The Swiss Student Life Study: Codebook
Timon Elmer, ETH Zürich
Kieran Mepham, ETH Zürich
Marion Hoffman, IAST Toulouse
Zsófia Boda, University of Essex
Isabel J. Raabe, University of Zürich
András Vörös, University of Manchester
Christoph Stadtfeld, ETH Zürich
Abstract
The Swiss StudentLife study was conducted between 2016 and 2020 (Vörös et al. 2021; Stadtfeld et al. 2019). Its aim was to understand the emergence of a student community and its short-term and long-term effects on the academic outcomes and well-being of its members. While similar questions and settings are commonly examined using a single data collection technique (e.g. survey, observation), we employ multiple methods to map the social dynamics of the community at different time periods and on different time scales. The study follows three cohorts of students (Cohort I: N = 226, Cohort II: N = 244, Cohort III: N = 652), who started 3-year bachelor programs at a Swiss technical university in 2016 and 2017. Members of each cohort shared most of their classes and often did course work in groups. Students came from various regions and countries; at the beginning of their studies, most of them did not know each other. However, after a few months they developed densely-knit multidimensional social networks, which were likely to influence their university experience and outcomes. This codebook document describes the various kinds of data that have been collected in the context of the Swiss StudentLife Study.
How to use the Codebook
The codebook document is designed to help (a) researchers who do not have access to the Swiss StudentLife data, to get detailed information on the data for potential data access requests or (b) researchers who have parts of the Swiss StudentLife data to acquire more details on the measures used in collecting the data.
Each data collection wave is stored in a separate R-object. The questionnaire data are in a data.frame format. The variables that are labeled as “Network” and have the same variable name (e.g., friend.P[1-20]) were transformed into adjacency matrices with the respective name (e.g., friend). These matrices are stored in an R-list with the respective wave name (e.g., net.L3, for wave 3 of the long questionnaire).
There are two types of missing codes:
NA: The person was part of the cohort at the given timepoint but did not participate in the respective questionnaire/data-collection.
-10: The person was not part of the cohort at the given timepoint, so he/she could not have participated in the questionnaire/data-collection.
For collaborations, the respective data can be transformed from R to other formats (e.g., csv, excel, SPSS).
Data Availability + Collaborations
In agreement with the ethics board of ETH Zürich (approval 2016-N-27 and 2017-N-42), we follow a strict protocol ensuring the anonymity of the study participants. Only highly anonymized data samples are shared on publicly available servers. The data are made available to other researchers in two cases:
Replication purposes: For replication purposes, parts of the dataset can be made available publicly. Two additional measures are taken to guarantee participant’s anonymity: First, variables that could identify individuals (e.g., gender, ethnicity, age) will be recoded such that any combination of variables will not yield a group N < 5. Thus, no participant can be identified with a certainty over 20%. For instance, if the combination of the variables gender and age, allow to identify a group of two females that are over the age of 30, the categories will be extended (e.g., participants over the age of 25) or one variable will be dropped from the publicly available data. Second, the anonymized student keys, that would make linking data of different releases possible, will not be released. Instead, they will be replaced by unique keys that can only be used in a given data-subset release.
Collaborations: If other researchers wish to use the Swiss StudentLife dataset for their own research, they can apply for access to specific parts of the anonymize data. To do this, please fill out the following form: Data Request Form.
More information on the study design, cohort information, participation rates, and other useful details on the data collection are documented in:
Vörös, A., Boda, Z., Elmer, T., Mepham, K., Hoffman, M., Raabe, I., & Stadtfeld, C. (2021). The Swiss StudentLife Study: Investigating the emergence of an undergraduate community through dynamic, multidimensional social network data. Social Networks, 65, 71–84.
Please cite this codebook as:
Elmer, T., Mepham, K., Hoffman, M., Boda, Z., Raabe, I., Vörös, A., & Stadtfeld, C. (2022). The Swiss Student Life Study: Codebook. doi: 10.31234/osf.io/9abjk
Study Design
The following types of data were collected in the Swiss StudentLife study. Further details on each of the data collection types can be found in Vörös et al. (2021).
