1932

Abstract

The methodological tool chest available to those who study digital technologies ranges from those that are uniquely digital methods to approaches that are well established in the social sciences. This domain of work includes the application of methods to answer questions about the relationship between digital technologies and the social world, as well as the use of digital methods to answer questions about the offline world. New or old, quantitative or qualitative, the methods used to study the digital have strengths and weaknesses unique to this area of research. These issues include questions about the scope of cyberethnography, the validity of trace data, and the analytical division between on- and offline interaction. This review focuses on an overview of different methods, their history, and their strengths and weaknesses as applied to the study of digital technologies, including ethnographic approaches, interviews, surveys, time and media diaries, trace data, and online experiments.

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2017-07-31
2024-10-14
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Literature Cited

  1. Anderson B, Tracey K. 2001. Digital living: the impact (or otherwise) of the Internet on everyday life. Am. Behav. Sci. 45:456–75 [Google Scholar]
  2. Anderson E. 2000. Code of the Street: Decency, Violence and the Moral Life of the Inner City New York: W.W. Norton & Company [Google Scholar]
  3. Appel L, Dadlani P, Dwyer M, Hampton KN, Kitzie V. et al. 2014. Testing the validity of social capital measures in the study of information and communication technologies. Inf. Commun. Soc. 17:398–416 [Google Scholar]
  4. Atkinson P. 2015. For Ethnography Los Angeles, CA: Sage [Google Scholar]
  5. Baker R, Brick JM, Bates NA, Battaglia M, Couper MP. et al. 2013. Summary report of the AAPOR task force on non-probability sampling. J. Surv. Stat. Methodol. 1:90–143 [Google Scholar]
  6. Baym N, Zhang YB, Lin M-C. 2004. Social interactions across media: interpersonal communication on the Internet, telephone and face-to-face. New Media Soc 6:299–318 [Google Scholar]
  7. Beaulieu A. 2010. Research note: from co-location to co-presence: shifts in the use of ethnography for the study of knowledge. Soc. Stud. Sci. 40:453–70 [Google Scholar]
  8. Bekkering E, Shim JP. 2006. Trust in videoconferencing. Commun. ACM 49:103–7 [Google Scholar]
  9. Berinsky AJ, Huber GA, Lenz GS. 2012. Evaluating online labor markets for experimental research: Amazon.com's Mechanical Turk. Political Anal 20:351–68 [Google Scholar]
  10. Bernard HR, Killworth P, Kronenfeld D, Sailer L. 1984. The problem of informant accuracy: the validity of retrospective data. Annu. Rev. Anthropol. 13:495–517 [Google Scholar]
  11. Blank G, Groselj D. 2014. Dimensions of Internet use: amount, variety, and types. Inf. Commun. Soc. 17:417–35 [Google Scholar]
  12. Blondel VD, Decuyper A, Krings G. 2015. A survey of results on mobile phone datasets analysis. EPJ Data Sci 4:1–55 [Google Scholar]
  13. Boase J. 2016. Augmenting survey and experimental designs with digital trace data. Commun. Methods Measures 10:165–66 [Google Scholar]
  14. Boczkowski PJ. 2005. Digitizing the News: Innovation in Online Newspapers Cambridge, MA: MIT Press [Google Scholar]
  15. Boellstorff T. 2012. Ethnography and Virtual Worlds: A Handbook of Method Princeton, NJ: Princeton Univ. Press [Google Scholar]
  16. Bohannon J. 2016. Mechanical Turk upends social sciences. Science 352:1263–64 [Google Scholar]
  17. Bollen J, Mao H, Pepe A. 2011. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. ICWSM 11:450–53 [Google Scholar]
