ESRA 2025 Preliminary Program

Monday 14 July    Tuesday 15 July    Wednesday 16 July    Thursday 17 July    Friday 18 July   

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Surveying Social Norms: Challenges for Survey Research 2
Session Organisers	Dr Ivar Krumpal (University of Leipzig) Mrs Anna Lena Fehlhaber (Leibniz University Hannover) Dr Anatol-Fiete Näher (Hasso-Plattner-Institut Potsdam)
Time	Tuesday 15 July, 11:00 - 12:30
Room	Ruppert C - 0.23

Monitoring collective action as well as individual behavior and attitudes via surveys and big data has posed new challenges to the survey discipline. This session aims at presenting and discussing current survey research on social norms. We aim to discuss best practices, new challenges and innovative designs that address both methodological and substantive problems related to the emergence, enforcement, change and decay of social norms. The explanation and measurement of norm compliance / deviance are also of central interest in our session.
We invite submissions that address these issues and/or present potential solutions. We also invite applications of norm research from any discipline. In particular, we are interested in studies that (1) deal with substantive problems and applications of survey research, such as norm compliance, deviant behavior, ethical preferences in regards to allocation or trolley problems; (2) present current survey research focusing on public opinion in regards to the emergence of new social norms, values and the production of collective goods; (3) deal with methodological problems such as nonresponse, social desirability bias or sampling issues presenting innovative designs and solutions addressing these problems; (4) discuss the use of artificial intelligence in the collection and analysis of data on social norms; (5) present experimental survey research (e.g. factorial surveys, conjoint experiments, field experiments) and statistical procedures to analyze such data (e.g. conjoint analysis); (6) integrate innovative experimental designs in well-established, large-scale population surveys of the general population; (7) discuss best practices in surveying social norms.

Keywords: Social norms, social desirability bias, nonresponse bias, factorial surveys, conjoint experiments

Papers

(Dis-)honest behaviour over time: A change of the social norm?

Dr Martina Kroher (Leibniz University Hannover) - Presenting Author

Social norms provide a framework for the social interaction of groups and societies. Using students as an example, we analyse the norm of honesty in exams and whether or how this norm has changed over the last decade. Deviations from the honesty norm are measured at the same German university at four different points in time (2013, 2016, 2020, and 2024) in order to identify changes in the norm as well as in the violations of the norm.
The focus of the analyses is on the self-reported (mis-)behaviour in exams and seminar papers of all students enrolled at this university in the corresponding semester. The respondents had to admit if they had ever cheated in a test (using crib sheets or copying answers from neighbours) or plagiarised in any way in a seminar paper. We are aware that this direct questioning on such sensitive issues will lead to some socially desirable or dishonest answers, nevertheless this so identified lower threshold may help to shed some light on the otherwise unknown extent of this deviant behaviour.
We always conducted a full survey, i.e. every enrolled student was informed by email and invited to participate in the survey. In total, between 1200 and 2600 students took part in one of the four online surveys.
Preliminary findings indicate a general downward trend in self-admitted misconduct, with students adapting their behaviour to the advancing technological capabilities. This leads to the highest share of cheating in the use of artificial intelligence in seminar papers. The contribution will examine the changing importance of different types of academic misconduct over time, taking into account reasons for and against cheating, field of study as well as socio-demographics.

Can Third-Person Recommendations Be Seen As (Societal) Attitudes? Evidence From a Factorial Survey on Retirement Home Placements