Questionnaires: Every few months the cohorts were surveyed with an online questionnaire. The questionnaire assessed various social network dimensons as well as individual attributes (e.g., well-being, study motivation). A summary of all the blocks that were administered in these questionnaires can be found in Blocks of the Long Questionnaires and details on the items can be found in Long Questionnaires for Cohort I and Long Questionnaires for Cohorts II and III. The timing of all questionnaires are reported in Table Waves (Cohort I) and Waves (Cohort II and III). In Cohort I there were also two so-called Intermediate Questionnaires that aimed at assessing Ego-networks. Two waves were added in Cohort II and III after the submission/publication of Vörös et al. (2021), that related to the COVID-19 lockdown situation (Waves L11 and L12).
Short questionnaires: The participants of Cohort I received short questionnaires on their mobile phones in two critical phases (first two months of their time at the university and the month before the end-of-the-year exams). In these short questionnaires, students were asked about their social interactions with other students, their learning habits and their well-being. The detailed items of the short questionnaires can be found in Short Questionnaires and Summer Short Questionnaires. Two planned questionnaires were not administered: A6 and SS7.
Study records: From the university administration, we received detailed records of the students registration to study programs, courses and exams as well as information on their age, gender, country of origin and exam results. These variables are described in the two sections Variable Overview (Cohort I) and Variable Overview (Cohorts II and III).
RFID data: Participants of Cohort I (N = 59) took part in a ‘welcome-weekend’ after the first week of their studies. During this socializing weekend, all participants (100% participation rate) wore an RFID badge during their waking hours. RFID badges allow to measure face-to-face social interactions (for details see Elmer et al. (2019) and Elmer and Stadtfeld (2020)).
Experimental data: All cohorts were part of at least one experimental study, in which the meeting opportunities at a student introductory were randomized (Cohort I and II, for details see Boda et al. (2020)) and/or the seating arrangement of the introductory lecture was randomized (Cohort II and III).
Facebook API: 74 participants of Cohort I further took part in the Facebook part of the study by agreeing to allow the friendship information between study participants to be sent to the Swiss StudentLife team by the Facebook API. For details see Vörös et al. (2021). For a visualization of the co-evolution of online and offline friendship data, see video.
Blocks of the Long Questionnaires
Our long questionnaires consisted of (up to) 32 blocks of items. Here, we list them and a give a brief description of their contents. Not all blocks were asked in all waves of all cohort, for details on the timing and the specific items of these blocks, see the variable lists below.
Introduction: Introduction to the study and our informed consent sheet.
Contact: Any details on contact details that the student wished to change (i.e. phone number, email address).
Socio-demographic questions: As per title, questions on topics such as participants’ country of origin, gender, working/financial status.
Personal relationships: First network questions on relations, such as friendships, conflicts, and romantic relations
Joint activities: Network questions on shared activities, such as commuting to university together, co-studying, and spending free-time together
Social support: Network questions on emotional, instrumental, and informational social support.
Perceptions: Network questions on perceptions; e.g. who participants believe to be smart, who they admire, who they feel is (un)popular.
Social roles: Network questions on the social roles other individuals might fulfill, e.g. as an event organizer, a conflict resolver, or a social activity starter
First contact occurrence: One question on where students met those with whom they are closest for the first time.
Social groups: Participants name social in and outgroups here, name their members, and answer questions on topics such as their (perceived) undertaken activities, contact frequency and quality.
Other social contacts: Asked about individuals in and outside the cohort to whom they are close, and their attitudes towards the participants’ university participation
Identities: Participants assessed how much their personal identity overlapped with various social categories, groups, and study-related entities such as their gender, their department, and their university, using Schubert and Otten’s (2002) method.
Depression: This block contained the CESD-R depression scale (Hautzinger and Bailer 1993).
Anxiety: This block contained the GAD-7 anxiety scale (Spitzer et al. 2006; Löwe et al. 2008).
Stress: This block contained the PSS-10 anxiety scale (Cohen and Williamson 1988).
Study union integration: This asked about membership in the relevant departmental study union(s), participation in the activities that it organized, and use of its study-related resources.
Studying habits: Questions were related to studying habits, such as location and duration of studying sessions, whether this was alone or with others, and self-perceptions on the participant’s studying success such as how likely they were to succeed at their next exam.
