From expectation-3-maximization to bayesian expectation-3-maximization: A latent mixture modeling-based bayesian algorithm for the 4-parameter logistic model

There is renewed interest in the four-parameter logistic model (4PLM), but the lack of a user-friendly calibration method constitutes a major barrier to its widespread application. In the present study, this researcher reformulated the 4PLM from a latent mixture modeling view and developed the Expectation-Maximization-Maximization-Maximization (EMMM) method. Combining the EMMM with the Bayesian approach, allowed the Bayesian Expectation-Maximization-Maximization-Maximization (BEMMM) algorithm to be proposed. First, the author compared the EMMM with BEMMM to confirm that the BEMMM method reduced the number of implausible estimates in EMMM. Next, when comparing the BEMMM with the Markov Chain Monte Carlo method (Culpepper, 2016) and Bayesian Modal Estimation (Waller & Feuerstahler, 2017), the results from a simulation study and a real-world data calibration indicated that the BEMMM and the MCMC are more accurate than the BME, while the BEMMM is much faster than the MCMC.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Educational Psychology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2018

Author dc:creator

• Zhang, Ci

Contributors dc:contributor

• Zhang, Jinming
• Chang, Hua-Hua
• Anderson, Carolyn J.

• Item response theory (IRT), 4PLM, EMMM, BEMMM

Statement dc:rights

• Copyright 2018 Ci Zhang

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/101297

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/101297