Discussion: Multiple Regression

Please follow the attachment closely.

This Discussion assists in solidifying your understanding of statistical testing by engaging in some data analysis. This week you will work with a real, secondary dataset to construct a research question, estimate a multiple regression model, and interpret the results.

Whether in a scholarly or practitioner setting, good research and data analysis should have the benefit of peer feedback. For this Discussion, you will post your response to the hypothesis test, along with the results. Be sure and remember that the goal is to obtain constructive feedback to improve the research and its interpretation, so please view this as an opportunity to learn from one another.

To prepare for this Discussion:

  • Review this week’s Learning Resources and media program related to multiple regression.
  • Create a research question using the Afrobarometer Dataset or the HS Long Survey Dataset, that can be answered by multiple regression.

By Day 3

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

  1. If you are using the Afrobarometer Dataset, report the mean of Q1 (Age). If you are using the HS Long Survey Dataset, report the mean of X1SES.
  2. What is your research question?
  3. What is the null hypothesis for your question?
  4. What research design would align with this question?
  5. What dependent variable was used and how is it measured?
  6. What independent variables are used and how are they measured? What is the justification for including these predictor variables?
  7. If you found significance, what is the strength of the effect?
  8. Explain your results for a lay audience, explain what the answer to your research question.

Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.

Required Readings

Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.
Chapter 12, “Regression and Correlation” (pp. 401-457) (previously read in Week 8)

Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
Chapter 8, “Correlation and Regression Analysis”
Chapter 11, “Editing Output” (previously read in Week 2, 3, 4, 5. 6, 7, and 8)

https://academicguides.waldenu.edu/rsch8210

6210 Week 9 Discussion: How To Complete The Discussion Requirement

Review the Week 9 Course Materials

Use the data set dataset for this Discussion

Be sure to submit your SPSS Output to the Discussion Board along with your analysis.

Identify 2 independent variables (IV1 and IV2) and their Level of Measurement. The IV1 and IV2 can be interval, ratio, nominal, or ordinal.

Identify the dependent variable (DV) and its Level of Measurement. The DV must be interval or ratio.

Write a research question for multiple regression. Use this format:

What is the relationship between IV1 and IV2s (state the IVs) and the DV (state the DV)?

Write the null hypothesis. Use this format:

-There is no relationship between IV1 and IV2 and the DV.

State the research design.

Use SPSS to answer the research question. Here’s how:

1. Open the GSS data set, select Analyze, select Regression, Select Linear, drag IV1 and IV2 into the Independent(s) box and the DV into the Dependent box, and click OK.

2. Review the Sig. value in the SPSS Output under ANOVA and decide to reject or fail to reject the null hypothesis.

3. If you reject the null and determine that the ANOVA is statistically significant, report and explain the effect size. Effect size is found under R square in the Model Summary.

4. If you fail to reject the null hypothesis, select new variables and repeat 1-3 steps above.

Write the regression equation. Here’s how:

Examine the Coefficients output and identify the Constant value under Unstandardized Coefficients in column B and the coefficient values for IV1 and IV2 directly below the Constant value. Write your regression equation in this format:

DV = Constant value + IV1(coefficient value) + IV2(coefficient value), but substitute the names of IV1, IV2 and the DV and the actual Constant value and coefficient value for IV1 and IV2. Here’s an example:

If Socioeconomic Status is the DV, Age is IV1 and Highest Degree is IV2, then your equation could look like this:

Socioeconomic status = 15 + (.114)(Age) + (11.3)(Highest Degree)

Do not use these numbers. Do your own calculations.

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