  18. Bond RM, Fariss CJ, Jones JJ, Kramer AD, Marlow C. et al. 2012. A 61-million-person experiment in social influence and political mobilization. Nature 489:295–98 [Google Scholar]
  19. boyd d, Crawford K. 2012. Critical questions for big data. Inf. Commun. Soc. 15:662–79 [Google Scholar]
  20. Brandtzæg PB. 2010. Towards a unified media-user typology (MUT): a meta-analysis and review of the research literature on media-user typologies. Comput. Hum. Behav. 26:940–56 [Google Scholar]
  21. Burawoy M. 1998. The extended case method. Sociol. Theory 16:4–33 [Google Scholar]
  22. Burke M, Marlow C, Lento T. 2010. Social network activity and social well-being. Proc. ACM CHI 2010 Conf. Hum. Factors Comput. Syst., Atlanta, Georgia1909–12 New York: ACM [Google Scholar]
  23. Bushman BJ, Anderson CA. 2015. Understanding causality in the effects of media violence. Am. Behav. Sci. 59:1807–21 [Google Scholar]
  24. Casler K, Bickel L, Hackett E. 2013. Separate but equal? A comparison of participants and data gathered via Amazon's MTurk, social media, and face-to-face behavioral testing. Comput. Hum. Behav. 29:2156–60 [Google Scholar]
  25. Chang L, Krosnick JA. 2003. Measuring the frequency of regular behaviors: comparing the “typical week” to the “past week”. Sociol. Methodol. 33:55–80 [Google Scholar]
  26. Clifford S, Jerit J. 2014. Is there a cost to convenience? An experimental comparison of data quality in laboratory and online studies. J. Exp. Political Sci. 1:120–31 [Google Scholar]
  27. Cobb C. 2003. Hanging out in the virtual pub: masculinities and relationships online (review). Leonardo 36:414–15 [Google Scholar]
  28. Correll S. 1995. The ethnography of an electronic bar: the lesbian cafe. J. Contemp. Ethnogr. 24:270–98 [Google Scholar]
  29. Csikszentmihalyi M, Larson R. 2014. Validity and reliability of the experience-sampling method. Flow and the Foundations of Positive Psychology: The Collected Works of Mihaly Csikszentmihalyi35–54 Dordrecht, Neth.: Springer [Google Scholar]
  30. Dibbell J. 1998. My Tiny Life: Crime and Passion in a Virtual World New York: Holt [Google Scholar]
  31. Dimond J, Fiesler C, DiSalvo B, Pelc J, Bruckman A. 2012. Qualitative data collection techniques: a comparison of instant messaging, email, and phone. Proc. 17th ACM Int. Conf. Support. Group Work, Sanibel Island, FL277–80 New York: ACM [Google Scholar]
  32. Drew R. 2005. Review: old habits and new media: ethnography meets cyberspace studies: Hanging Out in the Virtual Pub: Masculinities and RelationshipsOnline by Lori Kendall. Symb. Interact. 28:449–56 [Google Scholar]
  33. Du Bois WEB. 2014 [1899]. The Philadelphia Negro: A Social Study New York, NY: Oxford Univ. Press [Google Scholar]
  34. Earl J. 2015. CITASA: intellectual past and future. Inf. Commun. Soc. 18:478–91 [Google Scholar]
  35. Ellison NB, Gray R, Lampe C, Fiore AT. 2014. Social capital and resource requests on Facebook. New Media Soc 16:1104–21 [Google Scholar]
  36. Ellison NB, Steinfield C, Lampe C. 2007. The benefits of Facebook “friends”. J. Comput.-Mediat. Commun. 12:1143–68 [Google Scholar]
  37. Eslami M, Rickman A, Vaccaro K, Aleyasen A, Vuong A. et al. 2015. “I always assumed that I wasn't really that close to [her]”: reasoning about invisible algorithms in news feeds. Proc. 33rd Annu. ACM Conf. Hum. Factors Comput. Syst., Seoul, Repub. Korea153–62 New York: ACM [Google Scholar]
  38. Evans JA, Aceves P. 2016. Machine translation: mining text for social theory. Annu. Rev. Sociol. 42:21–50 [Google Scholar]
  39. Fiore AT, Tiernan SL, Smith MA. 2002. Observed behavior and perceived value of authors in Usenet newsgroups: Bridging the gap. Proc. SIGCHI Conf. Hum. Factors Comput. Syst., Minneapolis, MN323–30 New York: ACM [Google Scholar]
