#JSM2018 Rebecca Powell from @RTI_Intl talking about an experiment on Add Health shifting from interviewer administered to self administered survey
#JSM2018 Powell moved to a 55 self-administered survey from 90 minutes interviewer administered. Worried about response burden with this length of self-admin survey. Randomized n=7600 into either full 55 minute survey or 2 modules- one 35 minutes then 20 minutes.
#JSM2018 Powell Could select to continue on the web. In paper, had to first complete module A, then sent module B. Cover letters told about modules in the incentive part, but not up front. $55 incentive total in each condition
#JSM2018 Powell Full mail survey is 60 pages (!!!). They call this the “thud factor “ because the survey thuds on the table when you put it down. Short survey is 30 pages. (Still long!)
#JSM2018 Powell Singular design has higher response rate by end of data collection period
#JSM2018 Powell Only 6% of respondents did modular survey in two sittings, most did in one sitting (on web)
#JSM2018 powell Breaking web web survey into two modules increased item NR rates, especially for people who did in two sittings. People who did in two sittings took shorter time, not as attentive
#JSM2018 Powell Break-offs all came from the two sitting group - 14 of 20 breakoffs came from people not returning to survey
#JSM2018 Powell Perception of length - most people prefer one long survey
#JSM2018 Powell Takeaways- But reminds us that this is a longitudinal Survey with lots of contact over time.
#JSM2018 Powell Respondents had the mail survey in front of them, so this may have affected decisions to do online in one sitting
#JSM2018 Powell Have mail survey too - look at in future
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#JSM2018 Tobias Schmidt Looking at interviewer experience and interview duration
#JSM2018 Schmidt In this survey, duration linked to interviewer salaries.
#JSM2018 Schmidt Looking at interviewer experience over the course of survey and respondent experience within survey and experience over repeated surveys. Looking in particular at experience within panel survey for both Iers and Rs
#JSM2018 Wuyts Interested in within-survey workload. Use call history data and interview time data. Some Measure workload by fixed measures of experience and interview order cumulated over the field period. They use actual number of cases assigned at time t in field period
#JSM2018 Wuyts Use Paradata to create new measures of interview workload, based on sample units assigned on given day
#JSM2018 The brilliant Susan Murphy is this year’s Fisher Lecture award recipient!
#JSM2018 Murphy Lab does sequential experimentation in improving health. Some for companies.
#JSM2018 Murphy Experimentation and continual optimization is key. How do we use learning as an experiment is put into the field to improve outcomes for individuals? Mobile interventions are key here. Intervention may be either a push intervention or pull intervention
#JSM2018 Next up Hubert Hamer from NASS talking about NASS Small Area Estimation
#JSM2018 Hamer NASS has Agriculture Loss Coverage County Option program. Payments triggered based on county crop revenue falling below program guarantee. NASS surveys used to make this decision, along with other data
#JSM2018 Hamer Program paid out $3.7 billion on 2016. Small changes can affect payments
#JSM2018 Peter Miller appearing as a Northwestern University emeritus professor, providing comments on the CNSTAT reports
#JSM2018 Miller Survey paradigm vs multiple data source paradigm. Surveys may become irrelevant b/c they are slow, not granular, not nimble, costly, not sustainable
#JSM2018 Miller Multiple Data sources require new: methods, computing resources, privacy protections, training, data quality frameworks. Not cheap. What does this give us?
#JSM2018 Panel on CNSTAT report on Federal Statistics, Multiple Data Sources, and Privacy Protection, with @fraukolos kicking off The discussion
#JSM2018@fraukolos Goal of panel to evaluate combining data sources to possibly replace / augment surveys. Two reports out of the panel.
#JSM2018@fraukolos Conclusions: Current Federal Statistical Agencies face threats from falling response rates, rising costs, increased desire for granularity and timelines