Discussion: Performing Data Analysis

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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.

With these thoughts in mind:

Part 1

Post an explanation of the
importance of data cleaning, including assessment of missing data.
Provide one example of data cleaning and the potential impact it might
have on data analyses. Then explain the importance of descriptive
statistics in data analyses. Finally, explain the relationship between
hypothesis(es) and data analyses using at least one of your hypotheses
and data analytic strategies from your Final Project as an example.

Be sure to support your postings and responses with specific references to the Learning Resources.

Read a selection of your colleagues’ postings.

Part 2

Respond to at least two of your colleagues’ postings in one or more of the following ways:

• Ask a probing question.
• Share an insight from having read your colleagues’ postings.
• Offer and support an opinion.
• Validate an idea with your own experience.
• Make a suggestion.
• Expand on your colleagues’ postings.

Return to this Discussion in a few days
to read the responses to your initial posting. Note what you learned
and/or any insights you gained as a result of your colleagues’ comments.