Diversify your research toolkit with methods from behavioural science.
Quant-TIDE provides graduate students from engineering and the social sciences with interdisciplinary training and research support. Quant-TIDE is a 5-day workshop and accelerator. Days 1 to 3 provide training in advanced methods from the quantitative social sciences. Days 4 and 5 are a research accelerator where Quant-TIDE students get training in grant writing and pitch presentations. Quant-TIDE students will propose and pitch a research project that ties their research to a method they learned during the first three days of Quant-TIDE and will receive an honorarium of $500 from the ESS Consortium. Quant-TIDE is open to any Canadian graduate student in engineering or the social sciences with intersecting gender, race or ethnicity, LGBTQ2S+, socioeconomic, or disability identities that are underrepresented in Canadian STEM.
August 8 – August 12, 2022
5 days. 3 methods workshops. 1 research accelerator. A lifetime of knowledge.
Day 1: Fundamentals on Deep Learning
Deep learning, a class of machine learning methods inspired by information processing in the human brain, has revolutionized the way we build machines, automate processes, analyze data, and just problem-solve in a fast-increasing host of domains. These transformational changes are impacting wide sectors of society. The goal of this course is to provide a conceptual introduction to deep learning. Namely, we will cover Feedforward Networks, Recurrent Neural Networks, Convolutional Neural Networks, and the Transformer. The course will touch on a wide range of applications across different types of data modalities (e.g., speech, language, image) and provide a number of engineering tutorials (to be discussed at a high level). The main objective of the course is to support learners with basic or no prior knowledge of machine learning. Some familiarity with the Python programming language, basic linear algebra, basic calculus, and basic probability will be useful but not required.
Day 2: Multilevel models (MLM)
Multilevel modeling (MLM) is a statistical analysis that is used to analyze datasets in which cases are not independent (e.g., repeated measures, members from the same team, members from the same dyad). This analysis is more powerful than within-subjects ANOVA because it harnesses the statistical power of within-subjects designs and allows continuous predictors to be easily incorporated. This workshop will offer a practical introduction to MLM, and include some advanced topics such as moderation in MLM, multilevel mediation, effect size calculations, and generalized MLM models. Workshop materials will include example data and syntax for SPSS and R.
Day 3: Structural Equation Models
In this workshop, I will be offering a gentle introduction to Structural Equation Modeling (SEM), a general and flexible framework for modeling data (e.g., as observed and latent variables, with multiple effects, and accounting for measurement error). In the first part of the workshop, we will discuss what SEM is and how it compares to other analytic methods (e.g., regression analysis). In the second part of the workshop, we will practice conducting various SEM models (e.g., a path model, confirmatory factor analysis) using the lavaan package (Rosseel, 2012) in R (R Core Team, 2021). Here we will learn how to assess how well our models conform to our data, and discuss issues that often arise when fitting SEMs.
Days 4 & 5: Interdisciplinary Incubator
Quant-TIDE students receive training in grant writing and proposal presentations, receiving support from the expert workshop leaders. Quant-TIDE students can submit proposals to apply the knowledge learned in Quant-TIDE to their own research and will receive an honorarium of $500 from ESS in return.
We seek to increase the gender diversity and inclusivity of STEM in a way that honours the differing degrees of privilege and oppression that affect women and gender minorities who are Black, Indigenous, and People of Colour (BIPOC), identify with the LGBTQ2S+ community, grew up with low income, or live with a disability.
Engendering Success in STEM Consortium
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Quant-TIDE Code of Conduct
What is a Safer Space? A safer space is a supportive, non-threatening environment where all participants can feel comfortable to express themselves and share experiences without fear of discrimination or reprisal. We use the word safer to acknowledge that safety is relative: not everyone feels safe under the same conditions. By acknowledging the experiences of…
When is Quant-TIDE?
Quant-TIDE will take place virtually over zoom from August 8 – August 12, 2022. Please sign up for our newsletter to stay informed!