Motivation: This block contained the SELLMO motivation scale (Spinath et al. 2002; Wilbert 2011).
Quitting intentions: Questions on whether the participant had considered quitting their major, and why.
Work values: Contains a module on work orientations authored for the International Social Survey Programme (2015).
Gender aptitude perceptions: This included a battery of items constructed by Isabel Raabe for the purpose of this questionnaire. They asked about comparative aptitude of men and women on ability in language learning, math, and natural sciences; how easily and seriously men and women took their major, and how likely men and women were to succeed in their major.
Free-time behavior: Included a modified battery of items from the ISSP questionnaire on leisure time and sports (ISSP Research Group 2009). These asked about participants’ free-time expenditure and their participation in various social organizations (such as volunteering at church events or sports organizations).
Consumption behavior: Asked about frequency of consuming drugs, alcohol, and tobacco, and participants’ frequency of partying.
Politics: Here we asked about participants’ attitudes towards varied modern political topics, upcoming referenda, and political media consumption.
Loneliness: Measured the titular construct with a short version of the UCLA Loneliness Scale (Russell 1996; German version by Döring and Bortz 1993).
Trait affectivity: Contained the PANAS by Watson, Clark and Tellegen (1988; German: Krohne et al. 1996).
Excessive reassurance seeking: This block contained Joiner and Metalsky’s (2001; German: Schwennen and Bierhoff, 2014) scale on excessive reassurance.
Social media use and attitudes: This included items on participants’ use of varied social media platforms, including frequency and duration, their perceptions on whether these were a net benefit or cost to their lives. It also included network items on whom from the cohort they regularly saw posts of on social media, of whom they saw annoying posts, and of whom they saw entertaining or useful posts.
Self-monitoring: This block contained the Self-Monitoring scale by Lennox and Wolfe (1984; German: Schyns and Paul 2014).
Big Five: This contained either a 44 (Lang, Lüdtke, and Asendorpf 2001) or 10-item (Rammstedt and John 2007) measure of the Big Five personality traits.
Emotional Recognition Index: This block contained the Emotional Recognition Index task (Scherer and Scherer 2011)
Feedback: Participants indicated agreement on various statements about the questionnaire, such as whether it was enjoyable, whether they had technical problems, and whether the compensation was fair. Additionally, they could give text feedback here.
(taken from Vörös et al., (2021), Supplementary Information)
Network Items
| Variable.name | Original.item.text | English.translation | Max.alters |
|---|---|---|---|
| cohabit.classmates | Mit wem wohnst du im Moment? | Whom do you live with at the moment? | 5 |
| known.before | Wen kanntest Du bereits vor Beginn des Studiums? | Who have you known since before the start of the degree? | 20 |
| interaction | Mit wem erlebst Du angenehme Interaktionen? | Who do you experience pleasant interactions with? | 20 |
| friend | Welche Deiner Mitstudierenden würdest Du als Freunde bezeichnen? | Which of your fellow students would you consider a friend? | 20 |
| relationship.classmate | Mit wem bist du zusammen? | With whom are you ‘together’? | 20 |
| dislike | Wen magst Du nicht besonders? | Whom do you not particularly like? | 20 |
| conflict | Mit wem hast Du persönliche Konflikte? | With whom do you have interpersonal conflict? | 20 |
| study | Mit wem lernst Du regelmässig gemeinsam für das Studium (z.B. gemeinsames Arbeiten an einem Projekt oder an Hausaufgaben oder gemeinsames Lernen)? | With whom do you study together regularly (e.g. collaborating on a project or homework, or communal studying)? | 20 |
| travel | Wen von Deinen Mitstudierenden triffst Du regelmässig auf Deinem Weg zur Universität (z.B. im Bus, Tram, oder Zug)? | Whom of your fellow students do you regularly meet on the way to University (e.g. on the bus, tram or train)? | 20 |
