SOEP-IS 2023 - DIPS3_HOURLY: Smartphone sensing on the hourly level (DIPS project)

Krämer, Michael et al. Research Report SOEP-IS 2023 - DIPS3_HOURLY: Smartphone sensing on the hourly level (DIPS project) SOEP Survey Papers, No. 1431 Provided in Cooperation with: German Institute for Economic Research (DIW Berlin) Suggested Citation: Krämer, Michael et al. (2025) : SOEP-IS 2023 - DIPS3_HOURLY: Smartphone sensing on the hourly level (DIPS project), SOEP Survey Papers, No. 1431, Deutsches Institut für Wirtschaftsforschung (DIW), German Socio-Economic Panel (SOEP), Berlin This Version is available at: https://hdl.handle.net/10419/312143 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-sa/4.0/ 1431 2025 Series D – Variable Descriptions and Coding SOEP-IS 2023 – DIPS3_HOURLY: Smartphone Sensing on the Hourly Level (DIPS Project) Michael Krämer, Vanessa Brandes, Martin Gerike, Yannick Roos, Ramona Schoedel, Cornelia Wrzus, and David Richter Running since 1984, the German Socio-Economic Panel (SOEP) is a wide-ranging representative longitudinal study of private households, located at the German Institute for Economic Research, DIW Berlin. The aim of the SOEP Survey Papers Series is to thoroughly document the survey’s data collection and data processing. The SOEP Survey Papers is comprised of the following series: Series A – Survey Instruments (Erhebungsinstrumente) Series B – Survey Reports (Methodenberichte) Series C – Data Documentation (Datendokumentationen) Series D – Variable Descriptions and Coding Series E – SOEPmonitors Series F – SOEP Newsletters Series G – General Issues and Teaching Materials The SOEP Survey Papers are available at http://www.diw.de/soepsurveypapers Editors: Dr. Jan Goebel, DIW Berlin Dr. Christian Hunkler, DIW Berlin Prof. Dr. Philipp Lersch, DIW Berlin and Humboldt-Universität zu Berlin Dr. Levent Neyse, DIW Berlin and Berlin Social Science Center (WZB) Prof. Dr. Carsten Schröder, DIW Berlin and Freie Universität Berlin Prof. Dr. Sabine Zinn, DIW Berlin and Humboldt-Universität zu Berlin Please cite this paper as follows: Michael Krämer, Vanessa Brandes, Martin Gerike, Yannick Roos, Ramona Schoedel, Cornelia Wrzus, and David Richter, 2025. SOEP-IS 2023 – DIPS3_HOURLY: Smartphone Sensing on the Hourly Level (DIPS Project). SOEP Survey Papers 1431: Series D – Variable Descriptions and Coding. Berlin: DIW Berlin/SOEP This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. © 2025 by SOEP ISSN: 2193-5580 (online) DIW Berlin German Socio-Economic Panel (SOEP) Mohrenstr. 58 10117 Berlin Germany [email protected] The German Socio Economic Panel at DIW Berlin SOEP-IS 2023 – DIPS3_HOURLY: Smartphone Sensing on the Hourly Level (DIPS Project) Michael Krämer, Vanessa Brandes, Martin Gerike, Yannick Roos, Ramona Schoedel, Cornelia Wrzus, and David Richter 2025 The file dips3_hourly is part of a collection, which is released with doi:10.5684/soep.is.2023. SOEP Innovation Sample (2023) dips3_hourly Contents 1 Introduction 4 Overall Project Description ........................... 4 DIPS3 Data Collection .............................. 4 Hourly-Level Data ................................ 4 References .................................... 5 2 Identifiers 5 pid – Person ID ..................................... 5 subsample – Subsample ................................ 6 syear – Survey Year ................................... 6 n_days – Study Day Count ............................... 6 n_hour – Hour of the day ............................... 7 day – Day of the Week ................................. 7 weekend – Weekend .................................. 7 3 Texts 7 texts_outgoing_freq – Number of Texts Sent ..................... 8 texts_outgoing_length – Character Count of Texts Sent ............... 