  Providing diagnostic feedback about growth is crucial to formative decisions
such as targeted remedial instructions or interventions. This paper proposed a
longitudinal higher-order diagnostic classification modeling approach for
measuring growth. The new modeling approach is able to provide quantitative
values of overall and individual growth by constructing a multidimensional
higher-order latent structure to take into account the correlations among
multiple latent attributes that are examined across different occasions. In
addition, potential local item dependence among anchor (or repeated) items can
also be taken into account. Model parameter estimation is explored in a
simulation study. An empirical example is analyzed to illustrate the
applications and advantages of the proposed modeling approach.
