Teaching

This course serves as the first step in the graduate‐level statistics sequence in our Faculty—EDPY 505 (Quantitative methods I) and EDPY 605 (Quantitative methods II) follow. The purpose of this course is to present students with an introduction to descriptive and inferential statistics commonly used in social sciences research. I will emphasize three different aspects of statistical reasoning: (1) computational formulas and assumptions, (2) computer applications, and (3) appropriate uses of statistics in the applied settings. A thorough understanding of the topics covered in this course will prepare students for more advanced graduate work in educational statistics and ensure that students can conduct and interpret their own data analyses. 

Educational data mining is an emerging discipline aimed at analyzing large complex educational data sets for understanding data and extracting useful information. The purpose of this course is to 1) present students with a variety of educational data mining techniques, with an emphasis on conceptual understanding and applications; 2) help students learn how to implement these techniques with the statistical software R; and 3)  present students with a survey of research topics in the field of educational data mining.

This course serves as the second step in the three-course graduate‐level statistics sequence in the Faculty of Education, with EDPY 500 (Introduction to Data Analysis in Educational Research) as the prerequisite and EDPY 605 (Quantitative Methods II) as the next step. This course will provide students with a working knowledge of and skills in the analysis of data from experiments and surveys using regression and ANOVA techniques. Students will develop knowledge of and skills in understanding the underlying statistical models, matching statistical models to research designs, using computer software to conduct appropriate statistical analyses, and interpreting and reporting findings. A thorough understanding of the topics covered in this course will prepare students for more advanced quantitative work in educational research. 

The purpose of this course is to present students with multivariate statistical procedures so they can understand the logic of multivariate statistical analyses often conducted in social science research and properly use these statistical techniques in their own research. In addition, the laboratory component of the course will help students learn how to use computer software to conduct multivariate analyses. The following topics will be covered in class:

• Introduction to multivariate statistics

• Multivariate analysis of variance and covariance

• Discriminant analysis

• Logistic regression

• Multilevel linear modeling

• Principal components and exploratory factor analysis

• Structural equation modeling

The purpose of this course is to present students with an introductory background in the basic principles and applications of regression analysis and hierarchical linear modeling (HLM) in educational research.  This course starts with a brief review of simple linear regression analysis, followed by a discussion on multiple regression models. The majority of classes in this course will focus on the discussion of hierarchical linear modeling, which is becoming increasingly popular and important in educational and psychological research. This is because HLM allows one to consider the effects of common contexts shared by people (e.g., students in the same class). This is often ignored in traditional methods (e.g., regression), which possibly leads to erroneous conclusions. The course will review both the conceptual issues and methodological issues in using regression and hierarchical linear modeling.

This course provides a systematic introduction to the Mathematica programming language. It will help students learn how to use Mathematica to design functions and programs to solve problems arising in their research and graduate work. The course will use practical hands-on exercises to help students understand the material and provide a focused and practical learning experience.