Skill School at the Lund Social Science Methods Centre
Organised by the Lund Social Science Methods Centre in autumn 2026
The Lund Social Science Methods Centre offers six workshops in methods and methodologies from mid-September through mid-October in 2026. These are for teachers, researchers, and doctoral candidates who desire to continue to broaden and deepen their knowledge of research methods.
How do I sign up for the Skill School workshops?
You can find the Skill School offerings in 2026 below. If you are interested in attending any of the workshops, please submit the application form below.
You will be notified as soon as possible if you get a place or if you are in the waiting list for the workshop(s) you would like to attend.
If you have any questions about the workshop fees, location of the workshops etc., please visit the "Frequently Asked Questions" section below. Please direct any unanswered questions related to the content of the workshops to nils [dot] gustafsson [at] iko [dot] lu [dot] se. For questions related to practical concerns, email skillschool [at] sam [dot] lu [dot] se (skillschool[at]sam[dot]lu[dot]se).
Skill School Workshop offerings
In 2026 we offer six workshops, both qualitative and quantitative. Click on the workshop title to expand the detailed description.
Please note that some of the workshops are scheduled at the same time.
Instructor: Tullia Jack, Lund University, Department of Service Studies
Dates: 16–17 September (week 38), 09:15–12:00
TimeEdit schedule
Abstract
Is GenAI doomed to just make more AI slop, or can we harness it for our research? In this workshop we’ll play with GenAI across the research cycle: scoping literatures, brainstorming designs, data collection, familiarisation, summarising, exploratory coding, themes, comparisons and validation.
We’ll cover ethics, EU guidelines, GDPR, hallucinations, black-box tools and when to step away from GenAI. Bring a laptop, curiosity, and (optional) a non-sensitive dataset or project idea to experiment with. No prior AI skills needed.
About the instructor
Tullia Jack is an Associate Professor at the Department of Service Studies at Lund University in Helsingborg.
Her main research and teaching areas are Sustainability, Social practices and Consumption. She is currently investigating doing less in everyday life (IDLE) and is fascinated by how Gen AI is reshaping research.
Instructor: Benjamin Claréus, Kristianstad University, Department of Psychology
Date: 21, 22, 24, 28, 29 September (week 39 and 40), 13:00–16:00
TimeEdit schedule
Abstract
NVivo is a Computer-Assisted Qualitative Data Analysis Software (CAQDAS) with the potential of facilitating qualitative analysis, mixed methods research, and literature synthesis/review. It can be used to compile data from fieldwork, interviews, images, research articles and more, and to create and organize codes and notes during the analytic process.
During this workshop, we will discuss how CAQDAS in general and NVivo in particular can and can’t support our analytic endeavours. We will also familiarize ourselves with some of NVivo’s basic (e.g., node creation and merging) and more advanced functions (e.g., matrix coding).
Participants are encouraged to bring their own empirical material to work on during the workshop. The time spent in the classroom is about equally divided between discussions/demonstrations, and individual work where you can receive personalized advice, supervision, and guidance.
No prior knowledge of NVivo or qualitative methods is required. Please note that there are visual differences between NVivo for Windows and Mac, such that the Mac version is more stripped down. While Benjamin is knowledgeable about how to navigate both, all classroom demonstrations will be run on a Windows computer.
About the instructor
Benjamin Claréus is a PhD in Psychology and senior lecturer at Kristianstad University. He has been teaching research methods for the past few years. He has applied NVivo in his own research, for example, in conducting narrative analysis and in synthesizing literature for his dissertation.
Instructor: Nils Holmberg, Lund University, Department of Communication
Dates: 5–9 October (week 41), 09:00–12:00 (5–7 Oct.) and 13:00–16:00 (8–9 Oct.)
TimeEdit schedule
Abstract
This workshop is aimed at users who want to leverage AI and natural language interfaces to perform data analysis, generate data, and build applications — without traditional coding barriers. In the opening sessions, participants will explore AI-assisted workflows and low-code paradigms, learning how to structure analytical tasks as prompts and set up their working environment. Hands-on exercises guide attendees from asking questions to executing real analytical steps.
The workshop then deepens into practical application: cleaning and exploring data with AI, generating visualizations through natural language, and finally building lightweight apps and dashboards. By the end of the five days, participants will be able to translate problems into AI-assisted workflows, analyze and generate data using natural language, and build simple functional applications — while critically assessing the outputs AI produces.
About the instructor
Nils Holmberg is a senior lecturer at the Department of Communication, Lund University. He holds a PhD in Media and Communication Science from Lund University (2016) and has a background in experimental research on digital advertising, including the use of eye-tracking to study visual attention and comprehension in online environments. His current research focuses on computational content analysis in the social sciences, applying methods such as natural language processing and computer vision to analyze and visualize patterns in digital communication, with a particular emphasis on sustainability communication and the opportunities and limitations of AI-assisted analysis.
Instructor: Salla-Maaria Laaksonen, University of Helsinki
Dates: 12–13 October (week 42), 13:00–16:00
TimeEdit schedule
Abstract
Digital media platforms offer vast amounts of data that are of interest to social scientists. This course introduces students to the methodological apparatus of big data augmented ethnography (Laaksonen et al. 2017) for studying social media. This is a mixed-method approaches that builds on a combination of digital and computational methods, such as network analysis and computational content analysis, and more traditional approaches such as digital ethnography and qualitative content analysis. In essence, ethnographic observations can be used to contextualize the computational analysis of large data sets, while computational analysis can be applied to validate and generalize the findings made through ethnography. The course includes practical hands-on workshops on selected methods and tools that can be experimented with or without coding skills and complements them with exercises to help participants to explore the potential method combinations for their own research topics.
