Structural equation models combine factor analysis models and multivariate regression models to estimate associations between observed variables and unobserved variables. The main achievement of this Capstone Project is the understanding of structural equation models and application of the models to real-world data. In this report, we reviewed structural equation models and several prerequisite topics. We performed a simulation study to compare maximum likelihood structural equation model estimation versus two-stage sequential estimation using multiple linear regression and maximum likelihood factor analysis. The simulation study confirmed that confidence intervals produced by structural equation models are valid and those obtained by two-stage sequential estimation are largely inaccurate. We applied structural equation models to an educational data comparing the efficacy of teaching conditions on learning scientific inquiry skills among 177 middle school students in Fitchburg, Massachusetts using a computer simulated science microworld. Application of structural equation models to the educational data showed that there were no significant differences in test score gains between three learning conditions, while controlling for latent factors measured by survey responses.

Creator

• Legare, Jonathan Charles

Contributors

• Kim, Ryung S.

Degree

• MS

Unit

• Mathematical Sciences

Publisher

• Worcester Polytechnic Institute

Language

• English

Identifier

• etd-011509-161554

Keyword

• path diagrams
• Factor Analysis
• structural equation models

Advisor

• Kim, Ryung S.

Defense date

• 2008-12-01

Year

• 2009

Date created

• 2009-01-15

Resource type

• Report

Rights statement

• In Copyright