Pathology

Data analysis

Gene

Data mining

World Wide Web

Exploratory data analysis

Chemistry

Programming language

Alternative medicine

Data science

Toolbox

Data sharing

Open data

Transparency (behavior)

Computer science

Exploratory analysis

Computer security

Biochemistry

Robustness (evolution)

Medicine

Advances in Methods and Practices in Psychological Science

Secondary data analysis, or the analysis of preexisting data, provides a powerful tool for the resourceful psychological scientist. Never has this been more true than now, when technological advances enable both sharing data across labs and continents and mining large sources of preexisting data. However, secondary data analysis is easily overlooked as a key domain for developing new open-science practices or improving analytic methods for robust data analysis. In this article, we provide researchers with the knowledge necessary to incorporate secondary data analysis into their methodological toolbox. We explain that secondary data analysis can be used for either exploratory or confirmatory work, and can be either correlational or experimental, and we highlight the advantages and disadvantages of this type of research. We describe how transparency-enhancing practices can improve and alter interpretations of results from secondary data analysis and discuss approaches that can be used to improve the robustness of reported results. We close by suggesting ways in which scientific subfields and institutions could address and improve the use of secondary data analysis.

Recommendations for Increasing the Transparency of Analysis of Preexisting Data Sets