8 4 App Usage 8 comm_app_freq – Communication Apps: Frequency ................ 9 comm_app_time – Communication Apps: Duration ................ 9 socmed_app_freq – Social Media Apps: Frequency ................. 9 socmed_app_time – Social Media Apps: Duration .................. 10 audio_app_freq – Audio Entertainment Apps: Frequency .............. 10 audio_app_time – Audio Entertainment Apps: Duration .............. 11 career_app_freq – Career Apps: Frequency ...................... 11 career_app_time – Career Apps: Duration ...................... 11 create_app_freq – Creativity Apps: Frequency .................... 12 create_app_time – Creativity Apps: Duration ..................... 12 dating_app_freq – Dating Apps: Frequency ..................... 12 dating_app_time – Dating Apps: Duration ...................... 13 finance_app_freq – Finance Apps: Frequency .................... 13 finance_app_time – Finance Apps: Duration ..................... 14 food_app_freq – Food Apps: Frequency ........................ 14 food_app_time – Food Apps: Duration ........................ 14 game_app_freq – Game Apps: Frequency ...................... 15 game_app_time – Game Apps: Duration ....................... 15 health_app_freq – Health Apps: Frequency ...................... 16 health_app_time – Health Apps: Duration ...................... 16 internet_app_freq – Internet Apps: Frequency .................... 16 internet_app_time – Internet Apps: Duration .................... 17 know_app_freq – Knowledge Apps: Frequency .................... 17 know_app_time – Knowledge Apps: Duration .................... 18 news_app_freq – News Apps: Frequency ....................... 18 news_app_time – News Apps: Duration ....................... 18 orientat_app_freq – Orientation Apps: Frequency .................. 19 orientat_app_time – Orientation Apps: Duration .................. 19 photo_app_freq – Photography Apps: Frequency .................. 20 SOEP Survey Papers 1431 2 SOEP Innovation Sample (2023) dips3_hourly photo_app_time – Photography Apps: Duration ................... 20 read_app_freq – Reading Apps: Frequency ...................... 20 read_app_time – Reading Apps: Duration ...................... 21 security_app_freq – Security Apps: Frequency .................... 21 security_app_time – Security Apps: Duration .................... 21 settings_app_freq – Settings Apps: Frequency .................... 22 settings_app_time – Settings Apps: Duration .................... 22 shop_app_freq – Shopping Apps: Frequency ..................... 23 shop_app_time – Shopping Apps: Duration ..................... 23 spirit_app_freq – Spirituality Apps: Frequency .................... 23 spirit_app_time – Spirituality Apps: Duration .................... 24 time_app_freq – Time Apps: Frequency ....................... 24 time_app_time – Time Apps: Duration ........................ 24 tools_app_freq – Tools Apps: Frequency ....................... 25 tools_app_time – Tools Apps: Duration ........................ 25 transport_app_freq – Transport Apps: Frequency .................. 26 transport_app_time – Transport Apps: Duration ................... 26 visual_app_freq – Visual Entertainment Apps: Frequency .............. 26 visual_app_time – Visual Entertainment Apps: Duration .............. 27 weather_app_freq – Weather Apps: Frequency .................... 27 weather_app_time – Weather Apps: Duration .................... 27 5 Conversation Detection 28 n_aware – AWARE Conversations: Samplings .................... 28 n_voice – AWARE Conversations: Conv. Detected .................. 29 prop_voice – Proportion of Conversations ...................... 