Difference between revisions of "Quantitative Impact Assessment Workshop"
Jump to navigation
Jump to search
m |
m |
||
| Line 55: | Line 55: | ||
{| | {| | ||
|+ | |+ | ||
| − | + | | scope="col" width="80" |'''Date''' | |
| − | + | | scope="col" width="115" |'''Time''' | |
| − | + | |'''Description''' | |
| − | + | | scope="col" width="125" |'''Language''' | |
| − | + | | scope="col" width="210" |'''Link to Agenda and Materials''' | |
|- | |- | ||
|'''March 19''' | |'''March 19''' | ||
| Line 87: | Line 87: | ||
{| | {| | ||
|+ | |+ | ||
| − | + | | scope="col" width="80" |'''Date''' | |
| − | + | | scope="col" width="115" |'''Time''' | |
| − | + | |'''Description''' | |
| − | + | | scope="col" width="125" |'''Language''' | |
| − | + | | scope="col" width="210" |'''Link to Agenda and Materials''' | |
|- | |- | ||
|'''March 26''' | |'''March 26''' | ||
| Line 103: | Line 103: | ||
{| | {| | ||
|+ | |+ | ||
| − | + | | scope="col" width="80" |'''Date''' | |
| − | + | | scope="col" width="115" |'''Time''' | |
| − | + | |'''Description''' | |
| − | + | | scope="col" width="125" |'''Language''' | |
| − | + | | scope="col" width="210" |'''Link to Agenda and Materials''' | |
|- | |- | ||
|'''March 28''' | |'''March 28''' | ||
Revision as of 14:48, 7 March 2024
Register Now!
.png)
Statistics Canada and the Treasury Board of Canada Secretariat invite you to participate in the third Quantitative Impact Assessment (QIA) workshop.
Quantitative Impact Assessment (QIA) Workshop (2024)
Statistics Canada (StatCan) and the Treasury Board of Canada Secretariat (TBS) have organized a four-day workshop on using QIA methods for program evaluation. Participants will learn the strengths and limitations of QIA methods and how to better leverage data.
Session 1 - Fundamentals
| Date | Time | Description | Language | Link to Agenda and Materials |
| March 19 | 9am-12pm (ET) | Fundamentals provides a comprehensive overview of QIA concepts. Participants will learn the difference between program outcomes and program impacts, and be introduced to the common approaches to QIA (such as difference-in-differences, discontinuity estimators, and more). This session also offers a detailed overview of randomized controlled experiments – the gold standard of QIA. | English with bilingual materials | Session 1 - Agenda and Materials [click here] |
Session 2 - Data
| Date | Time | Description | Language | Link to Agenda and Materials |
| March 21 | 9am-12pm (ET) | Data provides an overview of the data environments available through Statistics Canada. Participants will learn about performing custom tabulations and multivariate analysis, with specific emphasis on the availability of gender and diversity data and Quality of Life data. This session explains how to access existing microdata and overcome data gaps. | English with bilingual materials | Session 2 - Agenda and Materials [click here] |
Session 3 - Case Studies I
| Date | Time | Description | Language | Link to Agenda and Materials |
| March 26 | 9am-12pm (ET) | Case Studies I focuses on three real-life examples of QIA methods being applied to evaluate program performance and impact. These case studies primarily leverage social data. The methods covered are (1) empirical density design, (2) hierarchical linear modelling, and (3) propensity score matching and difference-in-differences. | English with bilingual materials | Session 3 - Agenda and Materials [click here] |
Session 4 - Case Studies II
| Date | Time | Description | Language | Link to Agenda and Materials |
| March 28 | 9am-12pm (ET) | Case Studies II also focuses on three real-life examples of QIA methods being applied to evaluate program performance and impact. These case studies primarily leverage business microdata. The methods covered are (1) propensity score matching and entropy balancing, (2) matching difference-in-differences, and (3) modified causal forest. | English with bilingual materials | Session 4 - Agenda and Materials [click here] |
Stay connected
- Join the Business Innovation and Growth Support Data Community of Practice
- Contact the Data Science, Research and Development unit
