The Survey Methodology Summer School 2024, jointly organised by the European Survey Research Association (ESRA) and the Centre for Social Informatics, Faculty of Social Sciences, University of Ljubljana will take place from Monday the 1st of July 2024 until Wednesday the 3rd of July 2024 at the University of Ljubljana, Ljubljana, Slovenia.
Programme
- Oriol Bosch-Jover: Measuring Citizen’s Digital Behaviour using Web Trackers and Data Donations (full day) – 1 July 2024, 10am-5pm
- Caroline Roberts: The Essentials of Survey Methodology (full day) – 2 July 2024, 10am-5pm
- Olga Maslovskaya: (Non)-Probability Samples in the Social Sciences (half day) – 3 July 2024, 10am-1pm
- Vera Lomazzi: Total Survey Error (TSE) in Cross-National Surveys (half day) – 3 July 2024, 2pm-5pm
Interested parties can attend either individual courses or all courses. For individual courses the fees are £35 for a half day course, £50 for a full day course, or £125 if you would like to attend all courses. We will offer free participation for the first 25 applicants who are residents of Slovenia, Croatia, Serbia, Bosnia, Montenegro, Kosovo or North Macedonia. Places are limited and allocated on first come, first served basis. Registration is now open (just log into your ESRA account and click on “Book Short Course”). For suggestions on accomodation, see here.
Detailed Programme
Course 1: Measuring Citizen’s Digital Behaviour using Web Trackers and Data Donations
Course Description:
The expansion of the Internet, together with the capabilities of modern connected devices, result in a plethora of data that promise fascinating opportunities to understand individuals’ digital behaviours. This course will teach students how to measure digital behaviours using web trackers and data donations, and how to combine these approaches with online surveys. The course has the following learning objectives:
- Develop an understanding of what web tracking data and data donations are.
- Learn how web tracking data and data donations can be collected and analysed, and how they can be combined with surveys.
- Recognise the challenges and errors that might arise in every step of the process of collecting and analysing both data sources.
- Develop best practices when using this type of data, specifically, strategies to quantify, minimise and report potential errors.
- Evaluate the limits of their own and others’ web tracking and data donation collection strategies.
To aid students achieve the above learning outcomes, the course will have two interactive activities:
- Using the unique TRI-POL open access datasets, a cross-national longitudinal survey combined with web tracking data, students will familiarise with a web tracking dataset. Likewise, students will learn how to use computational methods such as Monte Carlo simulations and machine learning to quantify the data quality of digital trace data.
- Students will get hand-on experience about a specific type of data donation: screenshots and video recordings of the Digital Wellbeing / Screen Time from Android and iOS, which provide information of the time spent on apps and webs from individuals’ devices. They will learn how to automatize the extraction of information from those screenshots. Specifically, students will learn how to run an R script that sends images to Google Vision API, extracts the text from the images, and creates a workable structured dataset.
Bio:
Dr Oriol Bosch-Jover is a Postdoctoral Researcher at the Leverhulme Centre for Demographic Science at the University of Oxford, and Nuffield College. He is also a Research Fellow at the Research and Expertise Centre for Survey Methodology (Universitat Pompeu Fabra). As a computational methodologist, Oriol specialises in the use of survey and computational methods to understand how scientists can best collect and analyse new sources of data, such as digital trace data. He specialises in topics related to web and mobile surveys and the use of digital trace data and sensors to enhance or substitute surveys. His work has been published in journals such as Social Science Computer Review or the Journal of the Royal Statistical Society.
Oriol currently focuses on how social scientists can best collect information about citizens’ online behaviours using data donation and web trackers. Through a combination of survey and computational methods, his research explores how to quantify and minimize digital trace data errors, while comparing them with the ones of surveys. In his current role in Oxford, he is working on the development of a state-of-the-art data donation infrastructure.
Before joining Oxford, Oriol completed his PhD in Social Research Methods at The London School of Economics, an MSc in Survey Methods for Social Research from the University of Essex, and a BSc in Political Science from Pompeu Fabra University. Additionally, Oriol has extensive research experience, having worked as a researcher or consultant for The Alan Turing Institute, Wellcome Trust, University of Southampton, University of Mannheim, and the Institute for Social and Economic Research.
Course 2: The Essentials of Survey Methodology
Course Description:
This short course provides an introduction to the design and implementation of quantitative social surveys, and different procedures for maximising the quality of survey data. It will consider the various steps involved in conducting a survey and the challenges that can arise along the way. In particular, it will address the major sources of ‘survey error’ that result from these challenges and their potential effects on the accuracy of the data collected. It will present ways of minimizing the impact of survey errors on data quality and of ensuring the validity of the research findings.
