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Although qualitative secondary analyses are conducted across the social sciences, supra-assorted analyses that involve both the re-use of existing data and the collection of new, primary data are relatively uncommon. Additionally, discussions regarding qualitative secondary analysis have tended to ignore the re-use of researchers' own data (i.e., auto-data). Thus, with this article, we aim to contribute to this discussion by providing an example of a supra-assorted analysis in which we re-used data from one of our previous studies, Lone Mothers: Building Social Inclusion. This earlier, longitudinal study was conducted with 104 poor lone mothers across Canada. We supplemented this dataset with data from three focus groups and 20 semi-structured interviews engaging a total of 38 lone mothers. Both studies were informed by a feminist and social inclusion lens, and recruited a diverse sample of women in three cities across the country: Vancouver, British Columbia; Toronto, Ontario; and St. John's, Newfoundland. In addition, most of the lone mothers who participated in the secondary analysis had also been involved in the original study as interviewees and/or research assistants. We conclude the article by discussing the strengths and limitations of, and lessons learned from, the secondary study's design.
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