29 SOEP Survey Papers 1431 3 SOEP Innovation Sample (2023) dips3_hourly 1 Introduction Overall Project Description The DIPS3 study was part of a larger DFG-funded project on “Personality and social relationship dynamics: Shortand medium-term processes in daily life” with Cornelia Wrzus and David Richter as principal investigators. This project had the overall goal to investigate the dynamic, interdependent short-term and medium-term processes that define multiple social relationships and to better understand how these processes differ between people depending on diverse personality characteristics. To this aim, several modes of data collection were employed jointly, such as active daily diary assessments and passive smartphone sensing of behavioral indicators related to social contact (for measurement properties of these social contact indicators, see Roos et al., 2023). The project was given Institutional Review Board approval by Johannes Gutenberg University Mainz (Process Number: 2018JGU-psychEK002). DIPS3 Data Collection The third part of data collection within this project was integrated into the SOEP-IS in wave 2022 with the goal of recruiting members of this already existing panel study to take part in an additional, opt-in data collection with a smartphone app. As reported in Roos et al. (2024), 2,507 participants took part in the SOEP-IS study in 2022 of which 1,322 (53%) reported initial interest in the smartphone study and 844 (34%) fulfilled all requirements (i.e., regularly using a smartphone running on Android Version 6.1 or higher). Finally, roughly 15% of the 2022 SOEP-IS sample, that is, N = 386 participants, took part in the 14-day smartphone study and answered at least one daily diary. A detailed examination of sample selectiviy and different person-related sampling biases associated with selection into mobile sensing studies (including the DIPS3 study) will be available in Schoedel et al. (2024). At the end of the 2022 interview, SOEP-IS respondents were asked if they owned a smartphone running on Android OS Version 6.1 or higher and if they were interested in participating in an additional 14-day smartphone study. Those who agreed to participate were sent a postal invitation to take part in the study along with instructions on how to install and set up the PhoneStudy app which runs on Android OS (for more information on the app, see https://phonestudy.org/en/). Respondents were informed during the onboarding process about the study procedure and data protection. Informed consent was obtained during the setup of the app. After the installation of the app, respondents received daily notifications to fill out a brief questionnaire on their mood and social interactions each evening for 14 days. Questionnaires were available each day from 8:00 p.m. to 4:00 a.m. of the following day. Respondents were instructed to answer the questionnaire right before going to bed and received up to two reminders between 8:00 p.m. and 12:00 a.m. Additionally, smartphone sensing ran on the respondents’ phones, passively gathering data on anonymized social interactions, phone and app usage, and contact entries. Respondents received 40€ for participation. Additional study documentation materials including the wording of all items in English and German, the recruitment flyer, and a report on different app versions can be found on https://osf.io/zhc49/. Here, we only present the translated, English version of the items. Hourly-Level Data Raw data of smartphone sensing cannot be shared publicly due to privacy concerns and potential identification of respondents due to the richness of these data, especially when SOEP Survey Papers 1431 4 SOEP Innovation Sample (2023) dips3_hourly linked with the SOEP-IS panel data. Therefore, for this data release, we selected only the most meaningful behavioral indicators and aggregated them over time. The second of the three DIPS3 datasets presents the smartphone sensing data