| freetime | Mit wem Deiner Mitstudierenden verbringst Du regelmässig Deine Freizeit? Hier kannst Du an alle möglichen Freizeitaktivitäten denken wie zum Beispiel, gemeinsamer Sport, Filmschauen, Wandern, Kaffeetrinken, Biertrinken, Tanzen, etc. | With whom of your fellow students do you regularly spend your free time? Here you may think of all possible free-time activities such as communal sports, watching movies, hiking, drinking a coffee or beer, dancing, etc. | 20 |
| inst.support | Auf wen kannst Du zählen, wenn Du Hilfe bei praktischen Dingen brauchst (z.B. beim Erledigen von Arbeiten oder die Bereitstellung finanzieller Mittel) unter deinen Mitstudierenden? | Who can you count on when you need help on practical things (e.g. getting work done or borrowing money) among your fellow students? | 20 |
| inf.support | Auf wen von deinen Mitstudierenden kannst Du zählen, wenn Du einen guten Rat oder spezifische Informationen brauchst? | Whom of your fellow students can you count on if you need good advice or specific information? | 20 |
| e.support | Auf wen von deinen Mitstudierenden kannst Du zählen, wenn es Dir schlecht geht und Du emotionale Hilfe brauchst (z.B. Trost, Mitleid und Zuspruch)? | Whom of your fellow students can you count on when you are feeling low and need emotional help (e.g. comfort, compassion and encouragement)? | 20 |
| clever | Wer ist besonders schlau unter deinen Mitstudierenden? | Who is particularly smart among your fellow students? | 5 |
| funny | Wer ist besonders lustig unter deinen Mitstudierenden? | Who is especially funny among your fellow students? | 5 |
| serious | Wer von deinen Mitstudierenden nimmt das Studium sehr ernst? | Who of your fellow students takes their studies very seriously? | 5 |
| disturb | Wer stört während der Vorlesungen (z.B. Sprechen, lautes Essen oder andere Dinge, die Mitstudierende vom Zuhören abzulenken)? | Who disturbs during the lectures (e.g. speaking, loud eating or other things that distract fellow students from listening)? | 5 |
| conceited | Wer von deinen Mitstudierenden ist eingebildet, hält viel von sich selbst? | Which of your fellow students is conceited, thinks a lot of himself? | 5 |
| aggressive | Wer von deinen Mitstudierenden ist aggressiv? | Who of your fellow students is aggressive? | 5 |
| attractive | Wen von deinen Mitstudierenden findest Du körperlich attraktiv? | Which of your fellow students do you find physically attractive? | 5 |
| party | Wer von deinen Mitstudierenden feiert viel? | Who of your fellow students parties a lot? | 5 |
| admire | Wen von deinen Mitstudierenden bewunderst Du? | Which of your fellow students do you admire? | 5 |
| popular | Wer von deinen Mitstudierenden ist Deiner Meinung nach sehr beliebt unter Deinen Mitstudierenden? | Which of your fellow students do you think is very popular among your fellow students? | 5 |
| lookdown | Auf wen Deiner Mitstudierenden schaust Du hinab? | Whom of your fellow students do you look down upon? | 5 |
| unpopular | Wer von Deinen Mitstudierenden ist unbeliebt? | Who of your fellow students is unpopular? | 5 |
| social | Welche Deiner Mitstudierenden würden am ehesten soziale Aktivitäten starten? | Which of your fellow students would be most likely to start social activities? | 5 |
| conflict.solve | Welche Deiner Mitstudierenden wären am ehesten fähig, Konflikte zwischen Studierenden zu lösen? | Which of your fellow students would be most likely to resolve conflicts between students? | 5 |
| organise | Welche Deiner Mitstudierenden wären am ehesten fähig, ein „Ersti-Weekend“ zu organisieren? | Which of your fellow students would be the most capable of organizing a welcome weekend? | 5 |
| prestudy | Mit wem warst du bei dem Student Introduction Day in einer Gruppe? Versuche Dich an so viele Namen wie möglich zu erinnern. | With whom were you in a group at the Student Introduction Day. Please remember as many names as possible. | 20 |
| prestudy.pleasant | Mit wem hattest du angenehme Interaktionen am Student Introduction Day? | With whom did you have pleasant interactions at the Student Introduction Day? | 20 |
| group | Welche Deiner Mitstudierenden sind Teil der Gruppe [1-5]? (die Anzahl Nicht-Mitstudierenden Gruppenmitglieder kann auf der nächsten Seite genannt werden) | Which of your fellow students are part of the group [1-5]? (The number of non-student group members can be found on the next page) | 20 |
