What Are the Best Tips for Analyzing Survey Data?
The best tips for analyzing survey data depend on several factors, including the question format, the number of questions and the reason for conducting the survey. A typical review of survey data includes quantitative and qualitative analysis. Depending on the different types of questions, there might also be single verbatim responses that speak to the opinions of a significant percentage of responders.
Compiling quantitative information is one of the most straightforward aspects of analyzing survey data. It is essentially based on statistics and generating percentages. This type of analysis can be done when the answer choices are yes, no or multiple choice. After the answers are tallied, the data can be used to find patterns, trends and other common characteristics.
Quantitative information can be used when a researcher wants to establish popular opinion. It cannot offer in-depth analysis of a subject, but the results might be revealing nevertheless. The key factor for analyzing quantitative data well is that the results be tallied and reported accurately. Although there might be differing opinions about how the resulting statistics came to be, the answers are uniform.
Compiling qualitative data requires more interpretation. It is one of the most complex aspects of analyzing survey data. Qualitative data typically comes from interviews, surveys that ask for more than multiple choice answers and case studies. There often are patterns found in this kind of data, but they might not necessarily present themselves. For this reason, a qualitative survey might provide results that are wildly different from what was expected.
When analyzing qualitative survey data, it can be helpful to look for common themes. After these have been determined, the answers related to these themes can be grouped together. Then the information in each group can be analyzed for similarities, differences and other significant factors.
There can be other aspects of qualitative data that are difficult to categorize. This might include opinions that dissent from the majority and unique situations that stand out from the other results. Although this information can be difficult to integrate into the data overall, it can be useful as a basis for comparison. A dissenting factor also can be used to demonstrate the range of opinions, events or other elements in the data set.
Verbatim responses also can be useful when analyzing survey data. A well-spoken phrase can help a researcher understand patterns of quantitative data. It might also give further meaning to qualitative results. An individual response can help sum up all or a portion of the results as well, which can be useful when presenting survey findings.
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