Web Survey Bibliography
The most common CARI objective (to date) has been to identify potential errors (interviewer falsi cation, other interviewer errors, response errors), but identi cation really results from CARI coupled with other tools. This paper discusses a speci c application of CARI within a larger system designed to quickly identify both interviewer errors, and potential measurement error resulting from question design and implementation. Using CARI within this larger system provided the necessary tools to identify problems early in the data collection period, diminishing the potential for a negative impact on nal data quality. The system has three components: audio-recording selected questions but random subsets recorded during any one interview; behavior coding of recordings; and report generation { analysis tables generated and reviewed weekly. The system provides visibility into both interviewer and question performance; early and frequent review of information on both interviewers/questions, with flexibility in terms of amount sampled/coded and approach towards coding; and quantitative summary of results that can be veri ed, diminishing some of the caveats with a pure qualitative approach.
Our discussion of the speci c application is based on experience with a comparative establishment survey. Comparative surveys collect data from multiple groups (people living in dierent countries, speaking different languages, aging in dierent cohorts) in ways that support group comparisons. Establishment surveys are often concerned with dierent types of organizations. In 2007 we used CARI on the establishment-based National. Home and Hospice Care Survey (NHHCS), sponsored by the National Center for Health Statistics. The NHHCS was designed to produce nationally representative data on home health care agencies and hospice care agencies. Although both agency types provide health care in the home and their services overlap, they dier in some important ways. Hospices provide palliative end-of-life care; home health agencies provide care for people with a wide range of conditions and functional limitations, for treatment and rehabilitation purposes.
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Web survey bibliography (95)
- Virtual reality meets sensory research; 2017; Depoortere, L.
- Methods for Evaluating Respondent Attrition in Web-Based Surveys; 2016; Hochheimer, C. J.; Sabo, R. T.; Krist, A. H.; Day, T.; Cyrus, J.; Woolf, S. H.
- Exploration of Methods for Blending Unconventional Samples with Traditional Probability Samples; 2016; Gellar, J.; Zhou, H.; D.; Sinclair, M. D.
- Ratio of Vector Lengths as an Indicator of Sample Representativeness ; 2016; Shin, H. C.
- Online and Social Media Data As an Imperfect Continuous Panel Survey; 2016; Diaz, F.; Garmon, F.; Hofman, J. K.; Kiciman, E.; Rothschild, D.
- Validating self-reported mobile phone use in adults using a newly developed smartphone application; 2015; Goedhart, G., Kromhout, H., Wiart, J., Vermeulen, R.
- Innovative Uses of Paradata Across Diverse Contexts ; 2015; Cheung, G.; Pennell, B.-E.
- Build your own social network laboratory with Social Lab: a tool for research in social media; 2014; Garaizar, P., Reips, U.-D.
- Picking up the Bread Crumbs: Holistic Insights from Social Media; 2014; Souda, P.
- Survey optimisation considerations for Android, Apple and Windows 8 mobile devices; 2013; Owen, R.
- Second Life as a Survey Lab: Exploring the Randomized Response Technique in a Virtual Setting; 2013; Richards, A., Dean, E.
- Virtual Cognitive Interviewing Using Skype and Second Life; 2013; Dean, E., Head, B., Swicegood, J. E.
- Assessing Nonresponse Bias in the Green Technologies and Practices Survey; 2013; Meekins, B., Sverchkov, M., Stang, S.
- Why Big Data is a Small Idea…and Why You Shouldn’t Worry So Much; 2013; Needel, S.
- Doing real time research: Opportunities and challenges; 2013; Back, L., Lury, C., Zimmer, R.
- Digital technology and data collection; 2013; Henriksen, B., Jewitt, C., Price, S., Sakr, M.
- Effects of Self-Awareness on Disclosure During Skype Survey Interviews; 2013; Feuer, S., Schober, M. F.
- Cognitive Interviewing in Online Modes: a Comparison of Data Collected in Second Life and Skype; 2013; Swicegood, J. E., Head, B., Dean, E., Keating, M.
- Effects of Displaying Videos on Measurement in a Web Survey; 2013; Mendelson, J., Gibson, J. L., Romano Bergstrom, J. C.