Participants will be introduced to key principles in survey methodology that relate to the quality and cost of survey estimates. A significant component of survey error arises from the design of questionnaires, the mode of questionnaire administration, and the way in which respondents answer questions in different modes. Much can be done to mitigate such errors at relatively low cost. For this reason, the course will focus particularly on these aspects of survey design and implementation, to help survey designers recognize and address threats to measurement quality before collecting data, and make analysts aware of potential problems when using survey data.
The course is suitable for people starting out in survey research, whether they are responsible for conducting surveys, analysing survey data or both.
At the end of the course, students should be able to:
a) Summarise the key sources of error that impact on the accuracy of survey estimates and procedures typically used to minimise them;
b) Describe ways in which different survey design choices influence the risk of measurement error;
c) Follow best-practice guidelines on how to mitigate measurement error through effective questionnaire design.
Bio:
Dr Caroline Roberts is a senior lecturer in survey methodology and quantitative research methods in the Institute of Social Sciences at the University of Lausanne (UNIL, Switzerland), and an affiliated survey methodologist at FORS – the Swiss Centre of Expertise in the Social Sciences. At UNIL, she teaches courses on survey research methods, questionnaire design, public opinion formation and quantitative methods for the measurement of social attitudes. She has taught a number of summer school and short courses on survey methods, questionnaire design, survey nonresponse, mixed mode surveys, and data literacy. At FORS, she conducts methodological research in collaboration with the teams responsible for carrying out large-scale academic surveys in Switzerland, including the ESS, the ISSP, the EVS, the Swiss Election Studies and the Swiss Household Panel Survey. Her research interests relate to the measurement and reduction of nonresponse bias and other types of survey error – most recently in the context of surveys on smartphones. Caroline is currently Chair of the Methods Advisory Board of the European Social Survey and was President of the European Survey Research Association from 2019-2021 and Conference Chair from 2017-2019.
Course 3: (Non)-Probability Samples in the Social Sciences
Course Description:
The objective of the course is to provide students with a brief overview of the history, definitions and accumulated empirical evidence surrounding the debate about probability and nonprobability sample surveys. The benefits and challenges of different approaches to probability and nonprobability sampling (e.g., simple vs. stratified random sampling, snowball vs. respondent-driven sampling, social media and river sampling) will be introduced and discussed. A focus will be on real-world examples of why and how the choice of sample type matters. This course equips researchers and survey practitioners with necessary skills and knowledge to make evidence-based decisions on designing, utilising and evaluating (non-)probability sample survey data.
Level: Introductory
Bio:
Dr Olga Maslovskaya is Associated Professor in Survey Research and Social Statistics at the University of Southampton. She is a Survey Methodologist and is interested in all aspects of surveys, her special interests are the areas of survey data collection, data quality, and (non)-probability surveys. Olga is Deputy Director for ESRC-funded Survey Futures project which is Survey Data Collection Methods Collaboration in the UK. She also leads Research Strand on Methods for Surveys without Field Interviewers within the collaboration. Olga is also a Co-Investigator (Co-I) on ESRC-funded project “The Generations and Gender Survey (GGS) in the UK: Investigating demographic changes in the family and advancing online survey methodology“. She leads the workpackage which is responsible for the design of the survey and also for methodological experiments implemented in the first wave of the survey in the UK. Olga is a Co-I on ESRC-funded project “Understanding Coverage in the UK Population Longitudinal Studies”. Olga has an extensive experience of analyzing wide range of large-scale datasets and of employing various advanced statistical methods in different research contexts.
Course 4: Total Survey Error (TSE) in Cross-National Surveys
Course Description:
When conducting or using data from cross-sectional surveys several elements can compromise the quality of the survey data. This short course introduces key aspects of Total Survey Error in cross-national surveys.
The Total Survey Error framework is the most meaningful approach to understand and address biases that can arise in any phase of the survey cycle (design, data collection, processing, analysis, etc.). Cultural differences may intervene and further impact on the total survey quality.
Using examples of cross-national surveys, the course presents challenges and potential errors involved, their consequences for meaningful comparative research and possible mitigation strategies.
Level: introductory
Bio:
Dr. Vera Lomazzi is assistant professor in sociology at the University of Bergamo (IT). Until 2021, she was senior researcher GESIS – Leibniz Institute for the Social Sciences. She is secretary of the Executive Committee of the European Values Study and President of the European Survey Research Association. Her research covers comparability issues, European values, with a focus on gender equality and solidarity.
Suggested Accommodation
- Holiday Inn Express, 3 stars, Address: Podmilščakova ulica 51, Price: 105 €, Distance from faculty: 2 km
- Austria Trend Hotel, 4 stars, Address: Dunajska cesta 154, Price: 106 € + 15% discount for faculty visitors, Distance from FDV: 1 km