on the hourly level with each individual observation defined as an hourly interval, that is, from one full hour to the next, over the entire study period. The very first interval of each respondent starts with the hour of installing the PhoneStudy app and the last interval ends with the hour of deinstallation of the study app. The purpose of this aggregation time window was to provide a more fine-grained temporal resolution of the smartphone sensing indicators. Thereby, temporal dynamics in these continuously assessed indicators can be examined. Data can be linked to the daily-level data via the “pid” and “n_days” identifers. For the other two datasets, see: Aggregated on the daily level: https://www.diw.de/documents/publikationen/73/diw_01.c.936884.de/diw_ssp1430.pdf Short surveys after calls: https://www.diw.de/documents/publikationen/73/diw_01.c.936889.de/diw_ssp1432.pdf References Roos, Y., Krämer, M. D., Richter, D., Schoedel, R., & Wrzus, C. (2023). Does Your Smartphone “Know” Your Social Life? A Methodological Comparison of Day Reconstruction, Experience Sampling, and Mobile Sensing. Advances in Methods and Practices in Psychological Science, 6(3), 1–12. https://doi.org/10.1177/25152459231178738 Roos, Y., Krämer, M. D., Richter, D., & Wrzus, C. (2024). Persons in contexts: The role of social networks and social density for the dynamic regulation of face-to-face interactions in daily life. Journal of Personality and Social Psychology. Advance online publication. https://doi.org/10.1037/pspp0000512 Schoedel, R., Reiter, T., Krämer, M. D., Roos, Y., Bühner, M., Richter, D., Mehl, M. R., & Wrzus, C. (2024). Person-Related Selection Bias in Mobile Sensing Research: Robust Findings from Two Panel Studies [Manuscript submitted for publication]. 2 Identifiers Data was processed by adding general identifiers to track study progress over time and provide easier filtering. Temporal observations are defined as hourly intervals, that is, from one full hour to the next, over the entire study period. The very first temporal observation of each participant starts with the hour of installing the PhoneStudy App and the last temporal observation ends with the hour of deinstallation of the Study App. pid – Person ID 1233703 349 1247702 356 1344403 342 2001401 349 2012702 346 2037404 344 2043004 355 SOEP Survey Papers 1431 5 SOEP Innovation Sample (2023) dips3_hourly ... (372 rows omitted) 129825 41824401 348 41824901 356 41827601 348 41830701 348 41830702 357 41831501 345 41833601 353 Same person identifier as in SOEP-IS data files. subsample – Subsample 0[0] no 109559 1[1] yes 25162 During the study period, an older, outdated app version was distributed for a restricted time window due to a technical error (N = 72 with outdated version; N = 313 with correct version). This older app version differed slightly in some wordings of the daily diary items. Here, we present documentation for the correct app version that the majority of respondents installed. For more details on this matter and an extensive comparison of the two app versions, see OSF repository. https://osf.io/zhc49/ syear – Survey Year 2022 134721 n_days – Study Day Count 0 4704 1 8973 2 9233 3 9207 4 9208 5 9212 6 9189 ... (16 rows omitted) 74614 -7 102 -8 72 -9 57 -10 48 -11 48 -12 42 -13 12 SOEP Survey Papers 1431 6 SOEP Innovation Sample (2023) dips3_hourly 0 133815 1 165 2 127 3 97 4 41 5 17 6 22 ... (13 rows omitted) 84 25 3 29 1 31 1 33 1 37 2 38 1 -1 344 dating_app_time – Dating Apps: Duration 0 133815 0.0000166654586791992 5 0.0000333309173583984 3 0.0000333348910013835 3 0.000150001049041748 1 0.000199997425079346 1 0.0012500007947286 1 ... (541 rows omitted) 542 21.6357666691144 1 24.8163500030835 1 27.6878833293915 1 32.3519833405813 1 38.5241999983788 1 48.1976833303769 1 -1 