What is meant by 'secondary data analysis' in research?

Secondary data analysis refers to the practice of using existing data that has already been collected for purposes different from the original intent. In research, this approach allows researchers to derive insights, test hypotheses, or answer new research questions without the burden of collecting new data. This is particularly beneficial as it often saves time and resources, enabling access to larger datasets that may have been compiled over many years or across diverse populations.

The focus in secondary data analysis is on reinterpreting or reanalyzing data collected by others, which can include survey data, academic research findings, previous experiments, or administrative records. This method is highly efficient, especially in fields where obtaining primary data can be challenging or expensive.

In contrast, collecting new data from participants involves designing and implementing a study to gather firsthand information, while performing qualitative interviews centers on gathering detailed personal narratives and perspectives. Conducting a literature review entails summarizing and synthesizing existing research, which is a different process that does not specifically involve reusing collected data for new analysis. Thus, using existing data for a new purpose captures the essence of secondary data analysis accurately.