Augmenting survey data with digital trace data: Is there a threat to panel retention?

被引:3
|
作者
Trappmann, Mark [1 ,2 ]
Haas, Georg-Christoph [3 ]
Malich, Sonja [3 ]
Keusch, Florian [4 ]
Bahr, Sebastian [3 ]
Kreuter, Frauke [5 ,6 ]
Schwarz, Stefan [3 ]
机构
[1] Inst Employment Res, Res Unit Panel Study Labour Market & Social Secur, Nurnberg, Germany
[2] Univ Bamberg, Sociol, Especially Survey Methodol, Bamberg, Germany
[3] Inst Employment Res, Nurnberg, Germany
[4] Univ Mannheim, Social Data Sci & Methodol, Mannheim, Germany
[5] Univ Maryland, Joint Program Survey Methodol, College Pk, MD 20742 USA
[6] Univ Maryland, Social Data Sci Ctr, College Pk, MD 20742 USA
关键词
Attrition; Digital trace data; Panel survey; Privacy; Respondent burden; PARTICIPATION; ATTRITION; COVERAGE;
D O I
10.1093/jssam/smac023
中图分类号
O1 [数学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 0701 ; 070101 ;
摘要
Linking digital trace data to existing panel survey data may increase the overall analysis potential of the data. However, producing linked products often requires additional engagement from survey participants through consent or participation in additional tasks. Panel operators may worry that such additional requests may backfire and lead to lower panel retention, reducing the analysis potential of the data. To examine these concerns, we conducted an experiment in the German PASS panel survey after wave 11. Three quarters of panelists (n = 4,293) were invited to install a research app and to provide sensor data over a period of 6 months, while one quarter (n = 1,428) did not receive an invitation. We find that the request to install a smartphone app and share data significantly decreases panel retention in the wave immediately following the invitation by 3.3 percentage points. However, this effect wears off and is no longer significant in the second and third waves after the invitation. We conclude that researchers who run panel surveys have to take moderate negative effects on retention into account but that the potential gain likely outweighs these moderate losses.
引用
收藏
页码:541 / 552
页数:12
相关论文
共 50 条
  • [21] Using Panel Data for Macroeconomic Policy Evaluation: A Survey
    Smith, Ron P.
    EGE ACADEMIC REVIEW, 2019, 19 (04) : 389 - 399
  • [22] Predicting Voting Behavior Using Digital Trace Data
    Bach, Ruben L.
    Kern, Christoph
    Amaya, Ashley
    Keusch, Florian
    Kreuter, Frauke
    Hecht, Jan
    Heinemann, Jonathan
    SOCIAL SCIENCE COMPUTER REVIEW, 2021, 39 (05) : 862 - 883
  • [23] Using Digital Trace Data to Identify Regions and Cities
    Brelsford, Christa
    Arthur, Rudy
    Thakur, Gautam
    Williams, Hywel
    ARIC 2019: PROCEEDINGS OF THE 2ND ACM SIGSPATIAL INTERNATIONAL WORKSHOP ON ADVANCES IN RESILIENT AND INTELLIGENT CITIES (ARIC-2019), 2019, : 5 - 8
  • [24] Reflections on quality requirements for digital trace data in IS research
    Vial, Gregory
    DECISION SUPPORT SYSTEMS, 2019, 126
  • [25] COLLECTING DIGITAL DATA ON HM SURVEY SHIPS
    HUDDY, WAA
    JOURNAL OF NAVIGATION, 1978, 31 (03): : 465 - 469
  • [26] Visually Augmenting Documents With Data
    Latif, Shahid
    Beck, Fabian
    COMPUTING IN SCIENCE & ENGINEERING, 2018, 20 (06) : 96 - 103
  • [27] Augmenting Surveys with Paradata, Administrative Data, and Contextual Data
    Sakshaug, Joseph W.
    Struminskaya, Bella
    PUBLIC OPINION QUARTERLY, 2023, 87 : 475 - 479
  • [28] Data on Digital Transformation in the German Socio-Economic Panel
    Fedorets, Alexandra
    Kirchner, Stefan
    Adriaans, Jule
    Giering, Oliver
    JAHRBUCHER FUR NATIONALOKONOMIE UND STATISTIK, 2022, 242 (5-6): : 691 - 705
  • [29] LAND LINE SURVEY DATA WITHIN A DIGITAL CARTOGRAPHIC DATA-BASE
    EDSON, DT
    LEE, GYG
    PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 1977, 43 (06): : 755 - 756
  • [30] Algorithmic portfolio choice: lessons from panel survey data
    Scherer B.
    Financial Markets and Portfolio Management, 2017, 31 (1) : 49 - 67