Big Qual Analysis: Teaching Dataset

This teaching dataset is designed to help researchers and students get to grips with thinking about, handling and analysing large volumes of complex qualitative longitudinal data (QLR), including working with multiple archived data sets. The data set is an outcome of an ESRC National Centre for Research Methods research project 'Working across qualitative longitudinal studies: A feasibility study looking at care and intimacy'. The study examined the possibilities for developing new procedures and extending good practice for working across multiple sets of archived qualitative data. Our aim was to see whether it is possible to do Big Qual analysis across large volumes of complex qualitative material while retaining all that is distinct about rigorous qualitative research.

The teaching data set comprises transcripts and metadata from 356 in-depth qualitative interviews with 150 individuals (born between 1908 and 2001) whose lives were followed over time as part of the Timescapes Qualitative Longitudinal initiative (2007-2012). We extracted data from six of the core Timescapes projects, merged all the files into one data set, and then re-organised the material to enable you to explore the data over time and across the life course by gender and age cohort.

In the Dataset User Guide we detail the breadth-and-depth method we developed for analysing Big Qual data.

• Title: Big Qual Analysis: Teaching Dataset
• Data Creators: Weller, S., Davidson, E., Edwards, R. and Jamieson, L.
• Dataset Publisher: University of Leeds, UK: Timescapes Archive
• Published Date: 2019
• Identifier: https://doi.org/10.23635/14

Dataset Citation

Weller, S., Davidson, E., Edwards, R. and Jamieson, L. (2019) Big Qual Analysis: Teaching Dataset. University of Leeds, UK: Timescapes Archive. DOI: 10.23635/14

BigQual: 1950-1969_Men. [Case]

BigQual: 1950-1969_Women. [Case]

BigQual: 1970-1989_Men. [Case]

BigQual: 1970-1989_Women. [Case]

BigQual: Post_1990_Men. [Case]

BigQual: Post_1990_Women. [Case]

BigQual: Pre_1950s_Men. [Case]

BigQual: Pre_1950s_Women. [Case]