Contrary to the common view that secondary data analysis is easy or quick, the process makes use of the same methodologies as primary research and depends on existing data that may not be perfect for your research question. To make an informed decision about using the data, you might consider:
Points to consider:
(Jacobson, A., Hamilton, P., & Galloway, J. (1993). Obtaining and evaluating data sets for secondary analysis in nursing research. Western Journal Of Nursing Research, 15(4), 483-494.)
A thorough understanding of the data is critical to your success. Documentation is your key. It may include codebooks or dictionaries, manuals, and any reports resulting from the use of the data set. If such documentation is not available, you should consider developing your own codebook.
Documentation should include information about the variables, their names, labels and definitions. Without the definitions as clarification, the variable names may not match your interpretation of the term. The codebook should indicate the organization of the fields.
Handling of missing data should be part of the codebook. Researchers follow different practices so cells for missing data may have been left blank or may be indicated by a standard designation such as 9, 99, or 999. The researcher may have added an estimated value for the missing data and it is important for you to know what procedure was followed to determine the value. There may additional information on how much data is missing in each of the variables and how much data is missing overall.
Additional components of the codebook include copies of the research instruments, a detailed description of the methodologies used, procedures for data editing and coding as well as information about error rates.
If you anticipate having questions on using the data set for your research questions, it might be an important consideration to have a contact person from the original study available.
Data files may be available in many formats, Access, Excel, SPSS, R, for example. Do you have the necessary software and computing power to read the files? Is any conversion necessary?
Before analysis, data sets must be complete. If any data is missing due to the lack of a response or to incorrect coding, your findings may not be accurate. Your decisions on handling missing data will be influenced by effects on the sample size and distribution, the remaining amount of data for analysis and, ultimately, the impact on your research question.
There are several methods that can be used to deal with missing data. The following list briefly introduces them although you will need more information to apply the methods.
Aponte, J. (2010). Key elements of large survey data sets. Nursing Economic$, 28(1), 27-36.
Vance, D. E. (2012). Troubles and triumphs of secondary data analyses: general guidelines. Research Practitioner, 13(4), 128-135.
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