Item Nonresponse and Unit Nonresponse in Panel Studies |
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| Coordinator 1 | Dr Uta Landrock (LIfBi – Leibniz Institute for Educational Trajectories) |
| Coordinator 2 | Dr Ariane Würbach (LIfBi – Leibniz Institute for Educational Trajectories) |
| Coordinator 3 | Mr Michael Bergrab (LIfBi – Leibniz Institute for Educational Trajectories) |
Panel studies face various challenges, starting with establishing a panel, ensuring panel stability, minimizing sample selectivity and, overall, achieving high data quality. All these challenges are compromised by issues of nonresponse. Unit nonresponse may lead to small sample sizes, particularly if it occurs in the initial wave. It also may lead to panel attrition: Besides active withdrawals, respondents might also drop out for administrative reasons, for example, if (recurrent) non-respondents are excluded from the sample. Item nonresponse implies reduced data quality, since it decreases the statistical power for analyses based on the variables in question when respondents with missing information are excluded from analyses. It may, in extreme cases, lead to variables needing to be excluded from analyses due to their high proportion of missing values. Both, unit nonresponse and item nonresponse may introduce biases, either by increasing sample selectivity or by affecting the distribution of certain variables.
New and alternative data sources may shed new light on the issue of nonresponse, given that it is not yet entirely clear how these developments will affect longitudinal data collection.
We invite researchers to participate in this discussion, which may – among many others – include the following topics:
- Quantifying item and unit nonresponse, including resulting selectivity,
- Measuring the development of item and unit nonresponse across panel waves,
- Implications of item and unit nonresponse on data quality,
- Strategies for reducing item and unit nonresponse, e.g. by developing new question or response formats, introducing tailored incentive schemes, or offering different modes,
- Problems related to such measures, e.g., comparability across panel waves,
- Handling item and unit nonresponse, for example, by imputing missing values or weighting,
- Using contact and paradata to avoid item and unit nonresponse by monitoring fieldwork during data collection.