Web Survey Bibliography
Title Web Probing for Question Evaluation: The Effects of Probe Placement
Author Fowler, S.; Willis, G. B.; Moser, R. P.; Townsend, R. L. M.; Maitland, A.; Sun, H.; Berrigan, D.
Year 2016
Access date 09.06.2016
Abstract
Though the majority of cognitive interviewing is conducted in a laboratory environment by specialized interviewers, an alternative procedure,embedded probing, was developed decades ago (Converse & Presser, 1986; Schuman, 1966). Embedded probing involves the evaluation of select items through the administration of cognitive probes within a field survey environment, relying on much larger samples than are cost-effective with standard cognitive interviewing. Embedded probing has recently resurfaced in the form of web probing, mainly prompted by the increasing availability of internet-based platforms that provide fast, inexpensive access to respondents (Behr, Braun, Kaczmirek, & Bandilla,
2013; Murphy, Keating, and Edgar, 2013). However, little guidance exists concerning specific practices. In particular, as for standard cognitive interviewing, it is unclear whether probes should be administered concurrently with the target item, or retrospectively at the end of the interview (i.e. debriefing). This study examined web-based probing in the context of questionnaire pretesting and evaluation, relying on respondents from Amazon’s Mechanical Turk (mTurk), an online labor crowdsourcing platform. A questionnaire segment on walking behavior and attitudes, selected from the National Health Interview Survey (NHIS), was presented to 1,444 mTurk respondents. Probes were embedded for 4 targeted items. For example, respondents were asked: “When answering the question -- Where you live, are there roads, sidewalks, paths, or trails where you can walk? – Please say more about what you were thinking about...”, then typed their response to the probe into an open text box. As an experimental manipulation, approximately half of respondents answered the probes concurrently, and the remainder responded retrospectively. We will present the results of an analysis that compares the responses obtained as a function of probe placement, by assessing quantitative metrics such as number of words entered, as well as qualitative measures, including thematic coding and rater judgment of usefulness of the information obtained.
2013; Murphy, Keating, and Edgar, 2013). However, little guidance exists concerning specific practices. In particular, as for standard cognitive interviewing, it is unclear whether probes should be administered concurrently with the target item, or retrospectively at the end of the interview (i.e. debriefing). This study examined web-based probing in the context of questionnaire pretesting and evaluation, relying on respondents from Amazon’s Mechanical Turk (mTurk), an online labor crowdsourcing platform. A questionnaire segment on walking behavior and attitudes, selected from the National Health Interview Survey (NHIS), was presented to 1,444 mTurk respondents. Probes were embedded for 4 targeted items. For example, respondents were asked: “When answering the question -- Where you live, are there roads, sidewalks, paths, or trails where you can walk? – Please say more about what you were thinking about...”, then typed their response to the probe into an open text box. As an experimental manipulation, approximately half of respondents answered the probes concurrently, and the remainder responded retrospectively. We will present the results of an analysis that compares the responses obtained as a function of probe placement, by assessing quantitative metrics such as number of words entered, as well as qualitative measures, including thematic coding and rater judgment of usefulness of the information obtained.
Access/Direct link Conference Homepage (abstract)
Year of publication2016
Bibliographic typeConferences, workshops, tutorials, presentations
Web survey bibliography (4086)
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- Handbook of Research Methods in Health Social Sciences; 2017; Liamputtong, P.
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- Millennials and emojis in Spain and Mexico.; 2017; Bosch Jover, O.; Revilla, M.
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- Do distractions during web survey completion affect data quality? Findings from a laboratory experiment...; 2017; Wenz, A.
- Predicting Breakoffs in Web Surveys; 2017; Mittereder, F.; West, B. T.
- Measuring Subjective Health and Life Satisfaction with U.S. Hispanics; 2017; Lee, S.; Davis, R.
- Humanizing Cues in Internet Surveys: Investigating Respondent Cognitive Processes; 2017; Jablonski, W.; Grzeszkiewicz-Radulska, K.; Krzewinska, A.
- A Comparison of Emerging Pretesting Methods for Evaluating “Modern” Surveys; 2017; Geisen, E., Murphy, J.
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- Pushing to web in the ISSP; 2017; Jonsdottir, G. A.; Dofradottir, A. G.; Einarsson, H. B.
- The 2016 Canadian Census: An Innovative Wave Collection Methodology to Maximize Self-Response and Internet...; 2017; Mathieu, P.
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- In search of best practices; 2017; Kappelhof, J. W. S.; Steijn, S.
- Redirected Inbound Call Sampling (RICS); A New Methodology ; 2017; Krotki, K.; Bobashev, G.; Levine, B.; Richards, S.
- An Empirical Process for Using Non-probability Survey for Inference; 2017; Tortora, R.; Iachan, R.
- The perils of non-probability sampling; 2017; Bethlehem, J.
- A Comparison of Two Nonprobability Samples with Probability Samples; 2017; Zack, E. S.; Kennedy, J. M.
- Rates, Delays, and Completeness of General Practitioners’ Responses to a Postal Versus Web-Based...; 2017; Sebo, P.; Maisonneuve, H.; Cerutti, B.; Pascal Fournier, J.; Haller, D. M.
- Necessary but Insufficient: Why Measurement Invariance Tests Need Online Probing as a Complementary...; 2017; Meitinger, K.
- Nonresponse in Organizational Surveying: Attitudinal Distribution Form and Conditional Response Probabilities...; 2017; Kulas, J. T.; Robinson, D. H.; Kellar, D. Z.; Smith, J. A.
- Theory and Practice in Nonprobability Surveys: Parallels between Causal Inference and Survey Inference...; 2017; Mercer, A. W.; Kreuter, F.; Keeter, S.; Stuart, E. A.
- Is There a Future for Surveys; 2017; Miller, P. V.
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- A Working Example of How to Use Artificial Intelligence To Automate and Transform Surveys Into Customer...; 2017; Neve, S.
- A Case Study on Evaluating the Relevance of Some Rules for Writing Requirements through an Online Survey...; 2017; Warnier, M.; Condamines, A.
- Estimating the Impact of Measurement Differences Introduced by Efforts to Reach a Balanced Response...; 2017; Kappelhof, J. W. S.; De Leeuw, E. D.
- Targeted letters: Effects on sample composition and item non-response; 2017; Bianchi, A.; Biffignandi, S.