Birkenmaier, L., Daikeler, J., Fröhling, L., Gummer, T., Lechner, C. M., Lux, V., Schwalbach, J., Silber, H., Weiß, B., Weller, K., Wolf, C., Abel, D., Breuer, J., Dietze, S., Dimitrov, D., Döring, H., Hebel, A., Hochman, O., Jünger, S., Katsanidou, A., Kohne, J., Kunz, T., Mangold, F., Mathiak, B., Piepenburg, J. G., Pollak, R., Quandt, M., Rammstedt, B., Roßmann, J., Schellhammer, Stroppe, A.-K., Soldner, F., S., Stier, S., Wagner, C., Watteler, O., Weiß, J., Zapilko, B., Ziaja, S. (2024): Defining and Evaluating Data Quality for the Social Sciences. (GESIS Papers, 2024|06). GESIS – Leibniz Institute for the Social Sciences, https://doi.org/10.21241/ssoar.96764
The authors have identified the need for a commonly shared understanding of data quality for social science data.
While existing frameworks offer valuable guidance for assessing data quality, they tend to concentrate on specific dimensions or data types. The authors contend that while these frameworks are crucial, a more comprehensive perspective on data quality is needed to fully capture the inherent multidimensional nature of quality aspects in social science data.
Hence, this position paper provides a unfied framework for assessing data quality dimensions of social science data.