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Social Science Methods

Research group
Active research
Project period
2025 - ongoing
Project owner
The Department of Sociology and Work Science
Area
Society and economy
Topic
Social sciences

Short description

This research seminar series focuses on methodological approaches in the social sciences, with a particular emphasis on methods that are less commonly used or that cut across disciplinary traditions.

About the seminar series

Each seminar is devoted to a single method, where the presenter shares experiences of applying it in practice, reflects on its strengths and potential applications, and discusses its limitations and challenges. The point of departure for the seminar can be a published paper, an ongoing project, or a more speculative exploration. 

The aim of the series is to foster methodological exchange across subject areas and to stimulate critical engagement with approaches that bridge or transcend conventional divides between qualitative and quantitative research. By engaging with alternative ways of doing research, the seminar offers a space for critical reflection, inspiration, and methodological innovation.

When and where

Unless otherwise noted, all seminars will be held in room F417, Skanstorget 18 

Seminars will be held in English unless otherwise noted.

Papers are distributed latest one week in advance if not otherwise noted.

Upcoming seminars: autumn 2026

23 November: When Is Noise a Surprise? Abductive Inference in Computational Analysis

Presenter: Dr Elena Bogdanova, Associate Professor in the Department of Sociology and Work Science.

When: 13:15–15:00
Where: Room F417

In this seminar, Elena will explore the role of surprise in computational research and its implications for abductive inference and theorising.

Drawing on an ongoing study of how sustainable investment is defined and constructed in the annual and sustainability reports of Swedish occupational pension providers between 2015 and 2025, she will discuss her use of BERTopic to move beyond theoretical categories established in earlier research. The analysis produced an unexpected finding: some of the passages most explicitly concerned with sustainability were classified as noise rather than forming stable topics.

Is this computational ‘noise’ telling us something theoretically interesting about how sustainable investment is constituted, or does it instead reflect the reporting genre, the composition of the corpus, or modelling choices?

Using this empirical puzzle as a starting point, the seminar will consider when unexpected computational results can become theoretically productive surprises, and what this means for abductive inference at a time when machine learning and generative AI are increasingly involved in identifying and summarising patterns in qualitative data.

The seminar should be of interest to anyone working with computational methods, AI and research methods, qualitative or mixed-methods research, or questions of theory development and inference.

Everyone is welcome. 

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