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Transitioning to a Mixed-Mode Study Design in a National Household Panel Study: Effects on Fieldwork Outcomes, Sample
Katherine A McGonagle1, Narayan Sastry1
1University of Michigan, Institute for Social Research, Survey Research Center.
The U.S. Panel Study of Income Dynamics (PSID) successfully transitioned to a web-first mixed-mode data collection, achieving high response rates with reduced effort and cost. This new approach enhances efficiency in household panel studies.
Area of Science:
- Survey Methodology
- Social Science Research Methods
- Data Collection Techniques
Background:
- The U.S. Panel Study of Income Dynamics (PSID) historically used computer-assisted telephone interviewing (CATI).
- A transition to a web-first mixed-mode design (web and CATI) was planned for 2021.
- Evaluating the impact of data collection mode on fieldwork outcomes is crucial for survey efficiency.
Purpose of the Study:
- To compare the fieldwork outcomes of two sequential mixed-mode protocols: web-first versus CATI-first.
- To assess the effects of the new mixed-mode design on response rates, contact attempts, duration, and cost.
- To compare current mixed-mode results with historical data from a traditional telephone design.
Main Methods:
- An experimental design randomized sample families into two protocols: web-first and CATI-first.
- The web-first protocol prioritized online interviews, followed by telephone.
- The CATI-first protocol prioritized telephone interviews, with a later option for web completion.
Main Results:
- The web-first design yielded comparably high response rates to the CATI-first design.
- Web-first resulted in faster interview completion, lower effort, and reduced costs.
- Compared to the previous wave, the mixed-mode design generally reduced effort and showed similar non-response patterns across subgroups.
Conclusions:
- The web-first mixed-mode approach is effective for household panel studies, demonstrating efficiency gains.
- This study provides empirical evidence supporting the use of mixed-mode designs in large-scale surveys.
- The findings contribute valuable insights into optimizing data collection strategies and reducing costs in longitudinal studies.
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