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Autumn Methods Workshops 2022

Organised by the Faculty of Social Sciences in Autumn 2022

The Faculty of Social Sciences offers nine Autumn Workshops in Methods and Methodologies from mid-September through early November in 2022. These are for teachers, researchers, and PhD candidates who desire to continue to broaden and deepen their knowledge of research methods.

If you are interested in attending any of the Autumn Methods Workshops, please submit this form. The early application period ended on June 30, but we still have a few spots are available in some of the the workshops. 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. 

Please direct any unanswered questions related to the content of the workshops to nils [dot] gustafsson [at] isk [dot] lu [dot] se. For questions related to practical concerns, email buleza [dot] emerllahu [at] sam [dot] lu [dot] se & yagmur [dot] yilmaz [at] sam [dot] lu [dot] se.

Workshop fees

  • All workshops are free-of-charge for all Lund University PhD students. 
  • All workshops are free-of-charge for LU Faculty of Social Sciences staff.
  • For other cases, please sign up with your interest, and we will provide you with fee information. Alternatively, please send us an email.

Location

Please note that 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 below. 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.


Autumn Workshops in Methods and Methodologies

You may sign up for more than one workshop. Click on the workshop name to

The opportunities (4 qualitative and 5 quantitative workshops) are:

Qualitative methods workshops:
 

1. Qualitative Analysis and Coding using NVivo

Instructor: Tullia Jack, Lund University, Department of Service Management
Dates: 19–23 September (week 38), 9:15-12:00
Click here for the schedule and location info

2. Exploring Go-Alongs as a Qualitative Research Method

Instructor: Maggie Kusenbach, University of South Florida, Department of Sociology
Dates: 3–6 October (week 40), 9:15-12:00
Click here for the schedule and location info

3. Narrative Analysis

Instructor: Sébastien Tutenges, Lund University, Department of Sociology
Dates: 13-14 October (week 41), 9:15-12:00
Click here for the schedule and location info

4. Fieldwork, cross-cultural and feminist interviews

Instructor: Priscyll Anctil, Lund University, Department of Political Science
Dates: 27-28 October (week 43), 9:15-12:00
Click here for the schedule and location info

Quantitative methods workshops:
 

5. Crash Course in Basic Statistics

Instructor: Joost van de Weijer, Lund University, Humanities Lab
Dates: 19-23 September (weeks 38), 13:15–16:00
Click here for the schedule and location info

6. Data Visualization with R and ggplot2

Instructor: Johan Larsson, Lund University
Dates: 29–30 September (week 39), 13:15–16:00
Click here for the schedule and location info

7. Introduction to R

Instructor: Irina Vartanova, Institute for Futures Studies, Stockholm
Dates: 3–7 October (week 40), 13:15–16:00
Click here for the schedule and location info

8. Introduction to Python

Instructor: Nils Holmberg, Lund University, Department of Strategic Communication
Dates: 17–21 October (week 42), 13:15–16:00
Click here for the schedule and location info

9. Analysing network dynamics and peer influence processes with RSiena

Instructor: Christian Steglich, University of Groeningen and Linköping University
Dates: 31 October - 1 November (week 44), 9:15-12:00
Click here for the schedule and location info
 


Workshop descriptions

1. Qualitative Analysis and Coding using NVivo

Instructor: Tullia Jack, Lund University, Department of Service Management
Dates: 19–23 September (week 38), 9:15-12:00
Click here for the schedule and location info

Abstract: Computer-assisted qualitative data analysis software packages are promising for assisting with the analysis of qualitative data. But how to make the most of their potential? NVivo software is currently available for use for all Lund University researchers and teachers. This workshop will demonstrate how NVivo can help to address qualitative research problems, both in the abstract and via practical exercises. Over five half day sessions the workshop will familiarise participants with the NVivo interface, importing and coding various file types, cases and classifications, visualising data as well as running queries. These hands-on exercises will explore how NVivo can - not only store and sort data - but help you to make sense of empirical material, look for patterns and systematically illuminate your research questions.

Headshot of Tullia Jack

About the instructor

Tullia Jack got through her PhD in sociology with quite some help from NVivo. Following graduation she spent some extra time experimenting with NVivo's capabilities, and after delivering various NVivo courses (including this one for the past two years) she is more convinced of NVivo's usefulness in organising, comparing and diving deep into different qualitative data sets.
 

2. Exploring Go-Alongs as a Qualitative Research Method

Instructor: Maggie Kusenbach, University of South Florida, Department of Sociology
Dates: 3–6 October (week 40), 9:15-12:00
Click here for the schedule and location info

Abstract: Stationary interviews and observations are excellent methods that allow qualitative researchers to examine a wide range of topics and locations; however, they may not be ideal for all questions or settings that prompt our scholarly interest. In my own research, I have found that mobile go-alongs—a hybrid method that merges ethnographic observation and qualitative interviewing—can illuminate some blind spots and lead researchers to new discoveries. In addition to these benefits, go-alongs empower research participants and facilitate inclusion. Throughout the four-day workshop, we will discuss examples and key characteristics of go-along research. Most importantly, we will practice go-alongs in exercises and talk about how the method might benefit your own research, whether it is ongoing or planned for the future. The workshop is best suited for graduate students and faculty who have some prior experience with qualitative interviewing. Participants should be able to walk 45-60 minutes and willing to accompany others in unfamiliar places and situations.