  40. Fisher K, Gershuny J, Mullan K, Sullivan O, Morris S. 2015. Innovations and lessons from the UK 2014–2015 everyday life survey. Electron. Int. J. Time Use Res. 12:163–69 [Google Scholar]
  41. Gad S, Javed W, Ghani S, Elmqvist N, Ewing T. et al. 2015. ThemeDelta: dynamic segmentations over temporal topic models. IEEE Trans. Vis. Comput. Graph. 21:672–85 [Google Scholar]
  42. Gad S, Ramakrishnam N, Hampton K, Kavanaugh A. 2012. Bridging the divide in democratic engagement: Studying conversation patterns in advantaged and disadvantaged communities Presented at ASE/IEEE Int. Conf. Soc. Inform., Dec 14–16 Washington, DC: [Google Scholar]
  43. Gans H. 1962. The Urban Villagers New York: Free Press [Google Scholar]
  44. Garton L, Wellman B. 1995. Social impacts of electronic mail in organizations: a review of the research literature. Communication Yearbook BR Burleson 434–51 Thousand Oaks, CA: Sage [Google Scholar]
  45. Geiger RS, Ribes D. 2011. Trace ethnography: following coordination through documentary practices Presented at HICSS, 44th Hawaii Int. Conf. Syst. Sci Honolulu, HI: [Google Scholar]
  46. Gil de Zúñiga H, Jung N, Valenzuela S. 2012. Social media use for news and individuals' social capital, civic engagement and political participation. J. Comput.-Mediat. Commun. 17:319–36 [Google Scholar]
  47. Given LM, Cantrell Winkler D, Willson R, Davidson C, Danby S, Thorpe K. 2016. Parents as coresearchers at home: using an observational method to document young children's use of technology. Int. J. Qual. Methods 15:11–14 [Google Scholar]
  48. Golbeck J, Robles C, Turner K. 2011. Predicting personality with social media. CHI '11 Ext. Abs. Hum. Factors Comput. Syst., Vancouver253–62 New York: ACM [Google Scholar]
  49. Golder SA, Macy MW. 2014. Digital footprints: opportunities and challenges for online social research. Annu. Rev. Sociol. 40:129–52 [Google Scholar]
  50. Gonzales AL. 2014. Health benefits and barriers to cell phone use in low-income urban U.S. neighborhoods: indications of technology maintenance. Mobile Media Commun 2:233–48 [Google Scholar]
  51. Goodman JK, Cryder CE, Cheema A. 2013. Data collection in a flat world: the strengths and weaknesses of Mechanical Turk samples. J. Behav. Decis. Mak. 26:213–24 [Google Scholar]
  52. Goodman PS, Sproull L. 1990. Technology and Organizations San Francisco: Jossey-Bass [Google Scholar]
  53. Goulet LS. 2012. Friends in all the right places: social resources and geography in the age of social network sites PhD Thesis, Univ. Pa. Philadelphia: [Google Scholar]
  54. Gray ML. 2009. Out in the Country: Youth, Media, and Queer Visibility in Rural America New York: New York Univ. Press [Google Scholar]
  55. Greenberg BS, Eastin MS, Skalski P, Cooper L, Levy M, Lachlan K. 2005. Comparing survey and diary measures of Internet and traditional media use. Commun. Rep. 18:1–8 [Google Scholar]
  56. Greitemeyer T. 2011. Effects of prosocial media on social behavior: when and why does media exposure affect helping and aggression?. Curr. Dir. Psychol. Sci. 20:251–55 [Google Scholar]
  57. Hamilton K, Karahalios K, Sandvig C, Eslami M. 2014. A path to understanding the effects of algorithm awareness. CHI '14 Ext. Abs. Hum. Factors Comput. Syst., Toronto631–42 New York: ACM [Google Scholar]
  58. Hampton KN. 2003. Grieving for a lost network: collective action in a wired suburb. Inf. Soc. 19:1–13 [Google Scholar]
  59. Hampton KN. 2007. Neighborhoods in the network society: the e-Neighbors study. Inf. Commun. Soc. 10:714–48 [Google Scholar]
  60. Hampton KN. 2010. Internet use and the concentration of disadvantage. Am. Behav. Sci. 53:1111–32 [Google Scholar]