About the instructor
Salla-Maaria Laaksonen (Docent/Adjunct Professor, D.Soc.Sc) is a tenured Senior Researcher at the Centre for Consumer Society Research at the University of Helsinki. Her research concerns communication and organizing in the platform society, and the use of data, algorithms and AI in organizations. Her previous projects have focused on, for example, organizational reputation online, political communication on social media, and interactions with communicative AI. She has over 100 scientific publications, many in top communication journals. She is also an expert in digital and computational research methods, as well as an active ambassador of science communication and open science.
Instructor: Christopher Swader, Lund University, Department of Sociology
Dates: 12–16 October (week 42), 09:15–12:00
TimeEdit schedule
Abstract
The workshop provides an introduction to the R programming language for the purpose of data analysis in the social sciences. R is an increasingly popular scientific tool and often becomes the first-choice software for implementing newly developed statistical and computational methods, especially within academia. The main goal of the workshop is that participants learn the basic functionality of R language that covers the full cycle of data analysis including data loading, pre-processing, visualisation, modelling, and communication of the results. The practical work is based on real data problems and prepares participants for a whole range of diverse data analysis tasks.
About the instructor
Christopher Swader is an Associate Professor (Docent/Senior Lecturer) at the Sociology Department in Lund. His previous academic appointments were with the Higher School of Economics in Moscow (prior to 2015) and the University of Bremen in Germany. His work focuses on the connection between intimacy, modernization, and normative order, which he has approached through multiple and mixed methods. He serves as the founding Programme Director of the Social Scientific Data Analysis master programme at LU’s Graduate School. He has developed a model machine learning method (‘ICRegress’), visualizations, and robustness routines, available on GitHub, in addition to numerous innovation projects, each implemented using the R programming language.
Info about the master of science programme in Social Scientific Data Analysis
Instructor: Shelley Boulianne, School of Communication Studies, Mount Royal University and visiting Professor, Department of Arts and Cultural Sciences, Lund University
Dates: 15–16 October (week 42), 13:00–16:00
TimeEdit schedule
Abstract
Systematic literature reviews and meta-analyses are critical to scanning a field of research to identify research gaps, summarize areas of consensus, and inform practice/public policy. In the past, these evidence syntheses took hundreds of hours of work in searching, compiling, screening, identifying, and coding studies.
In this presentation, we will examine how AI could be used to conduct these evidence synthesis studies more quickly, comprehensively, and efficiently. For example, AI could be used to help craft search strategies, scan abstracts to determine relevance, and conduct a preliminary extraction of relevant data from the studies (e.g., when and where the study was conducted).
For this workshop, we will focus on HubMeta, which is a free web-based application. Please create an account before class begins. Presentation slides will be shared before class. A demonstration will be provided on each step of this evidence synthesis process (searching, compiling, etc.), and then there will be class time for participants to replicate the step for their own project, with assistance from the facilitator.
About the instructor
Dr. Boulianne is currently the R. Klein Research Chair (full professor rank) in Communication Studies at Mount Royal University (Calgary, Canada) and a visiting professor at Lund University. She has held professor positions in politics and international relations (University of Southampton, UK; Catholic University of Lille, France), sociology (MacEwan University, Canada), and communication (Mount Royal University, Canada). She was a visiting fellow at the Weizenbaum Institute for the Networked Society (Germany) and the Digital Democracy Center at the University of Southern Denmark. She is currently the North American editor at Information, Communication & Society (ICS) and an Associate Editor at Social Science Computer Review (SSCR). Her research examines the global dynamics of digital media use for citizen engagement in civic and political life. She has published multiple evidence syntheses related to digital media uses, political efficacy, protest participation, political consumerism, and online political participation. Currently, she has a Social Sciences and Humanities Research Council grant to test how AI can be used for evidence synthesis related to key public policy issues.
Frequently asked questions
Our Skill School workshops are primarily for teaching/research staff and doctoral students.
Students who sign up will only get a spot in the workshops that they want to attend in case there are openings. This only applies to students admitted to a master program at Lund University. If you are enrolled in Bachelor level studies or study at another institution, we will disregard your application.
All Skill School workshops are free-of-charge for all doctoral students at Lund University, as well as for all LU Faculty of Social Sciences staff.
For other cases, please sign up with your interest, and we will provide you with fee information for the workshops you are interested in attending. Alternatively, please send us an email at skillschool [at] sam [dot] lu [dot] se.
All the workshops will take place on campus in Lund, and it will not be possible to participate remotely. Detailed schedule and room information can be found under each relevant workshop listed above. If you are not familiar with the Paradise Campus where the majority of departments at the Faculty of Social Sciences are located, see this map.
Note that if you sign up for a workshop, but cannot attend, you must inform us two weeks before the workshop starts. Otherwise, your institution will be charged a no-show fee of 800 SEK for the missed workshop.
Our workshops are not designed as credit-bearing courses with officially established syllabi. However, you can ask your department/institution if you can earn credits if you submit a certificate of attendance with the description of the workshop (see next question about certificates of attendance).
Certificates for attending a workshop can be provided upon request from skillschool [at] sam [dot] lu [dot] se. Please note that you need to have attended 80% of the workshop to receive a certificate.
While some of our Skill School workshops are offered every year, we cannot guarantee that we will offer a certain workshop again in the future. Please check this page next spring for more information.
Contact
Contact nils [dot] gustafsson [at] iko [dot] lu [dot] se for questions about the content of the methods workshops that are not answered on this page.
Contact skillschool [at] sam [dot] lu [dot] se for questions of practical concern that are not answered on this page.