Sessional Lecturer - PPG2010H-S- Panel Data Methods for Policy Analysis
About the role
Course number and title
PPG2010H-S – Panel Data Methods for Policy Analysis
Course description
The course provides a rigorous introduction to statistical methods for the analysis of panel data with specific application to the major Canadian longitudinal data sets. This course is offered in collaboration with the Toronto Research Data Centre (RDC). The RDC provides secure access to Canada's preeminent panel data sets for public policy analysis as well as variety of other Statistics Canada data. The course will take place within RDC providing students hands-on experience with these important sources of information on public issues. The RDC offers both lecture space and a computer lab for tutorials. While the specific goal of this course is to introduce students to empirical methods for the analysis of longitudinal data, an important by product is their exposure to the RDC data. Instruction includes a combination of lectures and break-out groups. In break-out groups, students will complete series of problem sets that provide an introduction to the RDC panel data sets and practice in their analysis. The statistical methods reviewed will be drawn from a variety of disciplines to promote the inter-disciplinary study of public policy. Certain topics of particular relevance to the RDC panel data (e.g., cluster sampling, bootstrapping) will also be covered. The course is intended for a) MPP students from the Munk School of Global Affair & Public Policy; and b) students from departments, schools and faculties where small numbers preclude a similar course being offered, or that desire instruction in the use of data housed in the Toronto Region Statistics Canada RDC.
Estimated course enrolment
20 students
Estimated TA support
N/A
Class schedule
Tuesday 1-4pm
The delivery method for this course is expected to be in-person. Please note that, in keeping with current circumstances, the course delivery method may change as determined by the Faculty or the Department.
Sessional dates of appointment
January 1, 2026 - April 30, 2026
Salary
Sessional Lecturer I - $9,820.70; Sessional Lecturer I, Long Term - $10,510.04; Sessional Lecturer II - $10,510.04; Sessional Lecturer II (Long Term) - $10,760.28; Sessional Lecturer III - $10,760.28; Sessional Lecturers III (Long Term) - $11,030.36
Please note that should rates stipulated in the collective agreement vary from rates stated in this posting, the rates stated in the collective agreement shall prevail.
Minimum qualifications
Ph.D. with a specialization in statistical measurement and evaluation required. Extensive knowledge and background in statistical/quantitative methods in social sciences methodology required. Experience and knowledge of applying panel data methods in public policy required.
Preferred qualifications
Experience in teaching in a multidisciplinary context is an asset.
Description of duties
Teaching at the graduate level, developing the syllabus, teaching three-hour classes, providing weekly office hours for academic counseling of students, preparing and delivering course material, preparing and delivering assignments and tests, marking student work and submitting grades.
Application procedure
All individuals interested in this position must submit an updated Curriculum Vitae and the CUPE 3902 Unit 3 application form available at https://uoft.me/CUPE-3902-Unit-3-Application-Form to p.jory@utoronto.ca.
Closing Date
08/20/2025, 11:59PM EDT
This job is posted in accordance with the CUPE 3902 Unit 3 Collective Agreement.
It is understood that some announcements of vacancies are tentative, pending final course determinations and enrolment. Should rates stipulated in the collective agreement vary from rates stated in this posting, the rates stated in the collective agreement shall prevail.
Preference in hiring is given to qualified individuals advanced to the rank of Sessional Lecturer II or Sessional Lecturer III in accordance with Article 14:12 of the CUPE 3902 Unit 3 collective agreement.
Please note: Undergraduate or graduate students and postdoctoral fellows of the University of Toronto are covered by the CUPE 3902 Unit 1 collective agreement rather than the Unit 3 collective agreement, and should not apply for positions posted under the Unit 3 collective agreement.
About University of Toronto
Founded in 1827, the University of Toronto is Canada’s top university with a long history of challenging the impossible and transforming society through the ingenuity and resolve of our faculty, students, alumni, and supporters.
We are proud to be one of the world’s top research-intensive universities, bringing together top minds from every conceivable background and discipline to collaborate on the world’s most pressing challenges. As a catalyst for discovery, innovation, and progress, we prepare our students for success through an outstanding global education and commitment to inclusive excellence.
The ideas, innovations, and actions of more than 660,000 graduates advance U of T’s impact on communities across the globe.
