Diagnostic modeling, also known as cognitive diagnosis, is a field of psychometrics that has seen increased research activity in recent years due to the potential for diagnosing the mental states of individuals, such as in the social sciences (e.g., criteria indicating psychological disorders) or in education (e.g., the skills examinees may possess). To date, such models have been developed and expanded for use in a broad spectrum of research areas and have been shown to be effective at classifying individuals on a set of categorical latent variables. Although the development of diagnostic models has focused on more comprehensive and flexible measurement models for data collected at a single occasion, there have been no systematic efforts to extend these models into the measurement of individual change. Simply put, the researcher who wishes to study changes in diagnostic status over time currently cannot do so. This research project will focus on the development, evaluation, and utilization of longitudinal extensions of diagnostic models for the measurement of change over time. In developing longitudinal diagnostic models, advances must be made that balance the model demands of potentially large numbers of parameters with practical and accurate approximations. The investigators will develop practical and feasible longitudinal methods for diagnostic measurement, modeling, and assessment, and they will examine their accuracy, sensitivity, and comparative efficiency. The investigators also will examine the practical considerations and statistical issues in applying these methods in real-world longitudinal diagnostic analyses. Freely available software will be provided for researchers and practitioners to utilize these methods in their own studies.
This project will expand the practicality of diagnostic models and add to the number of research and real-world scenarios where such models can be used. The models and software developed from this project will fill an existing void that has limited the study of diagnostic phenomena over time. Furthermore, guidelines about the structure of data needed for each model (i.e., sample size or number of measurement occasions) will allow for application of such models across a wide spectrum of empirical research areas studying how behavioral phenomena change over time. The methods developed from this project will enable a more rigorous examination of how mental states change over time. The results of this research have the potential to greatly advance basic understanding of how people develop and change with respect to diagnostic attributes and clinical criteria.
This project sought to develop, refine, and implement methods for longitudinal diagnostic modeling. Diagnostic modeling is a field of psychometrics that has seen increased research activity in recent years due to the potential for diagnosing the mental states of individuals, such as in the social sciences (e.g., criteria indicating psychological disorders), or in education (e.g., the skills examinees may possess). To date, such models have been developed and expanded for use in a broad spectrum of research areas and have been shown to be effective at classifying individuals on a set of categorical latent variables. Although the development of diagnostic models has focused on more comprehensive and flexible measurement models for data collected at a single occasion, there have been no systematic efforts to extend these models into the measurement of individual change. The researcher who wishes to study changes in diagnostic status over time currently cannot do so. The focus of this proposal is on the development, evaluation, and utilization of longitudinal extensions of diagnostic models for the measurement of change over time. In particular, the project developed and evaluated a statistical algorithm for estimating these models, and began testing its utility in several specific research fields (e.g., educational assessment, assessment of cognition in older adults). However, work is still ongoing.
