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| scope="col" width="80" |'''Date'''
| scope="col" width="80" |'''Date'''
| scope="col" width="115" |'''Time'''
| scope="col" width="135" |'''Time'''
| scope="col" width="670" |'''Description'''
| scope="col" width="650" |'''Description'''
| scope="col" width="125" |'''Language'''
| scope="col" width="125" |'''Language'''
| scope="col" width="210" |'''Link to Agenda and Materials'''
| scope="col" width="210" |'''Link to Agenda and Materials'''
|}
|}
=='''<small>Session 1 - Fundamentals</small>'''==
=='''<small>May 9, 2024</small>'''==
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| scope="col" width="80" |'''March 19'''
| scope="col" width="80" |'''2024-05-09'''
| scope="col" width="115" |1:00pm-3:00pm (ET)
| scope="col" width="135" |1:00pm-3:00pm (ET)
| scope="col" width="670" |''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.
| scope="col" width="650" |''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.
| scope="col" width="125" |English with bilingual materials
| scope="col" width="125" |English with bilingual materials
| scope="col" width="210" |[[Quantitative Impact Assessment Workshop/Fundamentals|Session 1 - Agenda and Materials <small>[click here]</small>]]
| scope="col" width="210" |[[Quantitative Impact Assessment Workshop/Fundamentals|Session 1 - Agenda and Materials <small>[click here]</small>]]
|}
|}
=='''<small>Session 2 - Data</small>'''==
=='''<small>December 7, 2023</small>'''==
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| scope="col" width="80" |'''March 21'''
| scope="col" width="80" |'''2023-12-07'''
| scope="col" width="115" |9am-12pm (ET)
| scope="col" width="135" |9am-12pm (ET)
| scope="col" width="670" |''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.
| scope="col" width="650" |''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.
| scope="col" width="125" |English with bilingual materials
| scope="col" width="125" |English with bilingual materials
| scope="col" width="210" |[[Quantitative Impact Assessment Workshop/Data|Session 2 - Agenda and Materials <small>[click here]</small>]]
| scope="col" width="210" |[[Quantitative Impact Assessment Workshop/Data|Session 2 - Agenda and Materials <small>[click here]</small>]]
|}
|}
=='''<small>Session 3 - Case Studies I</small>'''==
=='''<small>June 8, 2023</small>'''==
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| scope="col" width="80" |'''March 26'''
| scope="col" width="80" |'''2023-06-08'''
| scope="col" width="115" |9am-12pm (ET)
| scope="col" width="135" |9am-12pm (ET)
| scope="col" width="670" |''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.
| scope="col" width="650" |''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.
| scope="col" width="125" |English with bilingual materials
| scope="col" width="125" |English with bilingual materials
| scope="col" width="210" |[[Quantitative Impact Assessment Workshop/Case Studies I|Session 3 - Agenda and Materials <small>[click here]</small>]]
| scope="col" width="210" |[[Quantitative Impact Assessment Workshop/Case Studies I|Session 3 - Agenda and Materials <small>[click here]</small>]]
|}
|}
=='''<small>Session 4 - Case Studies II</small>'''==
=='''<small>December 15, 2022</small>'''==
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| scope="col" width="80" |'''March 28'''
| scope="col" width="80" |'''2022-12-15'''
| scope="col" width="115" |9am-12pm (ET)
| scope="col" width="135" |9am-12pm (ET)
| scope="col" width="670" |''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.
| scope="col" width="650" |''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.
| scope="col" width="125" |English with bilingual materials
| scope="col" width="125" |English with bilingual materials
| scope="col" width="210" |[[Quantitative Impact Assessment Workshop/Case Studies II|Session 4 - Agenda and Materials <small>[click here]</small>]]
| scope="col" width="210" |[[Quantitative Impact Assessment Workshop/Case Studies II|Session 4 - Agenda and Materials <small>[click here]</small>]]
|}
|}
=='''<small>Session 4 - Case Studies II</small>'''==
=='''<small>May 5, 2022</small>'''==
{|
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| scope="col" width="80" |'''March 28'''
| scope="col" width="80" |'''2022-05-05'''
| scope="col" width="115" |9am-12pm (ET)
| scope="col" width="135" |9am-12pm (ET)
| scope="col" width="670" |''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.
| scope="col" width="650" |''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.
| scope="col" width="125" |English with bilingual materials
| scope="col" width="125" |English with bilingual materials
| scope="col" width="210" |[[Quantitative Impact Assessment Workshop/Case Studies II|Session 4 - Agenda and Materials <small>[click here]</small>]]
| scope="col" width="210" |[[Quantitative Impact Assessment Workshop/Case Studies II|Session 4 - Agenda and Materials <small>[click here]</small>]]
|}
|}
=='''<small>Session 4 - Case Studies II</small>'''==
=='''<small>December 2, 2021</small>'''==
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{|
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| scope="col" width="80" |'''March 28'''
| scope="col" width="80" |'''2021-12-02'''
| scope="col" width="115" |9am-12pm (ET)
| scope="col" width="135" |9am-12pm (ET)
| scope="col" width="670" |''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.
| scope="col" width="650" |''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.
| scope="col" width="125" |English with bilingual materials
| scope="col" width="125" |English with bilingual materials
| scope="col" width="210" |[[Quantitative Impact Assessment Workshop/Case Studies II|Session 4 - Agenda and Materials <small>[click here]</small>]]
| scope="col" width="210" |[[Quantitative Impact Assessment Workshop/Case Studies II|Session 4 - Agenda and Materials <small>[click here]</small>]]
