Online interdisciplinary seminar series on statistical methodology for social and behavior research(February 26)

This session is jointly sponsored by the Statistics department and the Research Methods, Measurement, and Evaluation program, University of Connecticut (UCONN), New England Statistical Society (NESS) and Statistical and Applied Mathematical Institute (SAMSI) as part of online interdisciplinary seminar series on statistical methodology for social and behavior research.

Speaker: DR. EDWARD IP

Date and Time: FRIDAY, 2/26/2021, 12PM


Abstract: 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.

Bio: Dr. Edward Ip is a Professor in the Department of Biostatistics and Data Science, in the Wake Forest School of Medicine. He received his master’s in education and PhD in statistics, both from Stanford. His research interests include latent variable modeling and longitudinal data analysis. He is currently Editor of the journal, Psychometrika, Application Reviews and Case Studies (ARCS) section.

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