PSYC 8202 Walden University Performing Data Analysis Discussion

PSYC 8202 Walden University Performing Data Analysis Discussion

PSYC 8202 Walden University Performing Data Analysis Discussion

Discussion: Performing Data Analysis

undefinedData 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.

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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.

 

UNFORMATTED ATTACHMENT PREVIEW

Learning Resources Required Readings DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.). Thousand Oaks, CA: Sage. • • Chapter 6, “Factor Analysis” Chapter 7, “An Overview of Item Response Theory” Survey Methodology • Chapter 10, “Postcollection Processing of Survey Data” Kreuter, F., Presser, S., & Tourangeau, R. (2008). Social desirability bias in CATI, IVR, and web surveys: The effects of mode and question sensitivity. Public Opinion Quarterly, 72, 847–865. Krosnick, J. A., et al. (2002). The impact of ‘no opinion’ response options on data quality: Nonattitude reduction or invitation to satisfice? Public Opinion Quarterly, 66(3), 371–403. Schaeffer, E. M., Krosnick, J. A., Langer, G. E., & Merkle, D. M. (2005). Comparing the quality of data obtained by minimally balanced and fully balanced attitude