An information quality (InfoQ) framework for ex-ante and ex-post evaluation of empirical studies

Abstract

Numbers are not data and data analysis does not necessarily produce information and knowledge. Statistics, data mining, and artificial intelligence are disciplines focused on extracting knowledge from data. They provide tools for testing hypotheses, predicting new observations, quantifying population effects, and summarizing data efficiently. In these fields, measurable data is used to derive knowledge. However, a clean, exact and complete dataset, which is analyzed professionally, might contain no useful information for the problem under investigation. The term Information Quality (InfoQ) was coined by Ref. [15] as the potential of a dataset to achieve a specific (scientific or practical) goal using a given data analysis method. InfoQ is a function of goal, data, data analysis, and utility. Eight dimensions that relate to these components help assess InfoQ: Data Resolution, Data Structure, Data Integration, Temporal Relevance, Generalizability, Chronology of Data and Goal, Construct Operationalization, and Communication. The eight dimensions can be used for developing streamlined evaluation metrics of InfoQ. We describe two studies where InfoQ was integrated into research methods courses, guiding students in evaluating InfoQ of prospective and retrospective studies. The results and feedback indicate the importance and usefulness of InfoQ and its eight dimensions for evaluating empirical studies.