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Helping students design their own data analysis projects

Author: Melanie Beres
Institution: University of Otago (New Zealand)
Type of case study: Training

Teaching

Melanie Beres teaches ‘Quantitative Methods in Sociology’ at postgraduate level and part of a mixed methods course for undergraduates at Otago University, New Zealand. The main objective for Beres with the Quantitative Methods in Sociology course is: “that students understand basic statistics and how to think about what tests to use under specific circumstances.” For this course she has been using the teaching version of the British Crime Survey where students “created and designed their own secondary data analysis research project” and where they decided what relations they wanted to explore and also chose some variables from the data accordingly.

Beres also co-teaches a mixed methods course where she is in charge of the qualitative portion. For this course she initially used excerpts from Inventing Adulthoods, a data collection that deals with abstract notions of adulthood, but moved on to use Cross-Generational Investigation of the Making of Heterosexual Relationships:

“Content-wise it was easier for the students to get their heads around it. With this one it was easier for the students to come up with a research question and pursue it. Also others could go [on] to more abstract ideas like power in heterosexual relations.”

Similar to the statistics course, students also had to do their own original analysis on the data. She elaborates: “I give them an introduction and they work on it. They design a research question that’s answerable and they do the coding and a thematic analysis.” Beres also mentioned that the wealth of information comprised in the datasets is immense. One way to get around this issue was to select some cases and then let the students choose from those:

“What I did was reduce the number [of interviews] that was available to them, so I went through them and looked for the interviews that seemed the most detailed. Obviously some participants talked more than others, there [were] ones that were more in-depth and had a broad range, where participants were more engaged…. So I reduced it to eight [interviews] and within those they could choose which of five interviews they were interested in doing.”

Beres emphasized that students could relate to this specific data collection because it deals with issues about sex and young people’s values.

Among the benefits of using real data is its complexity, Beres explained:

“As social researchers teaching methods it’s not just teaching about what you do, or what the test does and how to interpret it, but understanding all of the stuff that goes into that…So there is a complexity in real data that you can’t get in other datasets that are artificial, nice, neat and clean. Real datasets are not like that.”