Web Survey Bibliography
Title Social Media in Public Opinion Research: Report of the AAPOR Task Force on Emerging Technologies in Public Opinion Research
Author Murphy, J., Link, M. W., Childs, J. H., Tesfaye, C., Dean, E., Stern, M. J., Pasek, J., Cohen, J., Callegaro, M., Harwood, P. G.
Source AAPOR
Year 2014
Access date 04.06.2014
Full text
Abstract
Public opinion research is entering a new era, one in which traditional survey research may play a less dominant role. The proliferation of new technologies, such as mobile devices and social media platforms, are changing the societal landscape across which public opinion researchers operate. As these technologies expand, so does access to users’ thoughts, feelings and actions expressed instantaneously, organically, and often publicly across the platforms they use. The ways in which people both access and share information about opinions, attitudes, and behaviors have gone through perhaps a greater transformation in the last decade than in any previous point in history and this trend appears likely to continue. The ubiquity of social media and the opinions users express on social media provide researchers with new data collection tools and alternative sources of qualitative and quantitative information to augment or, in some cases, provide alternatives to more traditional data collection methods. The reasons to consider social media in public opinion and survey research are no different than those of any alternative method. We are ultimately concerned with answering research questions, and this often requires the collection of data in one form or another. This may involve the analysis of data to obtain qualitative insights or quantitative estimates. The quality of data and ability to help accurately answer research questions is of paramount concern. Other practical considerations include the cost efficiency of the method and speed at which the data can be collected, analyzed, and disseminated. If the combination of data quality, cost efficiency, and timeliness required by a study can best be achieved through the use of social media, then there is reason to consider these methods for research. An additional reason to consider social media in public opinion and survey research is its explosion in popularity over the last several years. At a time when many are eschewing landline telephones (Blumberg and Luke, 2013) or actively taking steps to prevent unsolicited contact (e.g. caller ID, restricted access buildings), many are now communicating and interacting online via social networking sites. It is only natural for researchers to aim to meet potential respondents where they have the best chance of getting their attention and potentially gaining their cooperation. However, this brave new world is not without its share of issues and pitfalls – technological, statistical, methodological, and ethical, and much remains to be investigated. As the leading association of public opinion research professionals, AAPOR is uniquely situated to examine and assess the potential impact of new “emerging technologies” on the broader discipline and industry of opinion research. In September 2012, AAPOR Council approved the formation of the Emerging Technologies Task Force with the goal of focusing on two critical areas: smartphones as data collection vehicles and social media as platform and information source. The current report focuses on social media; a companion report covers mobile data collection. This report examines the potential impact of social media on public opinion research – as a vehicle for facilitating some aspect of the survey research process (i.e., questionnaire development, recruitment, locating, etc.) and/or augmenting or replacing traditional survey research methods (i.e., content analysis of existing data). We distinguish between qualitative insights and quantitative indicators from social media and discuss the factors that must be evaluated to determine its fitness for use.
Access/Direct link
Year of publication2014
Bibliographic typeReports, seminars
Web survey bibliography - Callegaro, M. (41)
- Assessing the Accuracy of 51 Nonprobability Online Panels and River Samples: A Study of the Advertising...; 2016; Yang,Y.;Callegaro,M.;Yang,Y.;Callegaro,M.;Chin,K.;Yang,Y.;Villar,A.;Callegaro, M.; Chin, K.; Krosnick...
- Report of the Inquiry into the 2015 British general election opinion polls; 2016; Sturgis, P., Baker, N., Callegaro, M., Fisher, St., Green, J., Jennings, W., Kuha, J., Lauderdale, B...
- The quality of data collected using online panels: a decade of research ; 2015; Callegaro, M.
- Metrics and Design Tool for Building and Evaluating Probability-Based Online Panels; 2015; DiSogra, C.; Callegaro, M.
- Yes-no answers versus check-all in self-administered modes ; 2015; Callegaro, M.; Henderson, V.; Murakami, M.; Tepman, Z.
- A critical review of studies investigating the quality of data obtained with online panels based on...; 2014; Callegaro, M., Villar, A., Yeager, D. S., Krosnick, J. A.
- Online panel research: History, concepts, applications and a look at the future; 2014; Callegaro, M., Baker, R., Bethlehem, J., Goeritz, A., Krosnick, J. A., Lavrakas, P. J.
- Recent Books and Journals in Public Opinion, Survey Methods, and Survey Statistics; 2014; Callegaro, M.
- Paradata in web surveys; 2013; Callegaro, M.
- From mixed-mode to multiple devices. Web surveys, smartphone surveys and apps: has the respondent gone...; 2013; Callegaro, M.
