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About this Whitepaper

About this Whitepaper 

There is growing pressure within the scientific community to more fully repurpose existing data. However, the challenges of working with secondary data means researchers must be strategic in their approach. Our subject matter expert discusses the advantages, limitations, best practices, and analytical tools available for conducting secondary data analysis.

 

 

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Opportunities for Research

Learn the 4 more popular reasons secondary analysis is used by research teams.

 

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Successful Results

Read about the 7 characteristics that all successful secondary analysis projects share.

 

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Effective Tools

Review additional resources and examples of tools used for secondary analysis.

 

"The central appeal of secondary analyses is the ability to glean more meaningful information from a single dataset: with existing data, more causal relationships can be considered, research topics can be approached from different perspectives, and novel analytical models and methods can be piloted—all without additional experimental resources."

 

"With the advantages of secondary data analysis come a distinct set of challenges. Intuitively, these stem from the fact that the data are static: treatment and other variables cannot be retroactively manipulated, additional information cannot be collected, nor can the sample population be altered or expanded."