15.6 References

Andrich, D. (1988). Rasch models for measurement. SAGE.

Baker, F. B., & Kim, S. -H. (2017). The basics of Item Response Theory using R. Springer.

Bock, R. D. (1997). A brief history of item response theory. Educational Measurement: Issues and Practice, 16, 21—33.

Chalmers R. P. (2012). mirt: A Multidimensional Item Response Theory Package for the R environment. Journal of Statistical Software, 48(6), 1—29.

Crocker, L., & Algina, J. (1986). Introduction to classical and modern test theory. Holt, Rinehart, & Winston.

DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.). SAGE.

Mislevy, R. J., Wilson, M. R., Ercikan, K., & Chudowsky, N. (2003). Psychometric principles in student assessment. In D. Stufflebeam & T. Kellagham (Eds.), International handbook of educational evaluation (pp. 489—532). Kluwer Academic Press.

Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 14(1), 59—71.

Ostini, R., & Nering, M. L. (2006). Polytomous item response theory models. SAGE.

Paek, I., & Cole, K. (2020). Using R for Item Response Theory model applications. Routledge.

Revelle, W. (2023). An introduction to psychometric theory with applications in R.

Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometric Society.

Thissen, D. (1982). Marginal maximum likelihood estimation for the one-parameter logistic model. Psychometrika, 47, 175—186.