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Data analyses cannot be performed until data has been cleaned. In fact, many of the errors found in standard data analyses can be traced directly back to “dirty” data. In a perfect world, collected data would be flawless, but as when working with humans in any capacity, errors occur.

To begin the cleaning process, you first need to check collected data for errors, problems, dubious responses, and other issues. Many such checks may be done electronically using statistical software. Once the proper adjustments are made, you can run the analyses. Which analyses techniques you use should align with your hypothesis. In other words, a survey researcher uses his or her hypotheses to drive the data analyses. The hypotheses dictate the “family” of analyses used for the data. The more parsimonious and testable the theory driving the hypotheses, the more straightforward the data analyses will be.

To prepare for this Discussion, consider why data cleaning, including the assessment of missing data, is important. Then think about the role that descriptive statistics plays in data analyses. Finally, consider the relationship between hypothesis(es) and data analyses and how you would illustrate this relationship using at least one of your hypotheses and data analytic strategies from your Final Project as an example.