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Comparing Primary Research and Secondary Research Comparing Primary Research and Secondary Research
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Advantages and Disadvantages of Secondary Data Analysis Advantages and Disadvantages of Secondary Data Analysis
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Steps for Beginning a Secondary Data Analysis Steps for Beginning a Secondary Data Analysis
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Special Issue: Measurement Concerns in Existing Datasets Special Issue: Measurement Concerns in Existing Datasets
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Special Issue: Missing Data in Existing Data Sets Special Issue: Missing Data in Existing Data Sets
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Special Issue: Sample Weighting in Existing Data Sets Special Issue: Sample Weighting in Existing Data Sets
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Conclusion Conclusion
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Author Note Author Note
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Note Note
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References References
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28 Secondary Data Analysis
Get accessDepartment of Psychology, Michigan State University
Richard E. Lucas, Department of Psychology, Michigan State University, East Lansing, MI
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Published:01 October 2013
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Abstract
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. Despite the immense potential for secondary analysis as a tool for researchers in the social sciences, it is not widely used by psychologists and is sometimes met with sharp criticism among those who favor primary research. The goal of this chapter is to summarize the promises and pitfalls associated with secondary data analysis and to highlight the importance of archival resources for advancing psychological science. In addition to describing areas of convergence and divergence between primary and secondary data analysis, we outline basic steps for getting started and finding data sets. We also provide general guidance on issues related to measurement, handling missing data, and the use of survey weights.
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