Sessional Lecturer - PPG2010H-S- Panel Data Methods for Policy Analysis
About the role
Course number and title
PPG2010H-S – Panel Data Methods for Policy Analysis
Course description
The course provides a rigorous introduction to statistical methods for the analysis of panel data with specific application to the major Canadian longitudinal data sets. This course is offered in collaboration with the Toronto Research Data Centre (RDC). The RDC provides secure access to Canada's preeminent panel data sets for public policy analysis as well as variety of other Statistics Canada data. The course will take place within RDC providing students hands-on experience with these important sources of information on public issues. The RDC offers both lecture space and a computer lab for tutorials. While the specific goal of this course is to introduce students to empirical methods for the analysis of longitudinal data, an important by product is their exposure to the RDC data. Instruction includes a combination of lectures and break-out groups. In break-out groups, students will complete series of problem sets that provide an introduction to the RDC panel data sets and practice in their analysis. The statistical methods reviewed will be drawn from a variety of disciplines to promote the inter-disciplinary study of public policy. Certain topics of particular relevance to the RDC panel data (e.g., cluster sampling, bootstrapping) will also be covered. The course is intended for a) MPP students from the Munk School of Global Affair & Public Policy; and b) students from departments, schools and faculties where small numbers preclude a similar course being offered, or that desire instruction in the use of data housed in the Toronto Region Statistics Canada RDC.
Estimated course enrolment
20 students
Estimated TA support
N/A
Class schedule
Tuesday 1-4pm
The delivery method for this course is expected to be in-person. Please note that, in keeping with current circumstances, the course delivery method may change as determined by the Faculty or the Department.
Sessional dates of appointment
January 1, 2026 - April 30, 2026
Salary
Sessional Lecturer I - $9,820.70; Sessional Lecturer I, Long Term - $10,510.04; Sessional Lecturer II - $10,510.04; Sessional Lecturer II (Long Term) - $10,760.28; Sessional Lecturer III - $10,760.28; Sessional Lecturers III (Long Term) - $11,030.36
Please note that should rates stipulated in the collective agreement vary from rates stated in this posting, the rates stated in the collective agreement shall prevail.
Minimum qualifications
Ph.D. with a specialization in statistical measurement and evaluation required. Extensive knowledge and background in statistical/quantitative methods in social sciences methodology required. Experience and knowledge of applying panel data methods in public policy required.
Preferred qualifications
Experience in teaching in a multidisciplinary context is an asset.
Description of duties
Teaching at the graduate level, developing the syllabus, teaching three-hour classes, providing weekly office hours for academic counseling of students, preparing and delivering course material, preparing and delivering assignments and tests, marking student work and submitting grades.
Application procedure
All individuals interested in this position must submit an updated Curriculum Vitae and the CUPE 3902 Unit 3 application form available at https://uoft.me/CUPE-3902-Unit-3-Application-Form to p.jory@utoronto.ca.
Closing Date
08/20/2025, 11:59PM EDT
This job is posted in accordance with the CUPE 3902 Unit 3 Collective Agreement.
It is understood that some announcements of vacancies are tentative, pending final course determinations and enrolment. Should rates stipulated in the collective agreement vary from rates stated in this posting, the rates stated in the collective agreement shall prevail.
Preference in hiring is given to qualified individuals advanced to the rank of Sessional Lecturer II or Sessional Lecturer III in accordance with Article 14:12 of the CUPE 3902 Unit 3 collective agreement.
Please note: Undergraduate or graduate students and postdoctoral fellows of the University of Toronto are covered by the CUPE 3902 Unit 1 collective agreement rather than the Unit 3 collective agreement, and should not apply for positions posted under the Unit 3 collective agreement.
About University of Toronto
Founded in 1827, the University of Toronto is Canada’s top university with a long history of challenging the impossible and transforming society through the ingenuity and resolve of our faculty, students, alumni, and supporters.
We are proud to be one of the world’s top research-intensive universities, bringing together top minds from every conceivable background and discipline to collaborate on the world’s most pressing challenges. As a catalyst for discovery, innovation, and progress, we prepare our students for success through an outstanding global education and commitment to inclusive excellence.
The ideas, innovations, and actions of more than 660,000 graduates advance U of T’s impact on communities across the globe.