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Analyzing Your Primary Data

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Now that you've collected your primary data, it's time to figure out what that data means and what you can learn from it. The keys to analyzing your data are to pull out information that is the most pertinent to your writing, information you can highlight and discuss, and information that will support your claims (if you are making any).


Interviews are fairly easy to analyze, as you simply have to go back through the answers you received and decide how to use them within your writing. You can group the answers into categories and create a chart of how those answers may best fit within your paper or article.

If you recorded the interview with a tape or digital recorder, you may want to listen to it and type a transcript of the interview. Since transcription is a tedious process, only use this option if you need to.


When analyzing surveys, you want to get the raw data into a form that you can manipulate. If you were using a numerical system or yes/no answer system for your survey, you may find it helpful to enter the results into a spreadsheet program such as Microsoft Excel. If the survey was an open-ended question style, see if you can fit your answers into categories of responses.


Observations are more difficult to analyze because when you are taking notes, you often write down everything that you see. Start by organizing your notes into categories or by some criteria. Once you have everything organized, see if you can make some generalizations about what you have observed.

Over-generalizing your results

Your first attempts at primary research will most likely include small groups of people and may not be representative of the population as a whole. It is important to remember not to over-generalize your findings--in other words, don't assume that your findings are necessarily true of every person within the group or every person in a society.

Triangulation of Data

One of the benefits of combining primary research with secondary research is data triangulation. Data triangulation is when a piece of data, a finding, or a generalization is able to be verified with several different research methods. This helps add to your credibility and makes your findings stronger.

For example, you are studying binge drinking on campus. You find national averages that indicate that 45% of college students binge-drink nationwide. You conduct your own research at the Purdue campus. You find that 47% of the individuals you surveyed drink; you also interview a counselor on campus who reports that approximately 1/3 of the students they see suffer from a binge-drinking problem. Thus, your results from an interview with an expert and your own survey support the national averages.