On the Use of Mixed -Effects Models for the Analysis of Probability Judgments
Johnson, Timothy Robin
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/82016
Description
Title
On the Use of Mixed -Effects Models for the Analysis of Probability Judgments
Author(s)
Johnson, Timothy Robin
Issue Date
2001
Doctoral Committee Chair(s)
Budescu, David V.
Department of Study
Psychology
Discipline
Psychology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Statistics
Language
eng
Abstract
In this dissertation I propose a general statistical modeling framework for the purpose of making inferences concerning the external correspondence (i.e., calibration and discrimination) of probability judgments. The statistical model is based on a new stochastic judgment model which is expressed as a mixed-effects ordinal probit regression model. This model can be used to derive several new model-based measures of external correspondence as well as establishing a means of deriving the sampling/posterior distributions of such measures. Issues of model specification and inference for applied research are discussed in detail. Several detailed examples are given.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
