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ERIC Number: ED569032
Record Type: Non-Journal
Publication Date: 2014
Pages: 108
Abstractor: As Provided
Reference Count: N/A
ISBN: 978-1-3038-8043-8
ISSN: N/A
Exploring the Estimation of Examinee Locations Using Multidimensional Latent Trait Models under Different Distributional Assumptions
Jang, Hyesuk
ProQuest LLC, Ph.D. Dissertation, Michigan State University
This study aims to evaluate a multidimensional latent trait model to determine how well the model works in various empirical contexts. Contrary to the assumption of these latent trait models that the traits are normally distributed, situations in which the latent trait is not shaped with a normal distribution may occur (Sass et al, 2008; Woods & Thissen, 2006). As a result when studies construct evaluations or comparisons in order to determine the appropriate estimation method and to avoid inefficient ones, the distribution or distributional statistics of the latent trait are considered as a key assumption. This study explores the performance of parameter estimation using a bifactor model, a type of multidimensional latent trait model in order to provide information of the effects of violations of the distributional assumptions. The effects of the distributional assumptions are evaluated using simulation studies. A 2-parameter logistic bifactor model with three factors: one general and two specific factors, is used as a basic multidimensional latent model. Simulation studies construct eight distributional conditions based on the degree of skewedness of the general factor, the directions of skewedness of the specific factors, the correlation between specific factors and four types of item parameter conditions. The results showed that item parameter estimation was affected by the degree of skewedness of the general factor, the directions of skewedness of the specific factors, and the correlation between specific factors. These conditions of the latent trait distributions had different effects on item parameter estimation depending on the type of item parameter. Based on the variances of the mean biases and correlations between generated and estimated parameters, the most important condition of the latent trait distribution for ? parameter estimation was the correlation between the specific factors. With the increasing number of studies and practical need for multidimensional structures of latent traits, this research provides useful guidelines for constructing appropriate multidimensional models. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]
ProQuest LLC. 789 East Eisenhower Parkway, P.O. Box 1346, Ann Arbor, MI 48106. Tel: 800-521-0600; Web site: http://www.proquest.com/en-US/products/dissertations/individuals.shtml
Publication Type: Dissertations/Theses - Doctoral Dissertations
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A