344 finance_app_freq – Finance Apps: Frequency 0 130660 1 1733 2 884 3 485 4 261 5 116 6 70 ... (13 rows omitted) 161 20 1 21 1 25 2 SOEP Survey Papers 1431 13 SOEP Innovation Sample (2023) dips3_hourly 30 1 31 1 33 1 -1 344 finance_app_time – Finance Apps: Duration 0 130660 0.0000166654586791992 14 0.0000166694323221842 7 0.0000333309173583984 14 0.0000333348910013835 19 0.0000333388646443685 2 0.0000499963760375977 4 ... (3589 rows omitted) 3651 17.0163499951363 1 17.2330666700999 1 19.0231000026067 1 19.1574000000954 1 28.5066166639328 1 42.1579499959946 1 -1 344 food_app_freq – Food Apps: Frequency 0 133229 1 425 2 342 3 165 4 76 5 44 6 39 ... (4 rows omitted) 45 11 3 12 1 13 3 16 3 17 1 20 1 -1 344 food_app_time – Food Apps: Duration 0 133229 0.0000166654586791992 2 SOEP Survey Papers 1431 14 SOEP Innovation Sample (2023) dips3_hourly 0.0000166694323221842 3 0.0000333309173583984 2 0.0000333348910013835 4 0.0000499963760375977 2 0.0000500003496805827 3 ... (1119 rows omitted) 1126 15.0917000015577 1 15.2903333306313 1 15.6102000037829 1 15.6971499959628 1 16.8666833321253 1 17.8180499990781 1 -1 344 game_app_freq – Game Apps: Frequency 0 129515 1 1942 2 1258 3 631 4 409 5 204 6 129 ... (22 rows omitted) 281 35 1 43 2 46 2 61 1 62 1 66 1 -1 344 game_app_time – Game Apps: Duration 0 129515 0.0000166654586791992 33 0.0000166694323221842 13 0.0000333309173583984 15 0.0000333348910013835 11 0.0000333388646443685 2 0.0000499963760375977 6 ... (4467 rows omitted) 4776 43.5466500004133 1 44.0366666595141 1 44.7578166723251 1 46.1949166695277 1 50.4710500001907 1 SOEP Survey Papers 1431 15 SOEP Innovation Sample (2023) dips3_hourly 51.3529666701953 1 -1 344 health_app_freq – Health Apps: Frequency 0 130678 1 2231 2 836 3 309 4 141 5 80 6 27 ... (4 rows omitted) 56 11 10 12 3 13 2 14 1 18 2 45 1 -1 344 health_app_time – Health Apps: Duration 0 130678 0.0000166654586791992 12 0.0000166694323221842 8 0.0000333309173583984 10 0.0000333348910013835 11 0.0000499963760375977 8 0.0000500003496805827 4 ... (3511 rows omitted) 3640 28.9384166638056 1 31.0644666671753 1 34.7492166678111 1 36.2890166680018 1 40.5395166635513 1 42.7018000046412 1 -1 344 internet_app_freq – Internet Apps: Frequency 0 113844 1 7844 2 5585 3 2778 SOEP Survey Papers 1431 16 SOEP Innovation Sample (2023) dips3_hourly 4 1601 5 988 6 611 ... (19 rows omitted) 1119 27 2 28 1 30 1 31 1 36 1 46 1 -1 344 internet_app_time – Internet Apps: Duration 0 113844 0.0000166654586791992 30 0.0000166694323221842 9 0.0000333309173583984 11 0.0000333348910013835 17 0.0000499963760375977 5 0.0000500003496805827 9 ... (17664 rows omitted) 20446 54.3548000017802 1 55.7967333316803 1 56.1459333340327 1 57.2808666706085 1 57.9188833355904 1 59.4885666648547 1 -1 344 know_app_freq – Knowledge Apps: Frequency 0 133462 1 416 2 240 3 113 4 53 5 32 6 27 ... (4 rows omitted) 24 11 1 12 3 14 3 15 1 17 1 26 1 -1 344 SOEP Survey Papers 1431 17 SOEP Innovation Sample (2023) dips3_hourly know_app_time – Knowledge Apps: Duration 0 133462 0.0000166654586791992 5 0.0000166694323221842 1 0.0000333309173583984 5 0.0000333348910013835 2 0.0000500043233235677 1 0.0000666618347167969 1 ... (886 rows omitted) 894 25.7483166694641 1 29.2025000135104 1 30.6985000014305 1 30.8660166660945 1 39.2304500023524 1 44.4092666665713 1 -1 344 news_app_freq – News Apps: Frequency 0 129477 1 2317 2 1287 3 644 4 319 5 146 6 91 ... (5 rows omitted) 85 12 3 13 3 14 1 15 1 16 2 18 1 -1 344 news_app_time – News Apps: Duration 0 129477 0.0000166654586791992 70 0.0000166694323221842 27 0.0000333309173583984 42 0.0000333348910013835 63 0.0000333388646443685 7 SOEP Survey Papers 1431 18 SOEP Innovation Sample (2023) dips3_hourly 0.0000499963760375977 22 ... (4475 rows omitted) 4663 34.189483332634 1 34.4585333267848 1 36.5673500021299 