Course Overview

The aim of this course is to provide a general introduction to path analysis, factor analysis, structural equation modeling and multilevel analysis. The examples and data are extensively drawn from literature in health and medical sciences. Students will learn how to use Mplus and Lisrel software to undertake these analyses. After attending the course, students should be able to describe the relationship between commonly used statistical methods and structural equation modeling (SEM); define the statistical concepts behind factor analysis, path analysis, and structural equation modeling; understand the relation between SEM and multilevel modeling (MLM); explain the above statistical methods and properly interpret their results; and use a computer software package to undertake the statistical analyses and correctly specify the statistical models. SEM has been very popular among quantitative social scientists in the last two decades, and has started to draw attentions from epidemiologists. SEM is a very useful tool for testing causal models, and learning SEM theory is very helpful for students to understand the causal assumptions behind different models. SEM is also useful for explaining the concepts of confounding, mediation and moderation in epidemiological research. The course will start with basic concepts of SEM, such as model specification, fitness testing, interpretation of causality and model modification. Then, more advanced topics will be introduced, such as equivalence models, identification issues, and multiple groups testing. MLM will then be introduced for the analysis of clustered data, where random effects may be viewed as latent variables. Students will be assessed by their participation in the classroom discussion, one interim and one final report on the critical appraisal of literature and real data analysis.

Learning Achievement

By the end of this course, students should be able to: Describe the relations between general linear models and structural equation models Explain the statistical theory of principal component analysis, exploratory and confirmatory factor analysis, path analysis and structural equation models Understand the concepts and rationales of causal models within the framework of structural equation models Understand the concept of mediation and the decomposition of total effects into direct and indirect effects Undertake structural equation modeling using statistical software packages and interpret the results properly Report the results from structural equation modeling properly

Competence

Course prerequisites

Active participation in class discussion and practical session is required.

Grading Philosophy

Course schedule

Course type

Online Course Requirement

Instructor

Tu, Yu Kang

Other information

(College of Public Health) Graduate Institute of Epidemiology and Preventive Medicine,
Common General Education Center Master Program In Statistics of National Taiwan University

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