Headshot of Maggie Kusenbach

About the instructor

The workshop is led by Maggie Kusenbach, Professor of Sociology at the University of South Florida, USA. Maggie developed go-alongs in the 1990s as part of her dissertation research on urban neighboring in Los Angeles. Since then, she has used variations of go-alongs in other research projects, currently in a study on street art and urban development. Maggie regularly teaches courses and writes about qualitative research methods. At the moment, she is working on a SAGE Research Methods book about go-alongs.  
 

3. Narrative Analysis

Instructor: Sébastien Tutenges, Lund University, Department of Sociology
Dates: 13-14 October (week 41), 9:15-12:00
Click here for the schedule and location info

Abstract: There is a long tradition of studying narratives in the social sciences, but recent years have seen a veritable explosion of interest in the subject. Definitions of “narrative” and “story” have multiplied, new analytical approaches have emerged, and old theories have been revisited. This course will introduce some of the leading analytical strategies for studying stories, storytelling, and the context of narrative production. Beginning with a general discussion of what narratives are, why we humans spend so much time on sharing them, and what they do to us, the course will present different ways of doing narrative analysis while giving participants the opportunity to get hands-on experience with performing narrative analysis of concrete empirical data. The course requires no prior knowledge of narrative analysis or narrative theory.

Headshot of Sébastien TutengesAbout the instructor

Sébastien Tutenges is an associate professor in the Department of Sociology at Lund University in Sweden. He is the editor-in-chief of the Nordic Journal of Criminology. His past publications include papers in Addiction, British Journal of Criminology, Social Problems, Tourist Studies, and other journals.
 

4. Fieldwork, cross-cultural and feminist interviews

Instructor: Priscyll Anctil, Lund University, Department of Political Science
Dates: 27-28 October (week 43), 9:15-12:00
Click here for the schedule and location info

This workshop focuses on emotions and embodiment in qualitative methods to comprehend the challenges of working in uneasy and cross-cultural fieldwork. It draws on feminist scholars’ experiences and methodological tools to critically discuss feminist multi-methods, ethics, the circularity of fieldwork, and security dilemmas during qualitative fieldwork research. Based on the workshop facilitator's previous work with female and male former combatants from different armed groups in Colombia, we will discuss the challenges of conducting feminist and cross-cultural individual and collective interviews as well as body-mapping workshops in (post)war settings. The workshop is based on collective knowledge building; therefore, participants must expect to engage in activities such as body-mapping, autobiography writing, or narration reading.

Headshot of Pricyll Anctil

About the instructor

Priscyll Anctil Avoine is currently a Marie Curie / Vinnova Fellow at the Department of Political Science, Lund University. She is a researcher in Feminist Security Studies, mainly focusing on war and post-war militancy of female ex-combatants from insurgent armed groups. In her work, she has extensively used participatory and creative methods in cross-cultural contexts, such as collective and biographical interviews, body-mapping, podcast creation, participant observation, and art-based workshops. 
 

5. Crash Course in Basic Statistics

Instructor: Joost van de Weijer, Lund University, Humanities Lab
Dates: 19-23 September (weeks 38), 13:15–16:00
Click here for the schedule and location info

Abstract: One aim of the social and the behavioural sciences is to give insight into characteristics in relatively large groups of individuals (usually called populations). These characteristics can be, for instance, opinions about issues, performance in particular situations, and so on. The individuals can be people in general, the inhabitants of a country, or members of a specific target group. 

The predominant strategy within the sciences is to measure the characteristic of interest in a small subset (usually called a sample), on the basis of which inferences are made about the population from which the sample was drawn. 

Statistical analysis is the tool for making this strategy possible. One aspect of this analysis is to give a concise description of the findings within the sample. The second aspect is to aid the researcher to generalize these findings to the population. In this subsidiary course, I will illustrate these processes using one or more concrete examples, and explain the terminology that is most commonly used within the analysis.

Portrait of Joost van de Weijer

About the instructor

Joost works as the Humanities Lab's methodologist. As part of this employment, he assists students and researchers in planning and implementing experiments and analyzing the results. He teaches an introductory and a follow-up course in the statistical analysis of experimental data.
 

