Cybercrime has become a major issue in digitalized societies. Addressing the rising amount of cybercrime necessitates high-quality research, beginning with examining its prevalence and trends. Examining the prevalence and trends of cybercrime requires a methodological approach that tackles the typical data quality issues of (cyber)crime data, such as validity. A primary problem is that different types of data (e.g., administrative process-generated data and survey data) do not show the actual number of crimes committed, leading to a large darkfield (dark figures). In order to tackle the methodological issue of the darkfield and concomitant validity problems, this article builds on prior research on administrative data and survey data as well as on a general background regarding underreporting issues in crime research. For instance, discussing the role of social desirability and trust in surveys. It then draws on the previous methodological research on crime data, generally, and on cybercrime data, specifically, to suggest an integrated “mixed-data” approach in which different data types (such as administrative data and survey data) are analyzed comparatively in order to gain more information on crime prevalence and trends. Embedded in the previous research field, it proposes this procedure in form of the “Data Combination Approach” (DCA). This approach is described and discussed, including potentials and methods of analyses as well as challenges, particularly regarding the differences between data types. In doing so, this article provides a solid foundation for future high-quality research on crime (particularly cybercrime) prevalence and trends.
