Discussion: Correlation and Bivariate Regression

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Discussion: Correlation and Bivariate Regression

To prepare for this Discussion:

  • Review this week’s Learning Resources and media program related to regression and correlation.
  • Review Magnusson’s web blog found in the Learning Resources to further your visualization and understanding of correlations between two variables.
  • Construct a research question using the General Social Survey dataset, which can be answered by a Pearson correlation and bivariate regression.

By Day 3

Use SPSS to answer the research question. Post your response to the following:

  1. What is your research question?
  2. What is the null hypothesis for your question?
  3. What research design would align with this question?
  4. What dependent variable was used and how is it measured?
  5. What independent variable is used and how is it measured?
  6. If you found significance, what is the strength of the effect?
  7. Explain your results for a lay audience; explain the answer to your research question.

Specific Resources

Magnusson, K. (n.d.). Welcome to Kristoffer Magnusson’s blog about R, Statistics, Psychology, Open Science, Data Visualization [blog]. Retrieved from http://rpsychologist.com/index.html

As you review this web blog, select New d3.js visualization: Interpreting Correlations link, once you select the link, follow the instructions to view the interactive for interpreting correlations. This interactive will help you to visualize and understand correlations between two variables.

Note: This is Kristoffer Magnusson’s personal blog and his views may not necessarily reflect the views of Walden University faculty.

Extra resources

Discrete Data. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 62-67). Thousand Oaks, CA: SAGE Publications, Inc.

Fox, J. (1991). Quantitative Applications in the Social Sciences: Regression diagnostics Thousand Oaks, CA: SAGE Publications Ltd doi: 10.4135/9781412985604

Non-Normally Distributed Errors. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 41-49). Thousand Oaks, CA: SAGE Publications, Inc.

Nonconstant Error Variance. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 49-54). Thousand Oaks, CA: SAGE Publications, Inc.

Nonlinearity. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 54-62). Thousand Oaks, CA: SAGE Publications, Inc.

Outlying and Influential Data. (1991). In J. Fox (Ed.), Regression Diagnostics. (pp. 22-41). Thousand Oaks, CA: SAGE Publications, Inc.

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