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EPSY 590: Advanced Seminar in Educational Psychology: Growth Curve Analysis for Longitudinal Data

This course is an intermediate/advanced statistics course for students who are interested in learning modern approaches to analyzing longitudinal data, such as growth in student reading, math, or science proficiency over time; changes in substance abuse, psychopathology, delinquency, employment; physical, cognitive, or emotional growth; or other change over time.

EPSY 480: Educational Statistics 

This course is designed to introduce educators, counselors and other social service professionals, administrators, and future researchers to the fundamentals of educational statistics. Educational statistics provide us with powerful tools for answering questions about how students learn and develop and how effective various educational techniques are.

EPSY 490: Developments in Educational Psychology, Multimedia Comprehension

This course is designed to introduce researchers, designers, and instructors to the comprehension process of multiple media designed for educational use. Multimedia presentations are ubiquitous in formal and informal education, including traditional textbooks—whether in paper or e-book format, Web-based hyperlinked materials, educational games, animations, simulations, and other new hybrids such as virtual reality haptics. They can include representations such as text, narration, diagrams, graphs, photographs, animation, sounds, and other representations.

EPSY 580: Statistical Inference in Education 

This course is designed to introduce educators, counselors, and future researchers to educational statistics and research methods beyond the basic level covered in EPSY480, with an emphasis on regression and Analysis of Variance (ANOVA) models. These statistics are powerful tools for answering questions from a wide range of research designs, especially experiments and cross-sectional (one-time) research.

EPSY/PSYC 581: Applied Regression Analysis 

Emphasis on educational research applications of regression with special emphasis placed on application and interpretation of techniques. Topics covered include rudimentary linear algebra, the general linear model, different coding schemes, regression diagnostics, and extensions to binary data and nested data structures. This course is designed to introduce future researchers and practitioners to details of regression analyses of various sorts, and to continue laying the foundation for advanced methods such as Multilevel/Hierarchical Linear Modeling and Structural Equation Modeling.

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