Reference health
Do (Not!) Track Me: Relationship Between Willingness to Participate and Sample Composition in Online Information Behavior Tracking Research
33/33 checkable references clean · checked 2026-08-08
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
8 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 33 checked references that resolve
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How Much Time Do You Spend Online? Understanding and Improving the Accuracy of Self-Reported Measures of Internet Use
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Dealing with digital traces: Understanding protective behaviors on mobile devices
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Dealing with self-report bias in mobile Internet acceptance and usage studies
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Measuring Media Exposure in a Changing Communications Environment
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Internet social network communities: Risk taking, trust, and privacy concerns
resolves10.1038/s41598-021-82516-6
The ideological divide in confidence in science and participation in medical research
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(Almost) Everything in Moderation: New Evidence on Americans' Online Media Diets
resolves10.1177/20563051211033820
Do News Actually “Find Me”? Using Digital Behavioral Data to Study the News-Finds-Me Phenomenon
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The Influence of a Person’s Digital Affinity on Unit Nonresponse and Attrition in an Online Panel
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Two Half-Truths Make a Whole? On Bias in Self-Reports and Tracking Data
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Collecting Survey and Smartphone Sensor Data With an App: Opportunities and Challenges Around Privacy and Informed Consent
resolves10.1007/s11135-011-9640-9
Determinants of social desirability bias in sensitive surveys: a literature review
resolves10.1108/INTR-01-2021-0074
To track or not to track: examining perceptions of online tracking for information behavior research
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Collecting vertical trace data: Big possibilities and big challenges for multi-method research
resolves10.1177/0894439319828012
Explaining Online News Engagement Based on Browsing Behavior: Creatures of Habit?
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Willingness of Online Respondents to Participate in Alternative Modes of Data Collection
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A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use
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The Immensely Inflated News Audience: Assessing Bias in Self-Reported News Exposure
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Using Passive Data From a Meter to Complement Survey Data in Order to Study Online Behavior
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Turnout Bias in Postelection Surveys: Political Involvement, Survey Participation, and Vote Overreporting
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Populist Attitudes and Selective Exposure to Online News: A Cross-Country Analysis Combining Web Tracking and Surveys
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A question of trust: user-centered design requirements for an informatics intervention to promote the sexual health of African-American youth
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Avenues to News and Diverse News Exposure Online: Comparing Direct Navigation, Social Media, News Aggregators, Search Queries, and Article Hyperlinks
The 8 references without a DOI — listed, not checked
no DOI — not checkedBrehm J. O. (1993). The phantom respondents: Opinion surveys and political representation. University of Michigan Press.
no DOI — not checkedGalesic M. (2006). Dropouts on the web: Effects of interest and burden experienced during an online survey. Journal of Official Statistics, 22(2), 313–328.
no DOI — not checkedGuess A. M., Nyhan B., Reifler J. (2018). Selective exposure to misinformation: Evidence from the consumption of fake news during the 2016 US presidential campaign. European Research Council, 9(3), 4.
no DOI — not checkedLipps O. (2007). Attrition in the Swiss household panel. Methoden, Daten, Analysen (mda), 1(1), 45–68.
no DOI — not checkedRevilla M., Couper M. P., Ochoa C. (2019). Willingness of online panelists to perform additional tasks. Methods, Data Analyses, 13(2), 223–252. https://doi.org/10.12758/mda.2018.01
no DOI — not checkedStier S., Breuer J., Siegers P., Thorson K. (2020a). Integrating survey data and digital trace data: Key issues in developing an emerging field. SAGE Publications.
no DOI — not checkedWenz A., Jäckle A., Couper M. P. (2019). Willingness to use mobile technologies for data collection in a probability household panel. Survey Research Methods, 13(1), 1–22. https://doi.org/10.18148/srm/2019.v1i1.7298
no DOI — not checkedWieland M., Der Au A., Keller C., Brunk S., Bettermann T., Hagen L., Schlegel T. (2018). Online behavior tracking in social sciences: Quality criteria and technical implementation. In Stützer C., Welker M., Egger M. (Eds.), Computational social science in the age of big data: Concepts, methodologies, tools, and applications (pp. 131–160). Herbert von Halem.
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