General diagnostic classification models (DCMs) can be used to capture individual students’ cognitive learning status. Moreover, DCMs for longitudinal data are appropriate to track students transition of cognitive elements. This study developed an effective Bayesian posterior approximation method called variational Bayesian (VB) inference method for hidden Markov type longitudinal general DCMs. Simulation study indicated the proposed algorithm could satisfactorily recover true parameters. Comparative study of the VB and previously developed Markov chain Monte Carlo (MCMC) methods was conducted in real data example. The result revealed that the VB method provided similar parameter estimates to the MCMC with faster estimation time.

This paper demonstrates the process of invariance testing in diagnostic classification models in the presence of attribute hierarchies via an extension of the log-linear cognitive diagnosis model (LCDM). This extension allows researchers to test for measurement (item) invariance as well as attribute (structural) invariance simultaneously in a single analysis. The structural model of the LCDM was parameterized as a Bayesian network, which allows attribute hierarchies to be modeled and tested for attribute invariance via a series of latent regression models. We illustrate the steps for carrying out the invariance analyses through an in-depth case study with an empirical dataset and provide JAGS code for carrying out the analysis within the Bayesian framework. The analysis revealed that a subset of the items exhibit partial invariance, and evidence of full invariance was found at the structural level.

Students’ ability to effectively allocate time toward educational tasks and reduction of maladaptive behaviors such as procrastination are important predictors of successful educational outcomes. The Academic Time Management and Procrastination Measure (ATMPM) purports to measure the extent to which students engage in such behaviors; however, the psychometric properties of the ATMPM have only been explored with exploratory techniques. In addition, the extent to which measurement invariance is supported among first-generation college students (FGCS) and non-FGCS is unknown. The purpose of the present study was to (1) examine the factor structure of the ATMPM within a college population by employing confirmatory factor analysis and to (2) investigate measurement invariance through an application of multiple group confirmatory factor analysis (MGCFA). Results supported a three-factor solution (planning time, monitoring time, and procrastination), and invariance analyses supported full configural, metric, and scalar invariance.

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This paper demonstrates the process of invariance testing in diagnostic classification models in the presence of attribute hierarchies via an extension of the log-linear cognitive diagnosis model (LCDM). This extension allows researchers to test for measurement (item) invariance as well as attribute (structural) invariance simultaneously in a single analysis. The structural model of the LCDM is parameterized as a Bayesian network which allows attribute hierarchies to be modeled and tested for attribute invariance via a series of latent regression models. We illustrate the steps for carrying out the invariance analyses through an in-depth case study with an empirical dataset and provide JAGS code for carrying out the analysis within a Bayesian framework. The analysis revealed that a subset of the items exhibit partial invariance and evidence of full invariance was found at the structural level.