Teaching
Graduate Courses
PSY 611: Quantitative Data Analysis, Concordia University of Edmonton
This course covers statistical methods used to summarize and make inferences from quantitative data. The course provides students with an understanding of how to conceptualize and perform quantitative data analysis in psychological research. In the lecture component of this course, students learn the basics of quantitative research design, descriptive statistics, and inferential statistical methods. Topics include correlation, regression, multiple regression, moderation, mediation, t-tests, ANOVA, ANCOVA, MANOVA, and nonparametric tests. In addition, in the seminar component of this course, students become familiar with how to utilize SPSS to conduct various descriptive and inferential methods.
EDPY 505: Quantitative Methods I, University of Alberta
This course serves as the second step in the 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 provides students with a working knowledge of and skills in the analysis of data from experiments and surveys using regression and ANOVA techniques. Students 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 prepares students for more advanced quantitative work in educational research.
EDPY 500: Introduction to Data Analysis in Educational Research, University of Alberta
This course serves as the first step in the graduate‐level statistics sequence in the Faculty of Education––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. Three different aspects of statistical reasoning are emphasized: (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 prepares students for more advanced graduate work in educational statistics and ensures that students can conduct and interpret their own data analyses.
Undergraduate Courses
PSY 431: Theory and Practice of Psychometrics, Concordia University of Edmonton
This course serves as an introduction to measurement concepts and classical test theory (CTT), providing students with a solid foundation upon which to build their expertise in measurement. Through a critical lens, students explore the fundamental concepts of reliability, validity, and measurement bias as they apply to testing. Through hands-on activities and practical exercises, students have the opportunity to engage with selected psychometric techniques, gaining valuable experience in their utilization and interpretation.
PSY 311: Intermediate Statistics, Concordia University of Edmonton
This course is an intermediate course in statistical methods used in the social sciences with a focus on multivariate experimental and correlational techniques and the use of statistical software in the analysis of psychological data. As the course proceeds, a major component is learning how to properly use the Statistical Package for the Social Sciences (SPSS), a widely used computer software tool for statistical analysis. This course focuses on the proper application of different statistical procedures and the careful interpretation of computer output.
PSY 211: Statistical Methods for Psychological Research, Concordia University of Edmonton
This course is an introductory course in basic statistical methods. The topics include descriptive, inferential, and correlational techniques. This course helps students understand and appreciate the value of statistical procedures not only as they apply to the social sciences, but also as they apply to our thinking as social scientists. During this course, students further develop their analytical and critical thinking skills. Their knowledge of statistics helps them to address real-world issues in a precise and analytical fashion.
AUEPS 258: Educational Psychology for Teaching, University of Alberta
This course helps students explore the fundamental concepts and issues in educational psychology. The following topics are covered in class: developmental characteristics and theories, student differences and diversity, learning and thinking, creating a positive environment for learning and teaching, and assessing students’ capabilities. At the end of this course, students are able to: (1) develop an understanding of fundamental educational psychology concepts, and (2) apply these concepts in the classroom environment.
AUEDC 210: Introduction to Educational Technology, University of Alberta
This course helps students examine the frameworks, issues, and trends in educational technology for Alberta’s K-12 teachers. Lectures introduce a wide range of theoretical understanding of this field, and students have the opportunity to explore and practice digital technologies in the laboratory sessions. At the end of this course, students are able to: (1) understand key educational technology concepts and frameworks, (2) design technology-infused instructions, and (3) apply effective technology-infused instructions to the classroom.
AUSTA 153: Introductory Applied Statistics, University of Alberta
This course is an introductory statistics course focusing on fundamental statistical concepts to provide an understanding of the use of statistics in analyzing real-world phenomena. Topics include representation of categorical and quantitative variables, central tendency, variability, z-scores and probability, hypothesis testing, t-tests, correlation and scatter plots, and regression. Through these topics, students develop the research skills needed to examine and analyze data, and to present data visually. In addition to the lectures, this course has a laboratory component that is designed to help students examine, analyze, and interpret data.