Dr Katrin Drasch (FAU Erlangen-Nürnberg) - Presenting Author

This presentation discusses lessons insights from a pilot study using a factorial survey to examine third person judgments about when an older person should be placed in a retirement home. It assesses societal attitudes towards institutional elderly care and the trade-off between freedom and security while addressing the social norm of "retirement home as last resort." The theoretical framework is based on a Rational Choice approach and the theory of successful aging (Steverink et al., 1998), incorporating an age gradient (Ormel et al., 1999). A web-based factorial survey was developed following Auspurg and Hinz's (2015) recommendations and recent methodological considerations (e.g., Treischl & Wolbring, 2022). The survey included D-optimal balanced dimensions at the vignette level (resolution IV-design; D-efficiency 93.9) and varied individual characteristics and personal circumstances, such as social embeddedness and care availability, across a high- and low-cost scenarios. The dependent variable measured participants' recommendation for a described person moves into a retirement home on a 7-point Likert scale. We found that several of the theoretical mechanisms indeed apply but the explanatory power and more important, the difference between recommendations for the fittest person as compared to the most fragile person are small. This makes us conclude that in German society, the reputation of care homes seems really poor and the Covid situation made this even worse. While aiming to assess behavioral intentions to infer broader societal attitudes, the overall unsatisfactory results lead us to question whether such inferences to the societal macro level are plausible due to differing aggregation processes influenced by social interdependencies or whether such inferences should better be avoided.

Tailoring The Collection Of Behavioural Survey Data To Parametrize Epidemic Models Incorporating Human Behaviour

Dr Vittoria Offeddu (DONDENA Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan)
Dr Maria Cucciniello (DONDENA Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan; Department of Social and Political Sciences, Bocconi University, Milan)
Dr Elisabetta Colosi (DONDENA Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan)
Dr Lorenzo Lucchini (DONDENA Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan; BIDSA Bocconi Institute for Data Science and Analytics, Bocconi University, Milan)
Miss Laura Leone (DONDENA Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan, Italy; Nuffield College, University of Oxford, Oxford; Department of Sociology, University of Oxford, Oxford)
Dr Duilio Balsamo (DONDENA Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan, Italy; BIDSA Bocconi Institute for Data Science and Analytics, Bocconi University, Milan)
Dr Francesco Bonacina (DONDENA Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan)
Dr Chiara Chiavenna (DONDENA Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan; BIDSA Bocconi Institute for Data Science and Analytics, Bocconi University, Milan)
Miss Elena D'Agnese (DONDENA Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan; Department of Economics and Management, University of Pisa, Pisa; Department of Statistics, Computer Science, Applications, University of Florence, Florence) - Presenting Author
Professor Alessia Melegaro (DONDENA Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan; BIDSA Bocconi Institute for Data Science and Analytics, Bocconi University, Milan)

Recent epidemics have shown that human behaviour can significantly affect the emergence and the transmission of infectious diseases. However, current epidemic modelling approaches are suboptimally equipped to incorporate behavioural components, because they are seldom grounded on behavioural science theory, and behavioural survey data is often unsuitable to directly inform model design.
In this work, we propose an interdisciplinary framework for the collection of behavioural survey data aimed at parametrizing epidemic models.
Between March and July 2024, we administered a cross-sectional survey to representative samples of adults of six European countries to investigate factors potentially influencing preventive behaviours under different epidemic conditions. We embedded validated behavioural theory into epidemic model design from the outset, tailoring the behavioural data collection to specific modelling needs. Before the full launch, the questionnaire was evaluated by experts from different scientific disciplines and the general public to ensure an appropriate balance between the validity and functionality of the collected behavioural data and its applicability to epidemic modelling.
The collected data will allow us to amplify traditional modelling methodologies. For instance, by expanding the existing definition of “social contacts” beyond in-person interactions to include discussion contacts, and assessing not only respondents’ opinions on multiple topics, but also how they compared to those of their social circles, we constructed discussion matrices to inform how interactions influence individual decision-making processes preceding behavioural choices. Our data also allows us to identify the psychological antecedents of behaviour, e.g. willingness to vaccinate, and to account for changes in willingness over time, capturing its dynamic relationship with changing epidemic conditions.
The proposed approach provides critical insights for incorporating behavioural factors into epidemic models through data-driven approaches, increasing our ability to design novel computational epidemic models that provide a more accurate and thus more policy-relevant representation of reality.