Names
Jack Winterton (LSE LIFE) & Ryan Corry (Economics).
Department
LSE LIFE & students from Anthropology, Economics, Economic History, Geography and Environment, Government and Law.
Overview
Quant4Qual brings together students from across departments in a space that has 1) a facilitatory teaching style (2) peer-to-peer learning (3) grounded-learning in case-study analysis. The aim of the project is to establish an entry point for students to gain confidence working with empirical research methods in the social science.
Target audience
Quant4Qual student-teachers
Undergraduate students with a passion for sharing their understanding of the social sciences through empirical research. Typically, this has been 3rd year Economics students who have excelled in their studies and have an adeptness in teaching.
Quant4Qual participants
Undergraduate students from across all departments and years of study. (We have a classroom with students from nearly every department and all undergraduate years of study!)
Details
We are jumping the hurdle before a student has integrated statistics into their curricula learning. The course adds complexity each week as we start from “a perfect experimental world” to a more complex, messy world. The course assumed no knowledge of statistics prior to participation, indeed, the only prerequisite was a desire to better understand an exciting area of social science research. Participants learn of some of the most common tools and techniques used to answer causal “what if” questions through the use of real case studies. The course follows a series of 5 workshops lasting for 1.5hrs each. Every Monday evenings at 6pm in LSE LIFE for 5 weeks.
The initial group discussion of the workshop does not mention empirical research methods at all. Instead, the focus is on voicing the opinions of the class about a contemporary social issue which we will then use to frame our discussion of a particular research tool. For example, the Randomised Control Trials session began with a discussion of the #BlackLivesMatter movement and, in particular, the iconic image of Leshia Evans at the Baton Rouge protest. This is a powerful individual moment of protest which stands on its own as noteworthy. But we also want to know how this individual might speak to a larger systemic problem of racism and police brutality in the United States. How would we go about doing this? How do we collect these anecdotes together in a meaningful way to answer this question? How do we know whether discrimination is present in a larger population and what does it looks like? Going forward, the workshop now has these questions to motivate our learning and application of empirical research methods.
We have a diverse audience of social science students, so contemporary social issues like BlackLivesMatter were used to connect these diverse voices. In this way, we are also allowing the students to bring something to class that they can contribute themselves (Moore 1997). This is inline with previous work that suggests that students filter new information through what they already know (Garfield 1995). The emphasis of Quant4Qual content begins with developing an understanding of the social context in which we collect and interpret data (Watson 2006), Gal (2004). For this reason, we did not measure the development of students based on ability to calculate and construe processes in formulas.
The atmosphere is informal as we serve tea, coffee, and muffins at the beginning of the session as we invite the participants to meet and chat with the student-teachers before the session begins. The space lends itself to interactive learning on a small group and larger classroom level. We host the sessions in the evenings to avoid clashing with timetables and we therefore see it as essential to prove some food to the students.
Below is a week-by-week summary of activity:
Week 1: Randomised Control Trials
Week 2: Randomised Control Trials (advanced)
Week 3: Ordinary Least Square Regression
Week 4: Instrumental Variables
Week 5: Sharing Session
This course exists within the context of undergraduate students often identifying as being affiliated with qualitative or quantitative disciplines. We sort to challenge the idea that students’ identity is either as a quantitative or qualitative student. This divide grows over a lifetime, at least in part, because many students experience poor achievement and a low sense of self-efficacy in mathematics. These negative experiences with mathematics often culminate with the feeling that “I don’t do maths (or statistics).” This mindset can lead to the misapprehension of statistical ideas to persist into further studies and later life (Garfield, 2003 in Nikiforidou and Pange 2010). In an increasingly data-driven society, this is a problem. The mindset can solidify in an elite university setting as students feel that they need to always perform at their best, showing no sign of weakness, and therefore the opportunity for foundational learning is lost. Quant4Qual deals with a small but significant part of this puzzle. We aim to build confidence with statistics. We are not aiming to create a new breed of statisticians. But the growing quantization of society and data-driven decision creates a demand for students to gain a greater appreciation of these methods (Moore 1997).
Impact
Survey to be completed soon - watch this space!
Next steps
Keep going. So often these student-led projects fizzle out. I’m very proud that we have allowed students to step outside of their comfort zone, and in doing so, they have challenged themselves to work outside of the rigid academic moulds set out for them.
We would advise others to have a plan to recruit for the next two years. Quant4Qual invites engaged first-year students to come along to learn the ropes from their more senior student-teachers.