| other.group | Kennst Du jemanden Deiner Mitstudierenden, der oder die zu dieser Gruppe [1-5] gehört? Bitte nenne so viele Personen, wie Du kannst, aber nenne nur Personen welche zur Gruppe gehören. | Do you know any of your fellow students who belong to this group [1-5]? Please name as many people as you can, but name only people who belong to the group. | 20 |
| wkend.pleasant | Mit wem hast Du am Welcome-Wochenende angenehme Interaktionen erlebt? | With whom did you experience pleasant interactions on the welcome weekend? | 20 |
| wkend.conflict | Mit wem hattest Du am Welcome-Wochenende persönliche Konflikte? | With whom did you have personal conflicts on the welcome weekend? | 20 |
| political.discussion | Mit wem Deiner Mitstudierenden tauscht Du Dich über politische Themen aus? | With whom of your fellow students do you have exchanges on political topics? | 20 |
| contact | Mit welchen Deiner Mitstudierenden warst Du in Kontakt während der [Wintersemesterferien / Sommersemesterferien]? | With which of your fellow students were you in contact during the ([winter break / summer break)? | 20 |
| complain | Wer beklagt sich oft? | Who often complains? | 5 |
| sad | Wer ist oft traurig? | Who is often sad? | 5 |
| dislike.listening.to | Wem hörst du nicht gerne zu? | Who do you not like to listen to? | 5 |
| social.media | Von wem siehst Du regelmässig Beiträge auf einer Social Media Plattform (Facebook, Instagram, etc.)? | Who do you regularly see posts from on a social media platform (Facebook, Instagram, etc.)? | 20 |
| sm.comm | Mit welchen Deiner Mitstudierenden kommunizierst Du regelmässig über Soziale Medien? (ausgenommen Messaging-Apps). | Which of your fellow students do you regularly communicate with via social media? (except messaging apps). | 20 |
| msg.comm | Mit welchen Deiner Mitstudierenden kommunizierst Du | Which of your fellow students do you communicate with regularly via messaging apps (e.g. Whatsapp, Signal, SMS)? | 20 |
| regelmässig über Messaging-Apps (z.B. Whatsapp, Signal, SMS)? | |||
| sm.good | Wer postet auf Sozialen Medien Inhalte, die Du besonders gut findest? | Who posts content on social media that you particularly like? | 5 |
| sm.bad | Wer postet auf Sozialen Medien Inhalte, die Du besonders schlecht findest? | Who posts content on social media that you find particularly bad? | 5 |
(taken from Vörös et al., (2021), Supplementary Information)
Network boundaries and participation rates
During the course of the study, the composition of cohorts was not constant but varied depending on the students’ academic paths. For example, some students left university, repeated a year or a semester, or took a semester off and returned later to their studies. The list of students invited to the surveys was updated at several time points during the data collection to avoid inviting individuals who did not belong to the cohorts of interest. Because these updates could only be made occasionally and were based on incomplete information, we also retrospectively devised a procedure to identify the students who belonged to the cohort each time we sent a questionnaire.
Procedure for inviting students
The list of students invited to the surveys was updated based on information provided by the academic services of the university. This information did not provide a clear definition of different year cohorts, due to the flexibility of the programs, but partial information on individuals’ student status. Updates made were limited to the following:
the removal of students after they asked to be taken out of our study,
the removal of students officially dropping out of the program,
the removal of students repeating in the following year cohort after failing the main mandatory exam at the end of the first year,
the addition of repeaters of the previous year cohort after failing the main mandatory exam in the first year,
the removal of students from one cohort who we judged to be more likely in the other, despite the possibility that they could be in either one.
The last step of the procedure included a fair part of guessing but was necessary to avoid sending students two questionnaires at the same time.