  61. Hampton KN. 2016. Persistent and pervasive community: new communication technologies and the future of community. Am. Behav. Sci. 60:101–24 [Google Scholar]
  62. Hampton KN, Goulet LS, Albanesius G. 2015. Change in the social life of urban public spaces: the rise of mobile phones and women, and the decline of aloneness over thirty years. Urban Stud 52:1489–504 [Google Scholar]
  63. Hampton KN, Goulet LS, Marlow C, Rainie L. 2012. Why most Facebook users get more than they give: The effect of Facebook “power users” on everyone else Washington, DC: Pew Res. Cent. [Google Scholar]
  64. Hampton KN, Goulet LS, Rainie L, Purcell K. 2011. Social Networking Sites and Our Lives: How People's Trust, Personal Relationships, and Civic and Political Involvement are Connected to Their Use of Social Networking Sites and Other Technologies Washington, DC: Pew Research Cent. [Google Scholar]
  65. Hampton KN, Gupta N. 2008. Community and social interaction in the wireless city. New Media Soc 10:831–50 [Google Scholar]
  66. Hampton KN, Livio O, Goulet LS. 2010. The social life of wireless urban spaces: Internet use, social networks, and the public realm. J. Commun. 60:701–22 [Google Scholar]
  67. Hansen DL, Schneiderman B, Smith MA. 2011. Analyzing Social Media Networks with NodeXL: Insights from a Connected World Burlington, MA: Morgan Kaufmann [Google Scholar]
  68. Hansen MH, Hurwitz WN, Madow WG. 1953. Sample Survey Methods and Theory New York: Wiley [Google Scholar]
  69. Hargittai E. 2015. Is bigger always better? Potential biases of big data derived from social network sites. Ann. Am. Acad. Political Soc. Sci. 659:63–76 [Google Scholar]
  70. Hauser DJ, Schwarz N. 2016. Attentive Turkers: MTurk participants perform better on online attention checks than do subject pool participants. Behav. Res. Methods 48:400–07 [Google Scholar]
  71. Haythornthwaite C. 2002. Strong, weak and latent ties and the impact of new media. Inf. Soc. 18:1–17 [Google Scholar]
  72. Heim J, Brandtzæg P. 2007. Patterns of media usage and the non-professional users Presented at Conf. Hum. Factors in Comput. Syst. (CHI2007) Worksh Apr. 28–May 3 San Jose, CA: [Google Scholar]
  73. Higgins CA, McClean RJ, Conrath DW. 1985. The accuracy and biases of diary communication data. Soc. Netw. 7:173–87 [Google Scholar]
  74. Himelboim I, McCreery S, Smith M. 2013. Birds of a feather tweet together: integrating network and content analyses to examine cross-ideology exposure on Twitter. J. Comput.-Mediated Commun. 18:40–60 [Google Scholar]
  75. Hine C. 2015. Ethnography for the Internet: Embedded, Embodied and Everyday New York: Bloomsbury [Google Scholar]
  76. Howe J. 2006. The rise of crowdsourcing. Wired June 1–4 [Google Scholar]
  77. Howell N. 1990. Surviving Fieldwork Washington, DC: Am. Anthropol. Assoc. [Google Scholar]
  78. Hughes DJ, Rowe M, Batey M, Lee A. 2012. A tale of two sites: Twitter versus Facebook and the personality predictors of social media usage. Comput. Hum. Behav. 28:561–69 [Google Scholar]
  79. Hwang J, Sampson RJ. 2014. Divergent pathways of gentrification: racial inequality and the social order of renewal in Chicago neighborhoods. Am. Sociol. Rev. 79:726–51 [Google Scholar]
  80. Jackson JL. 2012. Ethnography is, ethnography ain't. Cult. Anthropol. 27:480–97 [Google Scholar]
  81. Jungherr A, Schoen H, Posegga O, Jürgens P. 2016. Digital trace data in the study of public opinion: an indicator of attention toward politics rather than political support. Soc. Sci. Comput. Rev. https://dx.doi.org/10.1177/0894439316631043 [Crossref] [Google Scholar]