- Classifying Mouse Movements to Predict Respondent Difficulty; 2013; Horwitz, R.
- Are You Seeing What I am Seeing? Exploring Response Option Visual Design Effects With Eye-Tracking; 2013; Libman, A., Smyth, J. D., Olson, K.
- Survey Reminder Method Experiment: An Examination of Cost Efficiency and Reminder Mode Salience in the...; 2013; Anderson, M., Rogers, B., CyBulski, K., Hall, J. W., Alderks, C. E., Milazzo-Sayre, L.
- Virtual research assistants: Replacing human interviewers by automated avatars in virtual worlds; 2013; Hasler, B. S., Tuchman, P., Friedman, D.
- From mixed-mode to multiple devices. Web surveys, smartphone surveys and apps: has the respondent gone...; 2013; Callegaro, M.
- Internet-Mediated Technologies and Mixed Methods Research; Problems and Prospects; 2012; Hesse-Biber, S.; Griffin, A. J.
- An Introduction to Using Video for Research; 2012; Jewitt, C.
- Online Surveys Aren't Just for Computers Anymore! Exploring Potential Mode Effects between Smartphone...; 2012; Buskirk, T. D., Andrus, C.
- Smartphone Apps and User Engagement: Collecting Data in the Digital Era; 2012; Link, M. W.
- Specific mixed-mode methodology to reach sensory disabled people in quantitative surveys; 2012; Fontaine, S.
- Facing The Future Webcams as a survey tool in China; 2012; Gordon, A., Llewellyn, T., Gu, E.
- Comfortable in the new medium: How online qual can benefit from our share-happy culture ; 2012; Rubenstein, P.
- Using Collaborative Web Technology to Construct the Health Information National Trends Survey; 2012; Moser, R. P., Beckjord, E. B., Finney Rutten, L. J., Blake, K., Hesse, B. W.
- The Representativity of Web Surveys of the General Population compared to Traditional Modes and Mixed...; 2012; Klausch, L. T., Schouten, B., Hox, J.
- Time use data collection using Smartphones: Results of a pilot study among experienced and inexperienced...; 2012; Scherpenzeel, A., Sonck, N., Fernee, H., Morren, Me.
- Using Webinar Polls to Collect Online Survey Data: The Case of a Behavioral Finance Problem; 2012; Sahu, C.
- The Game Experiments: Researching how gaming techniques can be used to improve the quality of feedback...; 2011; Sleep, D., Puleston, J.
- The benefits and constraints of e-mail interviews and discussions as methods of accessing valid data; 2011; Roberts, An.
- Facial imaging: The new face of online survey research; 2011; Gordon, A., McCallum, D., Sorci, M., Llewellyn, T.
- On Affordances and Technological Intersubjectivity; 2011; Vatrapu, R.
- Building online communities; 2011; Mlačić, B., Milas, G., Mikloušić, I.
- Eye Tracking in testing questionnaires: What’s the added value?; 2011; Tries, S.
- Video enhanced web survey; 2011; Fuchs, M., Kunz, T., Gebhard, F.
- Engagement, Consistency, Reach – why the Technology Landscape Precludes All Three; 2011; Johnson, A., Rolfe, G.
- Twitter mood predicts the stock market.; 2011; Bollen, J., Mao, H., Zeng, X.-J.
- Web based CATI on Amazon Elastic Compute Cloud and VirtualBox using queXS; 2011; Zammit, A.
- Web/Cloud Based CATI Using queXS; 2011; Zammit, A.
- Partnership-Driven Resources to Improve and Enhance Research (PRIMER): A Survey of Community-Engaged...; 2011; Dolor, R. J., Greene, S. M., Thompson, E., Baldwin, L.-M., Neale, A. V.
- Weaving the Web into Personal Communication Networks: A Mobile Phone Based Study of Smartphone Users; 2011; Kobayashi, T., Boase, J.
- Different functioning of rating scale formats – results from psychometric and physiological experiments...; 2011; Koller, M., Salzberger, T.
- Measurement invariance in training evaluation: Old question, new context; 2011; P., Gissel, A., Stoughton, J. W., Whelan, T. J.Clark, A. P.