Using Mixture and Rating Scale Models to Investigate Response Profiles

The purpose of this presentation is to demonstrate a new methodology for identifying different sub-populations of respondents in surveys using a finite mixture approach based on polytomous Rasch modeling. The proposed model aims to identify sets of respondent profiles that differ in their response tendencies. For instance, the model could be used to discover latent classes consisting of respondents either avoiding or preferring extreme categories. The presentation will offer additional approaches to modifying polytomous IRT models in order to accommodate the underlying structure of the response data.

Perman Gochyyev is a research psychometrician at the Berkeley Evaluation and Assessment Research (BEAR) Center, UC Berkeley. Perman received his PhD in Quantitative Methods and Evaluation from UC Berkeley in 2015. His research focuses on latent variable and multilevel modeling, multidimensional and ordinal IRT models, latent class models, and issues related to causal inference in behavioral statistics.

Tuesday, April 24, 2018 - 2:00pm
Tolman 2515
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