  82. Kahn AS, Ratan R, Williams D. 2014. Why we distort in self-report: predictors of self-report errors in video game play. J. Comput. Mediat. Commun. 19:1010–23 [Google Scholar]
  83. Katz VS, Gonzalez C. 2016. Community variations in low-income Latino families’ technology adoption and integration. Am. Behav. Sci. 60:59–80 [Google Scholar]
  84. Keeter S, Kennedy C, Dimock M, Best J, Craighill P. 2006. Gauging the impact of growing nonresponse on estimates from a national RDD telephone survey. Public Opin. Q. 70:759–79 [Google Scholar]
  85. Keeter S, Weisel R. 2015. Building Pew Research Center's American Trends Panel Washington, DC: Pew Res. Cent. [Google Scholar]
  86. Kendall L. 2002. Hanging Out in the Virtual Pub Berkeley, CA: Univ. Calif. Press [Google Scholar]
  87. Kiesler S, Sproull LS. 1986. Response effects in the electronic survey. Public Opin. Res. 50:402–13 [Google Scholar]
  88. Klecka WR, Tuchfarber AJ. 1978. Random digit dialing: a comparison to personal surveys. Public Opin. Q. 42:105–14 [Google Scholar]
  89. Kohut A, Keeter S, Doherty C, Dimock M, Christian L. 2012. Assessing the Representativeness of Public Opinion Surveys Washington, DC: Pew Res. Cent. [Google Scholar]
  90. Kozinets RV. 2002. The field behind the screen: using netnography for marketing research in online communities. J. Mark. Res. 39:61–72 [Google Scholar]
  91. Kramer ADI, Guillory JE, Hancock JT. 2014. Experimental evidence of massive-scale emotional contagion through social networks. PNAS 111:8788–90 [Google Scholar]
  92. Kraut R, Olson J, Banaji M, Bruckman A, Cohen J, Couper M. 2004. Psychological research online: report of Board of Scientific Affairs' Advisory Group on the Conduct of Research on the Internet. Am. Psychol. 59:105–17 [Google Scholar]
  93. Krippendorff K. 2004. Content Analysis, an Introduction to Its Methodology Thousand Oaks, CA: Sage [Google Scholar]
  94. Kross E, Verduyn P, Demiralp E, Park J, Lee DS. et al. 2013. Facebook use predicts declines in subjective well-being in young adults. PLOS ONE 8:8e69841 [Google Scholar]
  95. Kuru O, Pasek J. 2016. Improving social media measurement in surveys: avoiding acquiescence bias in Facebook research. Comput. Hum. Behav. 57:82–92 [Google Scholar]
  96. Kwon KH, Stefanone MA, Barnett GA. 2014. Social network influence on online behavioral choices: exploring group formation on social network sites. Am. Behav. Sci. 58:1345–60 [Google Scholar]
  97. Lane J. 2015. The digital street. Am. Behav. Sci. 60:43–58 [Google Scholar]
  98. Lane J. 2016a. The digital street: an ethnographic study of networked street life in Harlem. Am. Behav. Sci. 60:43–58 [Google Scholar]
  99. Lane J. 2016b. Review: Ethnography for the Internet: Embedded, Embodied and Everyday, by Christine Hine. Contemp. Sociol. 45:610–12 [Google Scholar]
  100. Lazer D, Kennedy R, King G, Vespignani A. 2014. The parable of Google Flu: traps in big data analysis. Science 343:1203–5 [Google Scholar]
  101. Lazer D, Pentland A, Adamic L, Aral S, Barabási A-L. et al. 2009. Computational social science. Science 323:721–23 [Google Scholar]
  102. Leskovec J, Horvitz E. 2008. Planetary-scale views on a large instant-messaging network. Proc. 17th Int. Conf. World Wide Web, Beijing915–24 New York: ACM [Google Scholar]
  103. Lewis K, Kaufman J, Gonzalez M, Wimmer A, Christakis N. 2008. Tastes, ties, and time: a new social network dataset using Facebook.com. Soc. Netw. 30:330–42 [Google Scholar]
  104. Licoppe C, Figeac J. 2015. Direct video observation of the uses of smartphones on the move. Mobility and Locative Media: Mobile Communication in Hybrid Spaces M Sheller 48–64 London: Routledge [Google Scholar]