6. Data Visualization with R and ggplot2

Instructor: Johan Larsson, Lund University
Dates: 29–30 September (week 39), 13:15–16:00
Click here for the schedule and location info

Abstract: In this workshop you will learn how to visualize data using R and the R package ggplot2. Data visualization is one of the primary tools for understanding and presenting data and can help uncover patterns that are otherwise hard to grasp. A central aspect of creating visualizations is making choices and there are plenty to make when it comes to data visualization, ranging from how the data is transformed before visualizing to the choice of color. In this workshop you will learn how to make the right choice in a variety of situations and for several different real data sets. We will also cover some of the more theoretical aspects of data visualization, such as the Grammar of Graphics, which is the theoretical framework upon which ggplot2 is built, as well as several principles that will help make your visualizations clear, effective, and engaging.

Portrait of Johan Larsson

About the instructor

Johan Larsson is a final year PhD student in Statistics at Lund University. His research focuses on sparse generalized linear models for variable selection in high-dimensional data settings. He also teaches a course on data visualization at the Department of Statistics and has developed software for visualizing data.
 

7. Introduction to R

Instructor: Irina Vartanova, Institute for Futures Studies, Stockholm
Dates: 3–7 October (week 40), 13:15–16:00
Click here for the schedule and location info

Abstract: The workshop provides an introduction to R programming language for statistical data analysis in social sciences. R is an increasingly popular scientific tool and often becomes the first-choice software for implementing newly developed statistical methods. The main goal of the workshop is that participants learn the basic functionality of R language that covers the full cycle of statistical data analysis including data loading, pre-processing, visualisation, modelling, and communication of the results. The workshop is focused on the “tidyverse” collection of packages which are designed not just for the machine to execute but also for humans to read and thus are intuitive and easier to learn. The practical work is based on real data problems and prepares participants for a whole range of diverse data analysis tasks.

Portrait of Irina Vartanova

About the instructor

Irina Vartanova is a researcher at the Institute for Futures Studies. Having completed a PhD in psychology, she applies her data analysis expertise to working in an interdisciplinary environment and collaborating with colleagues from different fields. She specializes in advanced statistical and computational methods in social research and has a lot of experience in analyzing large survey data. Her research primarily focuses on social norms and how they change in different cultures. In her free time, she likes to learn new tricks to make her R code clearer and faster.
 

8. Introduction to Python

Instructor: Nils Holmberg, Lund University, Department of Strategic Communication
Dates: 17–21 October (week 42), 13:15–16:00
Click here for the schedule and location info

Abstract: In this introduction to Python for social scientists you will learn how to use the Python programming language as a unified platform for handling multiple tasks and workflows connected to running research studies, collecting data, and analyzing numeric and textual data. On the first day we will get familiarized with the Python command prompt, writing basic control structures, and utilizing the scipy stack (numpy, pandas, etc). On the second day, we will practice building an online survey experiment using psychopy package and collect data. The third day will be dedicated to performing basic statistics, data analysis and visualization with the seaborn package. On day four, we will use the spaCy package powered by machine learning libraries for various natural language processing applications. On the fifth and final day we will learn how to use jupyter notebooks and google colab for sharing and collaborating on Python code.

Headshot of Nils HolmbergAbout the instructor

Nils Holmberg has a doctoral degree in Media and Communication Science from Lund University in December 2016. The focus of his dissertation was to investigate the effects of web advertising on children aged 9-12 when they use the internet to solve different types of tasks, e.g. read and understand texts in an online newspaper. To investigate this, he used experimental methods to systematically vary the content and form of web ads. Physiological measuring equipment was then used to investigate how different advertising properties affected children's visual attention and ability to solve tasks online. He has used Python extensively over the years to collect and analyze data.
 

9. Analysing network dynamics and peer influence processes with RSiena

Instructor: Christian Steglich, University of Groeningen and Linköping University
Dates: 31 October - 1 November (week 44), 9:15-12:00
Click here for the schedule and location info

Abstract: This workshop gives an introduction to the statistical modelling of longitudinal social network data. Starting with general considerations about purpose and quality criteria for such models, we then focus on the model family of stochastic actor-based models, implemented in the RSiena program (Ripley et al., 2022). Longitudinal social network data are understood here as two or more repeated observations of a directed graph on a given node set (usually between 20 and a few hundred nodes). The workshop consists of two parts, each of which consists of a lecture combined with hands-on exercises using the RSiena package, part of the R statistical programming environment. Part 1 addresses the modelling of dynamic, sociocentric networks, Part 2 the analysis of network influence processes (aka contagion or diffusion) in dynamically changing, sociocentric networks.

Headshot of Christian SteglichAbout the instructor

Christian Steglich is a mathematical sociologist and network researcher. Since 2002 he is one of the developers of the SIENA software facilitating actor-based analysis of dynamic networks. His research focuses on formal modelling and statistical inference for social network data, with special emphasis on social influence processes. Most of Steglich’s work has an empirical focus, but he also employs simulation techniques to study emergent macro- and meso-level phenomena, such as normative behaviour, social hierarchies, and subgroup structures in networks.