1 39.7877000013987 1 40.0205500006676 1 41.3940500020981 1 -1 344 orientat_app_freq – Orientation Apps: Frequency 0 131221 1 1432 2 947 3 387 4 174 5 74 6 39 ... (11 rows omitted) 96 19 1 21 1 22 1 23 2 24 1 26 1 -1 344 orientat_app_time – Orientation Apps: Duration 0 131221 0.0000166654586791992 18 0.0000166694323221842 10 0.0000333309173583984 8 0.0000333348910013835 4 0.0000333388646443685 1 0.0000499963760375977 4 ... (2953 rows omitted) 3105 45.7206000049909 1 46.5072833339373 1 47.1935833334923 1 52.331366666158 1 53.6437833309174 1 54.8757166663806 1 -1 344 SOEP Survey Papers 1431 19 SOEP Innovation Sample (2023) dips3_hourly photo_app_freq – Photography Apps: Frequency 0 125172 1 3652 2 2241 3 1252 4 671 5 423 6 253 ... (29 rows omitted) 705 37 1 39 3 44 1 47 1 53 1 63 1 -1 344 photo_app_time – Photography Apps: Duration 0 125172 0.0000166654586791992 19 0.0000166694323221842 6 0.0000333309173583984 12 0.0000333348910013835 11 0.0000333388646443685 1 0.0000499963760375977 9 ... (8149 rows omitted) 9141 31.6125000079473 1 32.4375666618347 1 34.4092000007629 1 34.4886666695277 1 43.4587666670481 1 51.9833333333333 1 -1 344 read_app_freq – Reading Apps: Frequency 0 134168 1 82 2 63 3 31 4 14 5 10 6 5 7 2 8 1 SOEP Survey Papers 1431 20 SOEP Innovation Sample (2023) dips3_hourly 11 1 -1 344 read_app_time – Reading Apps: Duration 0 134168 0.0000166654586791992 1 0.0000333309173583984 1 0.000116666158040365 1 0.000133335590362549 1 0.000333333015441895 1 0.000516668955485026 1 ... (195 rows omitted) 197 8.39638332923253 1 8.84966666698456 1 10.0002500017484 1 10.8863833347956 1 20.7452166676521 1 31.5631500005722 1 -1 344 security_app_freq – Security Apps: Frequency 0 130878 1 2050 2 661 3 317 4 160 5 96 6 59 ... (10 rows omitted) 147 17 4 18 1 19 1 21 1 23 1 120 1 -1 344 security_app_time – Security Apps: Duration 0 130878 0.0000166654586791992 10 0.0000166694323221842 3 0.0000333309173583984 3 SOEP Survey Papers 1431 21 SOEP Innovation Sample (2023) dips3_hourly 0.0000333348910013835 4 0.0000499963760375977 1 0.0000500003496805827 1 ... (3107 rows omitted) 3469 44.7582833210627 1 45.6130833307902 1 48.047866666317 1 50.0134166677793 1 51.3345833341281 1 60 3 -1 344 settings_app_freq – Settings Apps: Frequency 0 129553 1 3072 2 907 3 426 4 159 5 82 6 41 ... (17 rows omitted) 131 26 1 27 1 28 1 31 1 34 1 162 1 -1 344 settings_app_time – Settings Apps: Duration 0 129553 0.0000166654586791992 2 0.0000333309173583984 2 0.0000333348910013835 4 0.0000499963760375977 1 0.0000500003496805827 3 0.0000500043233235677 3 ... (4526 rows omitted) 4802 38.7701999982198 1 40.2238333304723 1 40.7730666677157 1 41.949066666762 1 45.0309000015259 1 60 2 -1 344 SOEP Survey Papers 1431 22 SOEP Innovation Sample (2023) dips3_hourly Ferreira, D., & Mulukutla, R. (2020). AWARE Plugin: Conversations. Retrieved from https://github.com/denzilferreira/com.aware.plugin.studentlife.audio_final Roos, Y., Krämer, M. D., Richter, D., Schoedel, R., & Wrzus, C. (2023). Does Your Smartphone “Know” Your Social Life? A Methodological Comparison of Day Reconstruction, Experience Sampling, and Mobile Sensing. Advances in Methods and Practices in Psychological Science, 6(3), 1–12. https://doi.org/10.1177/25152459231178738 n_voice – AWARE Conversations: Conv. Detected 0 117094 1 4975 2 2371 3 1486 4 1134 5 903 6 734 ... (48 rows omitted) 5937 55 18 56 17 57 18 58 15 59 6 60 7 61 6 prop_voice – Proportion of Conversations 0 117094 0.0133333333333333 1 0.0136986301369863 1 0.0149253731343284 2 0.0161290322580645 1 0.0163934426229508 1 0.02 1 ... (905 rows omitted) 17581 0.979591836734694 1 0.982758620689655 1 0.983050847457627 1 0.983333333333333 3 0.983606557377049 5 0.983870967741935 1 1 27 This variable is computed by dividing n_voice by n_aware. SOEP Survey Papers 1431 29