  105. Lin M, Lucas HC, Shmueli G. 2013a. Research commentary—too big to fail: Large samples and the p-value problem. Inf. Syst. Res. 24:906–17 [Google Scholar]
  106. Lin Y-R, Margolin D, Keegan B, Lazer D. 2013b. Voices of victory: a computational focus group framework for tracking opinion shift in real time. Proc. 22nd Int. Conf. World Wide Web, Rio de Janeiro737–48 New York: ACM [Google Scholar]
  107. Livingstone S. 2008. Taking risky opportunities in youthful content creation: teenagers' use of social networking sites for intimacy, privacy and self-expression. New Media Soc 10:393–411 [Google Scholar]
  108. Livingstone S, Helsper E. 2007. Gradations in digital inclusion: children, young people and the digital divide. New Media Soc 9:671–96 [Google Scholar]
  109. Loader BD, Dutton WH. 2012. A decade in Internet time: the dynamics of the Internet and society. Inf. Commun. Soc. 15:609–15 [Google Scholar]
  110. Madden R. 2010. Being Ethnographic: A Guide to the Theory and Practice of Ethnography London: SAGE [Google Scholar]
  111. Mann C, Stewart F. 2000. Internet Communication and Qualitative Research: A Handbook for Researching Online London: SAGE [Google Scholar]
  112. Margolin DB, Goodman S, Keegan B, Lin Y-R, Lazer D. 2016. Wiki-worthy: collective judgment of candidate notability. Inf. Commun. Soc. 19:1029–45 [Google Scholar]
  113. Markham AN. 1998. Life Online: Researching Real Experience in Virtual Space Walnut Creek, CA: AltaMira [Google Scholar]
  114. Mason W, Suri S. 2012. Conducting behavioral research on Amazon's Mechanical Turk. Behav. Res. Methods 44:1–23 [Google Scholar]
  115. Massey DS, Rugh JS. 2014. Segregation in post-civil rights America: stalled integration or end of the segregated century?. Du Bois Rev. Soc. Sci. Res. Race 11:205–32 [Google Scholar]
  116. Mayer-Schönberger V, Cukier K. 2013. Big Data: A Revolution that Will Transform how We Live, Work, and Think Boston: Houghton Mifflin Harcourt [Google Scholar]
  117. Michelson WM. 2005. Time Use: Expanding Explanation in the Social Sciences Boulder, CO: Paradigm Publishers [Google Scholar]
  118. Morel J, Licoppe C. 2010. Studying mobile video telephony. Mobile Methods M Büscher, J Urry, K Witcher 164–82 London: Routledge [Google Scholar]
  119. Morstatter F, Pfeffer J, Liu H, Carley KM. 2013. Is the sample good enough? Comparing data from Twitter's streaming API with Twitter's Firehose. Proc. 7th Int. AAAI Conf. Weblogs Soc. Media, Cambridge, MA, July 8–11400–8 Menlo Park, CA: AAAI Press [Google Scholar]
  120. Nehls K, Smith BD, Schneider HA. 2014. Video-conferencing interviews in qualitative research. Enhancing Qualitative and Mixed Methods Research with Technology S Hai-Jew 140–57 Hershey, PA: IGI Global [Google Scholar]
  121. Norman D. 1990. The Design of Everyday Things New York: Doubleday [Google Scholar]
  122. Ofcom. 2015. Adults Media Use and Attitudes London: Ofcom https://www.ofcom.org.uk/research-and-data/media-literacy-research/adults-media-use-and-attitudes [Google Scholar]
  123. Paolacci G, Chandler J. 2014. Inside the Turk: understanding Mechanical Turk as a participant pool. Curr. Dir. Psychol. Sci. 23:184–88 [Google Scholar]
  124. Paolacci G, Chandler J, Ipeirotis PG. 2010. Running experiments on Amazon Mechanical Turk. Judgm. Decis. Mak. 5:411–19 [Google Scholar]
  125. Pew Res. Cent. 2015a. Libraries at the Crossroads Washington, DC: Pew Res. Cent http://www.pewinternet.org/2015/09/15/libraries-at-the-crossroads/ [Google Scholar]
  126. Pew Res. Cent. 2015b. March 17–April 12, 2015—libraries and technology use dataset Pew Res. Cent Washington, DC: updated Nov. 4. http://www.pewinternet.org/2015/08/19/mobile-messaging-and-social-media-2015/ [Google Scholar]
