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A05: Mixing the Modes
Online, Face-to-Face or Mixed-Mode? Findings from a Methodological Experiment in the GGP Context
1Federal Institute of Population Reserach (BiB), Germany; 2Netherlands Interdisciplinary Demographic Institute (NIDI), Netherlands; 3Utrecht University, Netherlands
Relevance & Research Question: For a large panel study, the Generations and Gender Survey, that in the past has been conducted in CAPI mode only, this experiment investigates the chances and risks of partly moving online for the next round of data-collection. It tests a sequential mixed-mode (push-to-web) design, combining CAWI and CAPI modes. The particular circumstances to consider are a rather long questionnaire of approximately 60 minutes, a complex routing and a focus on family-demographic issues. We are comparing response rates, costs, representativity, accuracy of measurement, and further aspects of data-quality.
Methods & Data: The experiment has been carried out 2018 in three GGS countries – Germany, Croatia, and Portugal – with more than 1,000 respondents in each country. (Fieldwork is on-going on the day of submission.) In all three countries a reference group was interviewed in CAPI mode, while an experimental group was interviewed in a sequential mixed-mode design (CAWI and CAPI). In each country a further specific experiment was carried out: In Germany strategies of incentivation, in Croatia the timing of reminders, and in Portugal two modes of selecting a contact person within the household were compared.
Results: Since fieldwork is on-going only preliminary results are known so far: Regarding response rates and accurate measurement, the online mode works quite well, despite the interview length. This is particularly true with a generous incentivation strategy and closely timed reminders. However, break-off rates are high. And the consent to store contact information for a re-contact is clearly lower than in CAPI mode. The country context makes a strong difference. Largest problems were identified in Portugal, starting already with low contact rates. In the sequential mixed-mode, the CAPI follow-up has low response rates and tends to proceed slowly. It seems that fieldwork institutes are underestimating the number of interviewers needed.
Added Value: The experiment identifies recommendable design characteristics for a push-to-web design for the GGS, as well as for comparable panel studies. It provides comparisons of various aspects of data-quality between CAWI, CAPI and sequential mixed-mode. And it provides evidence for the importance of the country-context in that respect.
Design and Implementation of a Mixed Mode Time Use Diary in the Age 14 Survey of the Millennium Cohort Study
University College London, United Kingdom
Relevance & Research Question: Time diary data provide a comprehensive and sequential account of daily life and are used for a wide range of analytic purposes. Recent years have witnessed a steady growth of large-scale time diary data collection in cross-sectional as well as longitudinal surveys, driven by the increased research interest in population activity patterns and their relationship with long-term outcomes. The majority of social surveys collect paper-administered diaries, which have been shown to produce the most accurate and reliable daily activity estimates, but present challenges relating to respondent burden and administration costs. The use of new technologies for data collection could address these weaknesses by providing less burdensome diary instruments, improving data quality, and simplifying post-fieldwork data coding costs.
Methods & Data: The Millennium Cohort Study (MCS) was the first large-scale longitudinal survey to use a mixed-mode approach for the collection of time use data among teenagers. A smartphone app, web diary, and paper diary were specifically designed for the sixth wave of the survey, when cohort members were aged 14. The smartphone app in particular was a departure from the more traditional methods of time use data collection. This presentation will focus on the development of the time-use instruments, including their design, development and implementation in the field, as well as take-up and selection into different time diary modes, data quality across the instruments, and mode differences in measurement.
Results: The app proved the most popular choice among 14 year olds, with 64% choosing to use it over the other instruments. Those who completed the app diary were more likely to be female, high users of social media, and those whose families had higher incomes. In terms of data quality, the app and web diary proved comparable, and both outperformed the paper diary. Differences in measurement, specifically when looking at reported time spent doing different activities, was seen across the three modes, with the app and web being more similar than the paper diary.
Added Value: The experiences of MCS provide practitioners with useful evidence for designing high-quality instruments to collect time use data online.
Understanding mode switching and non-response patterns
University of Manchester, United Kingdom
The shift from single mode designs to mixed mode, where a combination of interview modes are used to collect data, is one of the key challenges of contemporary longitudinal studies. The decision about what modes to use and how to implement them can have a big impact, influencing costs, field-work procedures, non-response and measurement error.
The research proposed here aims to better understand one of the key characteristics of a mixed mode design: how people transition from one mode to another in time. This is essential for a number of reasons. Firstly, it can inform targeting strategies. For example, it can be used to target those people that are more likely to shift from a cheap mode to a more expensive one. It can also be used in models for dealing with non-response after data collection, such as weighting models. Thirdly, it can be used to explain measurement error that appears due to the mode design.
This paper will investigate the process of changing modes by looking at waves 5-10 of the UKHLS Innovation Panel where a sequential Web-Face to face design was used. Latent class analysis will be used to find the underlying patterns of change in time of modes. The clusters found will be used both as dependent variables, to understand who are the types of respondents in each, and as independent variables, to predict future wave non-response and mode selection. Findings will inform the design and use of the main UKHLS study.
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