This repository serves as a demo repository for the Evidence Gap Map described below. The interactive Evidence Gap Map itself may be accessed via this link: https://frohleon.github.io/egm/.

About this interactive evidence gap map

This interactive evidence gap map was created by the GESIS Data Quality group for the systematic literature review on data quality frameworks for social science research data, titled Assessing Data Quality in the Age of Digital Social Research: A Systematic Review, published in a special issue of the Social Science Computer Review.

This interactive EGM was created using EPPI-Mapper. In particular, it visualizes the results of the second research objective from the systematic review, detailed below.

Associated Article:

Daikeler, J., Fröhling, L., Sen, I., Birkenmaier, L., Gummer, T., Schwalbach, J., Silber, H., Weiß, B., Weller, K., & Lechner, C. (2024). Assessing Data Quality in the Age of Digital Social Research: A Systematic Review. Social Science Computer Review, 0(0). https://doi.org/10.1177/08944393241245395

Abstract: While survey data has long been the focus of quantitative social science analyses, observational and content data, although long-established, are gaining renewed attention; especially when this type of data is obtained by and for observing digital content and behavior. Today, digital technologies allow social scientists to track “everyday behavior” and to extract opinions from public discussions on online platforms (e.g., Twitter content). These new types of digital traces of human behavior, together with computational methods for analyzing them, have opened new avenues for analyzing, understanding, and addressing social science research questions. However, even the most innovative and extensive amounts of data are hollow if they are not of high quality. But what does data quality mean for modern social science data? To investigate this rather abstract question the present study focuses on four objectives. First, we provide researchers with a decision tree to identify which data quality framework to use based on the specific needs of the researchers’ use case. Second, we determine which social science data types and quality dimensions are already addressed in the existing data quality frameworks. Third, we identify gaps with respect to different data types and data quality dimensions within the existing frameworks which need to be filled. And fourth, we provide a detailed literature overview for the intrinsic and extrinsic perspectives on data quality. By conducting a systematic literature review based on text mining methods, we identified and reviewed 58 data quality frameworks. In our decision tree, the three categories: data type, the perspective it takes, and its level of granularity help researchers to find appropriate data quality frameworks. We, furthermore, discovered gaps in the available frameworks with respect to visual and especially linked data and point out in our review that even famous frameworks might miss important aspects. The article ends with a critical discussion of current the state of the literature and potential future research avenues.

Keywords: Data Quality, Data Quality Concepts, Data Quality Frameworks, Measurement, Representation, Systematic Review

Research Objectives: To guide researchers in questions on data quality and to facilitate and incentivize interdisciplinary exchange on data quality criteria, we provide a comprehensive and systematic review of existing data quality frameworks which target on quantitative social science data. We limit this review to quantitative data as of reasons of resources. In doing so, we will focus on our four objectives:

We will provide researchers with a decision tree to identify appropriate data quality frameworks for a given use case. We will determine which social science data types and quality dimensions are already addressed in the existing frameworks. Considering different data types, we will identify gaps that are not yet covered by existing quality frameworks, and that should be addressed by future research. We will provide a detailed literature overview for the intrinsic as well as the extrinsic perspective on data quality.