Longitudinal DCMs

Differing from classical test theory, item response theory, and student growth percentiles, which support norm-referenced interpretations of growth, longitudinal DCMs support categorical and criterion-referenced interpretations of growth. I have three recent articles that detail these developments:

Madison, M. J., & Bradshaw, L. P. (2018). Assessing growth in a diagnostic classification model framework.                                            Psychometrika, 83(4), 963-990.

Madison, M. J., & Bradshaw, L. P. (2018). Evaluating intervention effects in a diagnostic classification model framework.                     Journal of Educational Measurement, 55(1), 32-51. 

Madison, M. J. (in press). Reliably assessing growth with longitudinal diagnostic classification models. Educational                             Measurement: Issues and Practice.  

The Psychometrika article details the foundations of the Transition Diagnostic Classification Model (TDCM). The TDCM is a general longitudinal DCM that combines latent transition analysis (LTA) with the Log-linear Cognitive Diagnosis Model (LCDM; Henson, Templin, Willse, 2009). Via simulation, we show that the TDCM provides accurate and reliable classifications in a pre-test post-test setting, and is robust in the presence of item parameter drift. The Journal of Educational Measurement article extends the TDCM to multiple groups, thereby enabling the examination of group‐differential growth in attribute mastery and the evaluation of intervention effects. The utility of the multigroup TDCM was demonstrated in the evaluation of an innovative instructional method in mathematics education. The EM:IP article introduces reliability measures for longitudinal DCMs. 

tdcm R Package
All three articles cited above used Mplus to estimate the TDCM. Mplus provides tremendous flexibility, however, TDCM syntax is tedious. To make it easier to access the TDCM, we have developed the tdcm R package (Madison, Jeon, & Cotterell, 2023) to estimate the TDCM. It uses the CDM package (George, et al., 2016) as a foundation, but adds TDCM functionality. It is not published to CRAN yet, but you can use the files below to access it. Draft documentation can be found here. Email me at mjmadison@uga.edu with feedback or questions, or leave feedback here

R Script and Example Data Sets

Demonstration Video: 

Matthew J. Madison

Psychometrician and Statistician