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LHAE Quantitative Research Methods Courses

For students interested in learning statistical methods for educational research, many courses are available in the LHAE department and throughout OISE.

Research methodology offerings for the current year can be found by using the filter in the graduate course schedules. When planning and registering for courses, be sure to check not only LHAE courses but also courses with the OISE-wide “joint (JOI)” course code. JOI courses are open to students in all OISE departments.

Students unsure whether to take introductory (JOI1287) or intermediate (JOI3048) statistics, should take a self-administered placement test (Click the “+Enrol” button, log in with your UTORid, click “Submit” and “OK”. The test is non-credit. You will be required to complete a demographic survey before attempting the test. Recommendations will be presented to you upon completing the test and viewing your results.)

For answers to frequently asked questions, please check the slides prepared for the Information Session on Statistical Methods Offerings in LHAE and OISE, as of September 2017.

All statistical methods courses have a [RM] designation, so they may meet research methods course requirements in your degree program (see the OISE Graduate Bulletin or contact your Faculty Advisor to confirm the requirements of your degree).

Please note in the course descriptions below that intermediate and advanced statistics courses have prerequisites. Note also that introductory and intermediate courses are typically offered every year, while advanced courses are offered less frequently.


  • JOI1287H Introduction to Applied Statistics [RM]
    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. 
    Usually offered every year in FALL

Intermediate  (prerequisite: JOI1287 or equivalent)

  • JOI3048H Intermediate Statistics in Educational Research: Multiple Regression Analysis [RM]
    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 (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 JOI1287H or equivalent
    Usually offered every year in WINTER
    A.K. Chmielewski

Advanced (prerequisite: JOI3048 or equivalent)

  • LHA600X: Multilevel and Longitudinal Modelling in Educational Research [RM]
    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 JOI3048H, JOI1288H or equivalent
    Usually offered every 2 years
    A.K. Chmielewski
  • LHA6003: Quantitative Research Practicum [RM]
    This course will have 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 will be provided access to several kinds of data sets. If students have their own data, they can use their data in this course as well.  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 will be 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, I will provide instruction in the broad topic of causal inference, and offer classes on categorical analysis and propensity score matching. Finally, this course will also expose students to issues in research design and novel forms of data collection.
    Prerequisite: An intermediate statistics course such as JOI3048H, JOI1288H or equivalent
    S. Davies