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Secondary data is a critical resource in research, providing a cost-effective and time-efficient means to gather insights and conduct analyses. It involves the use of pre-existing information collected for purposes other than the researcher’s current study.
What is secondary data, and how do you conduct secondary data analysis? We outline the key steps in this easy-to-follow guide.
Secondary data analysis is the analysis of data that was collected by someone else. Below, we’ll review the definition of secondary data, how it can be used by researchers, and the pros and cons of this type of research.
Secondary data analysis is a cost-effective, accessible, and efficient means of utilizing existing data to answer new research questions. The manuscript provides an overview of the secondary data analysis process, as well as benefits and limitations inherent in the research method.
Secondary analysis of qualitative data is a topic unto itself and is not discussed in this volume. The interested reader is referred to references such as James and Sorenson (2000) and Heaton (2004). The choice of primary or secondary data need not be an either/or ques-tion.
Secondary data analysis refers to the analysis of existing data collected by others. Secondary analysis affords researchers the opportunity to investigate research questions using large-scale data sets that are often inclusive of under-represented groups, while saving time and resources.
Secondary analysis is any form of research that relies on or uses previously conducted research for the purposes of a new study. If existing data is cited or previously conducted studies help to achieve a new outcome, it’s secondary analysis.
Secondary research is a very common research method, used in lieu of collecting your own primary data. It is often used in research designs or as a way to start your research process if you plan to conduct primary research later on.
Researchers conducting secondary data analysis commonly analyse data from the same dataset multiple times throughout their careers. However, prior knowledge of the data increases risk of bias, as prior expectations about findings could motivate researchers to pursue certain analyses or questions.
Secondary data analysis is now firmly embedded in nursing science, helping researchers to uncover new insights that can improve nursing education, patient care, health service delivery, public health, and health policy.