Posted on Tuesday, February 23, 2021
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
Topic: PARTIALLY ORDERED RESPONSES AND APPLICATIONS
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.
For inquiry, please contact Dr. Xiaojing Wang at xiaojing.wang@uconn.edu
