RMME/STAT Joint Colloquium:

Partially Ordered Responses and Applications

Edward Ip
Wake Forest University

Friday, February 26^th, at 12:00PM EST

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=mc1ed9d99b7e6b63ab2796d729867e365

Partially ordered set (poset) responses are prevalent in fields such as psychology, education, and health. For example, the psychopathologic classification of no anxiety (NA), mild anxiety (MA), anxiety with depression (AwD), and severe anxiety (SA) form a poset. Due in part to the lack of analytic tools, poset responses are often collapsed into other data forms such as ordinal data. During such a process, subtle information within a poset is inevitably lost. In this presentation, a longitudinal latent-variable model for poset responses and its application to health data will be described. It is argued that latent variable modeling enables the integration of information from both ordinal and nominal components in a poset. Using the abovementioned example, NA>{MA,AwD}>SA form the ordinal component, and MA and AwD form the nominal component. Specifically, it will be demonstrated that the latent variable model “discovers” implicit ordering within the nominal categories. This is possible because both intra-person and inter-person information are borrowed to reinforce inference. Some potential applications of the poset model will also be highlighted.

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