Cohort composition definition
At the end of the study, complete information on course and examination registrations of the students was collected (datafiles: “student_general_information_cohortX”, “student_information_wave_cohortX”, “course_information_cohortX”, “registration_information_wave_cohortX”, and “exam_information_wave_cohortX”). This information was used to create meta variables for each cohort that researchers can use to define the dynamic composition of the cohort they want to investigate (see sections Meta for Cohort I and Meta for Cohorts II and III). Examples of the use of these variables are:
selecting students who follow the intended curriculum by registering and passing mandatory exams
selecting students who started studying together
Close inspection of these meta variables indicate that student paths are varied and the construction of an accurate boundary for the cohort is a difficult task. Nevertheless, a baseline boundary was devised such that a student is considered part of the cohort at a certain wave if she or he has at least one co-registration with another student of the program. The boundary we chose (having at least one co-registration with others) is a relatively loose definition, that includes students with varying levels of participation in the program. Furthermore, students followed less and less the same courses in the course of the whole program, so they are much more dispersed in 3rd year than in the 1st. These fluctuations can help in understanding (a) why some participation rates are low (in the 2nd and 3rd year, we might have included students that are not really “active”in the cohort), and (b) suggest that the data from the 1st year might be more appropriate for some research.
Participation rates
Here we report two participation rates computed as the ratios of the number of answered questionnaires versus the number of students invited and the number of students part of the cohort according to our baseline definition. A detailed discussion on the challenges of defining the participation rates (due to the boundary definitions) are reported in Vörös et al. (2021). For Cohort I, the participation rates are the following:
Similarly, for cohort II, participation rates are:
For Cohort III, the participation rates are:
For all cohorts, variables indicating who was invited and who belongs to the baseline cohort are available in the first Table of sections Meta for Cohort I and Meta for Cohort II and III.
Cohort I
Cohort I (N = 226) consisted of students starting their studies in September 2016 in a science / engineering major at a Swiss university.
Waves
Variable Overview
Long Questionnaires
Intermediate Questionnaires
Short Questionnaires
Summer Short Questionnaires
Meta
For cohort I, specific information related to students’ academic status at each wave is contained in data.frames with the following variables:
The following objects are available to describe the academic paths of students and the cohort boundaries:
Information related to courses and exams offered for this cohort can be found in the following table:
General information on students contains the following:
Cohort II + III
Cohort II (N = 244) and III (N = 652) consisted of students starting their studies in September 2017 in a science / engineering major at a Swiss university.
Waves
Variable overview
Long Questionnaires
Meta
Specific information related to students’ academic status at each wave in cohort II is contained in data.frames with the following variables:
And similarly for cohort III:
For both cohorts II and III, the following objects are available to describe the academic paths of students and the cohort boundaries:
Information related to courses and exams offered for cohort II can be found in the following table:
And similarly for cohort III:
General information on students of both cohorts contain the following:
References
Boda, Zsófia, Timon Elmer, András Vörös, and Christoph Stadtfeld. 2020. “Short-term and long-term effects of a social network intervention on friendships among university students.” Scientific Reports 10 (1): 1–12. https://doi.org/10.1038/s41598-020-59594-z.
Elmer, Timon, Krishna Chaitanya, Prateek Purwar, and Christoph Stadtfeld. 2019. “The validity of RFID badges measuring face-to-face interactions.” Behavior Research Methods, 1–19. https://doi.org/10.3758/s13428-018-1180-y.
Elmer, Timon, and Christoph Stadtfeld. 2020. “Depressive symptoms are associated with social isolation in face-to-face interaction networks.” Scientific Reports, 1–12. https://doi.org/10.1038/s41598-020-58297-9.
Stadtfeld, Christoph, András Vörös, Timon Elmer, Zsófia Boda, and Isabel J Raabe. 2019. “Integration in emerging social networks explains academic failure and success.” Proceedings of the National Academy of Sciences of the United States of America 116 (3): 792–97. https://doi.org/10.1073/pnas.1811388115.
Vörös, András, Zsófia Boda, Timon Elmer, Kieran Mepham, Marion Hoffman, Isabel J Raabe, and Christoph Stadtfeld. 2021. “The Swiss StudentLife Study: Investigating the emergence of an undergraduate community through dynamic, multidimensional social network data.” Social Networks, no. 65: 71–84.
Version 1.0 |