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 [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.