Quantitative Research Methods Courses
The Department of Leadership, Higher & Adult Education offers many courses for students interested in statistical methods for education research.
Course offerings for the current year can be found using "Research Methodology" filter in OISE's course timetable tool. All statistical methods courses have a [RM] designation.
When planning and registering for courses, students should not only check LHAE courses, but also OISE-wide courses available to students in all OISE departments. Course codes for OISE-wide courses begin with "JOI."
For answers to frequently asked questions, check out the PowerPoint slide deck from September 2023 information session on statistical methods course offerings.
Note: Students unsure whether to take the introductory (LHA5014 or JOI1287) or intermediate (JOI3048 or JOI1288) statistics course should take this self-administered, non-credit placement test.
To complete the test, log in with your UTORid, click the “Enroll in Course” button, and click the “Go to the Course” button. The test is non-credit and will not reflect on your ACORN account or transcript. Recommendations will be presented to you upon completing the test and viewing your results.
Introductory Courses
This course provides an introduction to quantitative methods of inquiry and a foundation for more advanced courses in applied statistics for students in education and social sciences.
The course covers univariate and bivariate descriptive statistics; an introduction to sampling, experimental design and statistical inference; contingency tables and Chi-square; t-test, analysis of variance, and regression. Students will learn to use Excel software.
At the end of the course, students should be able to define and use the descriptive and inferential statistics taught in this course to analyze real data and to interpret the analytical results. No prior knowledge of statistics is required.
Department: LHAE
Schedule: Usually offered every year in the Fall Session.
This course provides an introduction to quantitative methods of inquiry and a foundation for more advanced courses in applied statistics for students in education and social sciences.
The course covers univariate and bivariate descriptive statistics; an introduction to sampling, experimental design and statistical inference; contingency tables and Chi-square; t-test, analysis of variance, and regression. Students will learn to use SPSS software.
At the end of the course, students should be able to define and use the descriptive and inferential statistics taught in this course to analyze real data and to interpret the analytical results.
Department: APHD
Schedule: Usually offered every year in the Fall and Winter Sessions.
Intermediate Courses
This is an intermediate applied statistics course designed for students who have already taken one course in elementary concepts (e.g., sampling and statistical inference). The course covers the use, interpretation, and presentation of bivariate and multivariate linear regression models, curvilinear regression functions, dummy and categorical variables, and interactions; as well as model-selection, assumptions, and diagnostics. Examples and assignments will draw from commonly-used large-scale educational datasets.
Students are encouraged to use Stata; the course will also serve as an introduction to this software package (however, students may instead choose to use SPSS or other software they are familiar with). The objective of the course is to equip students with the skills to use, interpret and write about regression models in their own research.
Prerequisite: An introductory statistics course, such as Introduction to Statistics for Educational Research (LHA5014H), Introduction to Applied Statistics (JOI1287H), or equivalent.
Department: LHAE
Schedule: Usually offered every year in the Winter semester.
This course will cover: survey sampling, experimental design, and power analysis; analysis of variance for one-way and multi-way data with fixed, mixed, and random effects models; linear and multiple regression; multiple correlation; analysis of covariance.
Prerequisite: An introductory statistics course, such as Introduction to Statistics for Educational Research (LHA5014H), Introduction to Applied Statistics (JOI1287H), or equivalent.
Department: APHD
Schedule: Usually offered every year in the Fall and Winter Sessions.
Advanced Courses
This is an advanced applied statistics course designed for doctoral or advanced master’s students and serving as a comprehensive introduction to multilevel modelling, also known as “hierarchical linear modelling (HLM)” or “mixed effects modelling.” These powerful models have become very common in educational research, both for the analysis of data with a multilevel structure (e.g., students nested in schools, school boards, provinces or countries) and for the study of educational change (e.g., student learning/growth, school improvement or organizational change).
The course covers two-level and three-level cross-sectional and growth curve models, as well as model selection, assumptions and diagnostics. Examples and assignments will draw on data from large-scale national and international datasets; the course will also serve as an introduction to the HLM7 software package.
The objective of the course is to equip students with the skills to use, interpret and write about multilevel models in their own research.
Prerequisite: An intermediate statistics course, such as Intermediate Statistics in Educational Research: Multiple Regression Analysis (JOI3048H), Intermediate Statistics and Research Design (JOI1288H) or equivalent.
Department: LHAE
Schedule: Usually offered every 2 years.
This course has several goals. The foremost is to prepare students wishing to conduct large scale data analysis for their theses or dissertations, to write a quantitative journal article, or to conduct a quantitative research project for a policy audience. Students will receive thorough guidance in the management and analysis of large scale data sets, including administrative and survey data.
Through the DEPE lab, students are provided access to several kinds of data sets (students can also use their own data in the course). Students will write their term papers in a journal article format and will tackle some form of quantitative data analysis.
A secondary goal of the course is to provide some supplementary instruction in statistical techniques. As students are expected to begin the course with knowledge of basic statistics, inference and multiple regression, the instructor will provide instruction in the broad topic of causal inference, and offer classes on categorical analysis and propensity score matching.
Finally, this course exposes students to issues in research design and novel forms of data collection.
Prerequisite: An intermediate statistics course, such as Intermediate Statistics in Educational Research: Multiple Regression Analysis (JOI3048H), Intermediate Statistics and Research Design (JOI1288H) or equivalent.
Department: LHAE
Schedule: Usually offered every 2 years.
