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

Zhang, Ci

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https://hdl.handle.net/2142/101297 Copy

Description

Title

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

Author(s)

Zhang, Ci

Issue Date

2018-04-13

Director of Research (if dissertation) or Advisor (if thesis)

Zhang, Jinming

Committee Member(s)

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

Department of Study

Educational Psychology

Discipline

Educational Psychology

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

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

Abstract

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.

Graduation Semester

2018-05

Type of Resource

text

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

Copyright and License Information

Copyright 2018 Ci Zhang

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