  127. Pontin J. 2007. Artificial intelligence, with help from the humans. New York TimesMar. 25 BU5 [Google Scholar]
  128. Rader E. 2017. Examining user surprise as a symptom of algorithmic filtering. Int. J. Hum.-Comput. Stud. 98:72–88 [Google Scholar]
  129. Radford J, Pilny A, Reichelmann A, Keegan B, Welles BF. et al. 2016. Volunteer science. Soc. Psychol. Q. 79:376–96 [Google Scholar]
  130. Rainie L, Wellman B. 2012. Networked: The New Social Operating System Cambridge, MA: MIT Press [Google Scholar]
  131. Reips U-D. 2001. The web experimental psychology lab: five years of data collection on the Internet. Behav. Res. Methods Instrum. Comput. 33:201–11 [Google Scholar]
  132. Reisdorf BC, Axelsson A-S, Maurin H. 2016. Living offline—a qualitative study of Internet non-use in Great Britain and Sweden Work. Pap., Quello Cent East Lansing, MI: [Google Scholar]
  133. Reisdorf BC, Jewkes Y. 2016. (B)Locked sites: cases of Internet use in three British prisons. Inf. Commun. Soc. 19:771–86 [Google Scholar]
  134. Rheingold H. 1993. The Virtual Community Reading, MA: Addison-Wesley [Google Scholar]
  135. Rice R. 1990. Computer-mediated communication system network data. Int. J. Man-Mach. Stud. 32:627–47 [Google Scholar]
  136. Robinson J, Godbey G. 1997. Time for Life: The Surprising Ways Americans Use Their Time University Park, PA: Pa. State Press [Google Scholar]
  137. Robinson JP, Martin S. 2010. IT use and declining social capital? More cold water from the General Social Survey (GSS) and the American Time-Use Survey (ATUS). Soc. Sci. Comput. Rev. 28:45–63 [Google Scholar]
  138. Robinson JP, Tracy E. 2015. Cruising through the millennium? 2003–2013 changes in American daily life Work. Pap., Maryland Pop. Res. Cent College Park, MD: [Google Scholar]
  139. Robinson L, Cotten SR, Ono H, Quan-Haase A, Mesch G. et al. 2015. Digital inequalities and why they matter. Inf. Commun. Soc. 18:569–82 [Google Scholar]
  140. Robinson L, Schulz J. 2009. New avenues for sociological inquiry: evolving forms of ethnographic practice. Sociology 43:685–98 [Google Scholar]
  141. Rouse SV. 2015. A reliability analysis of Mechanical Turk data. Comput. Hum. Behav. 43:304–7 [Google Scholar]
  142. Rubinson C. 2014. Ontological considerations when modeling missing data with relational databases. Soc. Sci. Comput. Rev. 32:769–80 [Google Scholar]
  143. Schober MF, Pasek J, Guggenheim L, Lampe C, Conrad FG. 2016. Social media analyses for social measurement. Public Opin. Q. 80:180–211 [Google Scholar]
  144. Sessions LF. 2010. How offline gatherings affect online communities: when virtual community members “meetup.”. Inf. Commun. Soc. 13:375–95 [Google Scholar]
  145. Shapiro DN, Chandler J, Mueller PA. 2013. Using Mechanical Turk to study clinical populations. Clin. Psychol. Sci. 1:2213–20 [Google Scholar]
  146. Shapka JD, Domene JF, Khan S, Yang LM. 2016. Online versus in-person interviews with adolescents: an exploration of data equivalence. Comput. Hum. Behav. 58:361–67 [Google Scholar]
  147. Sharma SK, Willis M, Snyder J, Østerlund C, Sawyer S. 2015. Using ethnography of email to understand distributed scientific collaborations Presented at iConference 2015 March 24–27 Newport Beach, CA: [Google Scholar]
  148. Shaw LH, Gant LM. 2002. In defense of the Internet: The relationship between Internet communication and depression, loneliness, self-esteem, and perceived social support. CyberPsychol. Behav. 5:157–71 [Google Scholar]
  149. Shih C-F, Venkatesh A. 2004. Beyond adoption: development and application of a use-diffusion model. J. Mark. 68:59–72 [Google Scholar]
  150. Singer E. 2006. Introduction: nonresponse bias in household surveys. Public Opin. Q. 70:637–45 [Google Scholar]
