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Department of Leadership, Higher and Adult Education

Courses

Quantitative Research Methods Courses

The Department of Leadership, Higher and 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 slide deck from the September 2017 information session on statistical methods course offerings.  

 

Introductory and intermediate courses

Note: Students unsure whether to take the introductory (JOI1287) or intermediate (JOI3048) statistics course should take this self-administered, non-credit placement test.

To complete the test, click the “+Enrol” button, log in, click “Submit” and “OK.” You will be required to complete a demographic survey before attempting the test.


INTRODUCTORY

Introduction to Applied Statistics (JOI1287H)  

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.

Schedule: Usually offered every year in the Fall Session


INTERMEDIATE

Intermediate Statistics in Educational Research: Multiple Regression Analysis (JOI3048H)

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.

Instructor: Anna Chmielewski
Schedule: Usually offered every year in the Winter semester
Prerequisite: An introductory statistics course, such as Introduction to Applied Statistics (JOI1287H), or equivalent)

Advanced courses

Multi-level and Longitudinal Modelling in Educational Research (LHA600X)

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.

Instructor: Anna Chmielewski
Schedule: Usually offered every 2 years
Prerequisite: An intermediate statistics course, such as Intermediate Statistics in Educational Research: Multiple Regression Analysis (JOI3048H), Intermediate Statistics and Research Design (JOI1288H) or equivalent. 


Quantitative Research Practicum (LHA6003)

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.

Instructor: Scott Davis
Prerequisite: An intermediate statistics course, such as Intermediate Statistics in Educational Research: Multiple Regression Analysis (JOI3048H), Intermediate Statistics and Research Design (JOI1288H) or equivalent.