: Results and implications. Journal of Educational and Behavioral Statistics, 4, 207–230.
  • Spector, P. (2008). Data manipulation with r. New York, NY: Springer.
  • Stone, C. A. (2000). Monte Carlo based null distribution for an alternative goodness‐of‐fit test statistic in IRT models. Journal of Educational Measurement, 37, 58-75.
  • Thissen, D., Chen, W.-H., & Bock, R. D. (2003). Multilog 7 for windows: Multiple-category item analysis and test scoring using item response theory [computer software]. lincolnwood, il: Scientific software international. IL: Scientific Software International.
  • Zimowski, M. F., Muraki, E., Mislevy, R. J., & Bock, R. D. (1996). Bilog-mg: Multiple-group irt analysis and test maintenance for binary items. Chicago: Scientific Software International, 4(85), 10.

Automating Simulation Research for Item Response Theory using R

Year 2018, , 682 - 700, 16.12.2018
https://doi.org/10.21449/ijate.472185

Abstract

A simulation study is a useful tool in examining
how validly item response theory (IRT) models can be applied in various settings.
Typically, a large number of replications are required to obtain the desired precision.
However, many standard software packages in IRT, such as MULTILOG and BILOG, are
not well suited for a simulation study requiring a large number of replications
because they were developed as a stand-alone software package that is best suited
for a single run. This article demonstrated how built-in R functions can be used
to automate the simulation study using the stand-alone software packages in IRT.
For a demonstration purpose, MULTILOG was used in the example codes in the appendices,
but the overall framework of a simulation study and the built-in R functions used
in this article can be applied for a simulation study using other stand-alone software
packages as well.

References

  • Bandalos, D. L. (2006). The use of monte carlo studies in structural equation modeling research. In Structural equation modeling: A second course (pp. 385–426).
  • Greenwich, CT: Information Age.
  • De Ayala, R. J. (2009). Theory and practice of item response theory. New York, NY: The Guilford Press. Finch, H. (2008). Estimation of item response theory parameters in the presence of missing data. Journal of Educational Measurement, 45, 225–245.
  • Friedl, J. (2006). Mastering regular expressions. Sebastopol, CA: O’Reilly Media, Inc.
  • Harwell, M., Stone, C. A., Hsu, T.-C., & Kirisci, L. (1996). Monte carlo studies in item response theory. Applied Psychological Measurement, 20, 101–125.
  • Kim, H. J., Brennan, R. L., & Lee, W. C. (2017). Structural Zeros and Their Implications With Log‐Linear Bivariate Presmoothing Under the Internal‐Anchor Design. Journal of Educational Measurement, 54, 145-164.
  • Kim, K. Y., & Lee, W. C. (2017). The Impact of Three Factors on the Recovery of Item Parameters for the Three-Parameter Logistic Model. Applied Measurement in Education, 30, 228-242.
  • Kim, S., & Lee, W. C. (2006). An Extension of Four IRT Linking Methods for Mixed‐Format Tests. Journal of Educational Measurement, 43, 53-76.
  • Nader, I. W., Tran, U. S., & Voracek, M. (2015). Effects of Initial Values and Convergence Criterion in the Two-Parameter Logistic Model When Estimating the Latent Distribution in BILOG-MG 3. PloS one, 10, e0140163.
  • Partchev, I. (2009). irtoys: Simple interface to the estimation and plotting of irt models. R package version 0.1, 2.
  • R Core Team. (2015). R: A language and environment for statistical computing [Computer software manual]. Vienna, Austria. Retrieved from http://www.R-project.org/ (ISBN 3-900051-07-0)
  • Reckase, M. D. (1979). Unifactor latent trait models applied to multifactor tests: Results and implications. Journal of Educational and Behavioral Statistics, 4, 207–230.
  • Spector, P. (2008). Data manipulation with r. New York, NY: Springer.
  • Stone, C. A. (2000). Monte Carlo based null distribution for an alternative goodness‐of‐fit test statistic in IRT models. Journal of Educational Measurement, 37, 58-75.
  • Thissen, D., Chen, W.-H., & Bock, R. D. (2003). Multilog 7 for windows: Multiple-category item analysis and test scoring using item response theory [computer software]. lincolnwood, il: Scientific software international. IL: Scientific Software International.
  • Zimowski, M. F., Muraki, E., Mislevy, R. J., & Bock, R. D. (1996). Bilog-mg: Multiple-group irt analysis and test maintenance for binary items. Chicago: Scientific Software International, 4(85), 10.
There are 16 citations in total.

Details

Primary Language English
Subjects Studies on Education
Journal Section Articles
Authors

Sunbok Lee This is me 0000-0002-0924-7056

Youn-jeng Choi This is me

Allan S. Cohen This is me

Publication Date December 16, 2018
Submission Date August 22, 2018
Published in Issue Year 2018

Cite

APA Lee, S., Choi, Y.-j., & Cohen, A. S. (2018). Automating Simulation Research for Item Response Theory using R. International Journal of Assessment Tools in Education, 5(4), 682-700. https://doi.org/10.21449/ijate.472185

Cited By

Computer Adaptive Testing Simulations in R
International Journal of Assessment Tools in Education
BAŞAK ERDEM KARA
https://doi.org/10.21449/ijate.621157

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