- Web coverage in the UK and its potential impact on general population web surveys; 2013; Callegaro, M.
- Effects of Progress Indicators on Short Questionnaires; 2012; Sedley, A., Callegaro, M.
- Effects of Pagination on Short Online Surveys; 2012; Sedley, A., Callegaro, M.
- A Systematic Review of Studies Investigating the Quality of Data Obtained with Online Panels; 2012; Callegaro, M., Villar, A., Krosnick, J. A., Yeager, D. S.
- A taxonomy of paradata for web surveys and computer assisted self interviewing (Casi); 2012; Callegaro, M.
- Unpublisihed internal Google report on break off rates by device type; 2011; Callegaro, M.
- Should we use the progress bar in online surveys? A meta-analysis of experiments manipulating progress...; 2011; Callegaro, M., Yang, Y., Villar, A.
- IVR and web administration in structured interviews utilizing rating scales: exploring the role of motivation...; 2011; Yang, Y., Callegaro, M., Bhola, D. S., Dillman, D. A.
- The Effect of Email Invitation Subject Title and Text on Online Survey Completion Rates in Internet...; 2009; Kruse, Y., Thomas, M., Nukulkij, P., Callegaro, M.
- Differences Between Internet and Non-Internet Households on Survey Items: Do These Differences Disappear...; 2009; Zhang, C., Callegaro, M., Thomas, M.
- Producing Straightlining and Item Non-Differentiation in a Web Survey: How Visual Design Plays a Role...; 2009; Callegaro, M., Shand-Lubbers, J., Dennis, J. M.
- Do we hear different voices?: Investigating the differences between internet and non-internet users...; 2009; Zhang, C., Callegaro, M., Thomas, M., DiSogra, C.
- The Effect of Email Invitation Customization on Survey Completion Rates in an Internet Panel: A Meta...; 2009; Callegaro, M., Kruse, Y., Thomas, M., Nukulkij, P.
- Panel Conditioning and Attrition in the AP-Yahoo! News Election Panel Study; 2009; Kruse, Y., Callegaro, M., Dennis, J. M., DiSogra, C., Subias, S., Lawrence, M., Tompson, T.
- Recruiting Probability-Based Web Panel Members Using an Address-Based Sample Frame: Results from a Pilot...; 2009; DiSogra, C., Callegaro, M., Hendarwan, E.
- Presentation of a Single Item versus a Grid: Effects on the Vitality and Mental Health Scales of the...; 2009; Callegaro, M., Shand-Lubbers, J., Dennis, J. M.
- Computing Response Rates for Probability-Based Web Panels; 2009; DiSogra, C., Callegaro, M.
- Is the digital divide still closing? New evidence points to skewed online results absent non-Internet...; 2008; Callegaro, M., Wells, T.
- Effects of Pre-coding Response Options for Five Point Satisfaction Scale in Web Surveys; 2008; Callegaro, M., Wells, T., Kruse, Y.
- An implementation of a within-household selection procedure for web surveys; 2008; Callegaro, M., Osborn, L., Debell, M., Leuvano, P.
- Response options order effect and category number association: An experiment using items on a five point...; 2008; Tang, G., Callegaro, M.
- More than the digital divide?: Investigating the differences between Internet and non-Internet users; 2008; Zhang, C., Callegaro, M., Thomas, M.
- “R U in the Network?!” Using Text Messaging Interfaces as Screeners for Working Cell Phone...; 2008; Buskirk, T. D., Rao, K., Callegaro, M., Arens, Z., Steiger, D. M.
- Computing Metrics for Online Panels; 2008; Callegaro, M., DiSogra, C.
- Impact of new technologies in data collection methods; 2008; Callegaro, M.
- Key Issues in Research Accuracy: Sources of bias and error in online research; 2008; Dennis, J. M., Callegaro, M.
- The influence of mobile telephones on telephone surveys; 2008; Kuusela, V., Callegaro, M., Vehovar, V.
- The influence of advance letters on response in telephone surveys; 2007; de Leeuw, E. D., Callegaro, M., Hox, J., Korendijk, E., Lensvelt-Mulders, G. J.
- Using Text Messages in U.S. Mobile Phone Surveys ; 2007; Steeh, C. G., Buskirk, T. D., Callegaro, M.
- Response latency as an indicator of optimizing. A study comparing job applicants and job incumbents...; 2004; Callegaro, M., Yang, Y., Bhola, D. S., Dillman, D. A.
- Electronic Voting Machines – A comparison applying the principles of computer-human interaction...; 2003; Callegaro, M., Peytcheva, E.