Conference Agenda

Relevance & Research Question
A large body of interdisciplinary literature has clearly demonstrated the negative effects of lengthy surveys. A shorter questionnaire, by contrast, could reduce the burden on respondents and again contribute to better data quality. Starting from this assumption, we investigate how the use of a modular survey design, in which a self-administered survey is split up into several parts that can be completed at the respondent's convenience, affects participation and module completion rates, order of starting modules and respondents’ evaluation of the questionnaire.

Methods & Data
For this purpose, we embedded a randomized experiment over three survey waves in the German Internet Panel (GIP), a long-standing probability-based online panel of the German population. We split the questionnaires into three to four topic modules, each of which took between three and 15 minutes to complete and randomly divided respondents into five groups, each receiving different information about the topic, length and incentives of the modules. While the respondents in group 1 received the questionnaire as a single survey in the usual way, group 2 was additionally shown the topics of the individual survey modules. Participants in groups 3, 4, and 5 were given an overview of all survey topics and were free to choose the order and timing in which they completed the different survey modules, but were given different additional information about the length and incentives of the survey modules.

Results
Our results indicate that respondents did not complete the survey more often when they could choose the order in which they answer the modules, even when they knew the length and incentive next to the topic of each module. Rather than determining the order in which modules should be answered, respondents showed a clear preference for answering all survey modules in the given order at one time.

Added Value
Our aim is to find out how web-based surveys, especially long-term panel studies, can be optimized in order to increase data quality (i.e. higher completion rates, respondent satisfaction) and - in the long term - to retain respondents in the panel.

Relevance & Research Question
This poster will present the usability and benefits of a new R package for the flexible integration of georeferenced survey data or other social science datasets with customized Earth observation (EO) data derived from public sources like the Copernicus data services.
Methods & Data

The unique feature of the tool is the possibility of carrying out both geographically and temporally medium- to high-resolution queries, which at the same time function efficiently on simple workstations. Our tested workflow development has identified five major levers: parameter type, indicator intensity, focal time period, baseline time period, and spatial buffer. Flexibility on these five attributes will be maximized for users. The tool also offers the functionality to automatically derive spatio-temporal links with other georeferenced data (e.g., surveys, digital behavioral data). Users benefit from the core variables integrated into the interface for social research. Examples include data on local air quality and pollutants, extreme weather events, or land use changes.

Results
The workflow will be exemplified on the basis of a research project which we currently conduct on the effects of flooding exposure on climate change opinion.
Added Value
The project advances the automatization of these data integration processes between social science data and EO data based on an open-source, user-friendly tool.

Research Question

Many online surveys offer incentives to enhance response rates, betting on stronger motivation for a response once respondents' participation costs are rewarded. Commonly, respondents receive incentives such as cash or vouchers. Additionally, online panel surveys may include a points-based incentive program allowing respondents to accumulate reward points throughout the study and redeem them anytime to get a shopping voucher. We would like to address the following research questions: What are the distinct patterns of reward point redemption among online survey participants, and how can these be categorized into behavioural clusters? How do demographic and socioeconomic factors influence reward point redemption behaviours?

Methods

In 2023, the Institute for Employment Research in Germany launched a new online panel survey of the German workforce (IAB-OPAL) using a push-to-web approach. The quarterly survey utilises a post-paid points-based incentive program, allowing respondents to earn reward points in their accounts after completing the survey. They can collect these points over time and redeem them for shopping vouchers from various providers at their convenience.

We comprehensively assess respondents' redemption behaviours of across five survey waves using individual tracking data on inflows and outflows of reward points of 13.513 panelists. First, we analyse recurring redemption patterns and identify distinct behavioural clusters by applying time series k-means. Second, we explore other dimensions of redemption behaviour, such as the timing of point redemption across different demographic groups and specific temporal trends. Lastly, we investigate the demographic and socioeconomic drivers of redemption behaviours, giving special attention to the respondents who collect reward points without redeeming them.

Results

The analysis reveales several key insights into reward point redemption behaviours within the IAB-OPAL panel survey. Respondents exhibited a wide range of behaviours, from frequent small redemptions to rare but large-point redemptions. Through clustering methods, distinct behavioural groups were identified.

Added Value

Our findings shed light on the dynamics of reward point redemption in online panels and have practical implications. We provide valuable guidance for designing online panel surveys that may incorporate a points-based incentive program. Moreover, our results can assist survey practitioners in budget planning, decision-making, and fieldwork preparation.