Systematic Encoding and Interpretation of Civic Engagement (SEIZE)

Background and Objective

More than one third of the population in Germany is engaged in voluntary work – for example in sports clubs, with volunteer fire brigades, in animal welfare or in social initiatives. Despite this broad participation, there is still a lack of detailed empirical knowledge about the specific tasks that volunteers actually perform and how these activities are distributed across different population groups and types of regions.

The SEIZE research project addresses this knowledge gap and aims to create a data basis that enables a differentiated analysis of civil society engagement in Germany. At its core is the systematic data preparation of more than 60,000 open-ended responses from the 2019 German Survey on Volunteering. Through a multimethod approach, a dataset is generated that facilitates easy use of these data for quantitative analyses. This target dataset allows users to gain new insights into the diversity, scope and spatial distribution of civil society activities in rural and non-rural areas.

Approach

Data preparation in the SEIZE project follows a multi-stage approach that systematically integrates qualitative and quantitative elements. In the first step, the scientific state of research on methodological approaches to capturing voluntary engagement is reviewed, with a particular focus on identifying conceptual and methodological challenges. Special attention is given to studies that address the conceptualization, measurement, and operationalization of civil society engagement.

A central element of the project is the systematic preparation of previously unused open-ended responses from the 2019 German Survey on Volunteering. These open responses represent a valuable data source, as they provide insights into the diversity and specificity of voluntary activities that are often not fully captured in standardized response categories.

The analysis is based on a methodologically sophisticated procedure for systematic content analysis: the open-ended responses are classified and coded using an inductively developed coding scheme. This involves a multi-step process combining manual and algorithmically supported procedures. First, the text data are carefully reviewed and systematically tagged. A classification algorithm is then developed that uses machine learning methods to enable automated and consistent coding of large amounts of data. The validity of the automated coding is ensured through manual checks. This methodological design not only allows for an efficient and detailed analysis of the survey’s free-text responses but is also transferable to earlier (e.g., 2014) and future surveys (e.g., 2024) – a key prerequisite for conducting time series analyses and tracking long-term developments in volunteering.

Following the qualitative coding, the classified data are prepared for future quantitative analyses.