  151. Sinha I. 1999. The Cybergypsies: A True Tale of Lust, War, and Betrayal on the Electronic Frontier New York: Viking [Google Scholar]
  152. Sproull L, Kiesler S. 1991. Connections: New Ways of Working in the Networked Organization Cambridge, MA: MIT Press [Google Scholar]
  153. Stefanone MA, Kwon KH, Lackaff D. 2012. Exploring the relationship between perceptions of social capital and enacted support online. J. Comput.-Mediated Commun. 17:451–66 [Google Scholar]
  154. Stoecker R. 1991. Evaluating and rethinking the case study. Sociol. Rev. 39:88–112 [Google Scholar]
  155. Tumasjan A, Sprenger TO, Sandner PG, Welpe IM. 2011. Election forecasts with Twitter: how 140 characters reflect the political landscape. Soc. Sci. Comput. Rev. 29:402–18 [Google Scholar]
  156. Turkle S. 1997. Life on the Screen: Identity in the Age of the Internet New York: Simon & Schuster [Google Scholar]
  157. US Bur. Census. 1975. Historical Statistics of the United States, Colonial Times to 1970 Washington, DC: US Gov. Print. Off. [Google Scholar]
  158. van Deursen AJAM, Helsper EJ, Eynon R. 2016. Development and validation of the Internet Skills Scale (ISS). Inf. Commun. Soc. 19:804–23 [Google Scholar]
  159. Vandewater EA, Lee S-J. 2009. Measuring children's media use in the digital age issues and challenges. Am. Behav. Sci. 52:1152–76 [Google Scholar]
  160. Velez JA, Greitemeyer T, Whitaker JL, Ewoldsen DR, Bushman BJ. 2016. Violent video games and reciprocity: the attenuating effects of cooperative game play on subsequent aggression. Commun. Res. 43:447–67 [Google Scholar]
  161. Vitak J, Zube P, Smock A, Carr CT, Ellison N, Lampe C. 2010. It's complicated: Facebook users' political participation in the 2008 election. Cyberpsychol. Behav. Soc. Netw. 14:107–14 [Google Scholar]
  162. Watts DJ. 2011. Everything Is Obvious: Once You Know the Answer New York: Crown Business [Google Scholar]
  163. Webster JG, Wakshlag J. 1985. Measuring exposure to television. Selective Exposure to Communication D Zillmann, J Bryant 35–62 Hillsdale, NJ: Lawrence Erlbaum [Google Scholar]
  164. Wellman B. 1979. The community question: the intimate networks of East Yorkers. Am. J. Sociol. 84:1201–31 [Google Scholar]
  165. Wellman B. 1999. The network community. Networks in the Global Village B Wellman 1–48 Boulder, CO: Westview [Google Scholar]
  166. Wellman B. 2004. The three ages of Internet studies: ten, five and zero years ago. New Media Soc 6:123–29 [Google Scholar]
  167. Wellman B. 2006. Sociologists engaging with computers: introduction to the Symposium on the History of CITASA, 1988 to 2005: from microcomputers to communication and information technologies. Soc. Sci. Comput. Rev. 24:139–49 [Google Scholar]
  168. Wellman B, Hampton KN. 1999. Living networked on and offline. Contemp. Sociol. 28:648–54 [Google Scholar]
  169. Wells C, Thorson K. 2017. Combining big data and survey techniques to model effects of political content flows in Facebook. Soc. Sci. Comput. Rev. 35:33–52 [Google Scholar]
  170. Wesler H, Smith M, Fisher D, Gleave E. 2008. Distilling digital traces: computational social science approaches to studying the Internet. The Sage Handbook of Online Research Methods N Fielding, RM Lee, G Blank 116–40 London: SAGE [Google Scholar]
  171. Zhao S. 2006. Do Internet users have more social ties? A call for differentiated analyses of Internet use. J. Comput.-Mediated Commun. 11:844–62 [Google Scholar]
  172. Zipf GK. 1949. Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology Cambridge, MA: Addison-Wesley [Google Scholar]
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