Part 2

To prepare for this Part 2 of your Assignment:

• Review Warner’s Chapter 12 and Chapter 2 of the Wagner course text and the media program found in this week’s Learning Resources and consider the use of dummy variables.
• Using the SPSS software, open the Afrobarometer dataset or the High School Longitudinal Study dataset (whichever you choose) found in this week’s Learning Resources.
• Consider the following:
  • Create a research question with metric variables and one variable that requires dummy coding. Estimate the model and report results. Note: You are expected to perform regression diagnostics and report that as well.
• Once you perform your analysis, review Chapter 11 of the Wagner text to understand how to copy and paste your output into your Word document.

For this Part 2 Assignment:

Write a 2- to 3-page analysis of your multiple regression using dummy variables results for each research question. In your analysis, display the data for the output. Based on your results, provide an explanation of what the implications of social change might be.

Use proper APA format, citations, and referencing for your analysis, research question, and display of output.

Professor Note: How To Complete The Part 2 Requirement:

“Review the Week 10 Course Materials

Use only the General Social Survey (GSS) dataset for this discussion

Identify 2 independent variables (IV1 and IV2) and their Level of Measurement.

IV1 can be interval, ratio, nominal, or ordinal.

IV2 will be the dummy variable and must be nominal. Do not choose an ordinal variable.

Identify a 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 IV2 (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.

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

What is the relationship between IV1 and IV2 (state the IVs) and the DV (state the DV)?

Write the null hypothesis. Use this format:

There is no relationship among IV1 and IV2 and the DV.

State the research design.

Use SPSS to answer the research question. Let’s start by creating the dummy variable. Here’s how:

Open the GSS data set, select Transform, select Recode Into Different Variables, Select a nominal variable to recode into a dummy variable (as an example, we will useMarital Status) and drag it into the Input Variable > Output Variable box, enter Married in both the Name and Label boxes, click Change, click Old and New Values, enter 1 in the Value box (Why? Because 1 is the value assigned to Married in the Values column of the GSS data set), enter 1 in the Value box in the New Value box, click Add, checkAll Other Values in the New Value box, enter 0 in the Value box in the New Value box, click Add, click Continue, click OK.

You have now created a new (dummy) variable, Married, with 2 levels, married and all others (widowed, divorced, separated, never married, and n/a.

Scroll to the very bottom of the GSS dataset and your new (dummy) variable, Married, will appear in the last row. Be sure to use your dummy variable in your regression. Use Married if you wish to.

Now run your multiple regression by following the same steps (with some additions) that you used in the Week 9 Discussion:

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, click Statistics,check Estimate, check Model Fit, check Collinearity diagnostics and click Continue. Click Plots, drag *ZRESID into the Y box, drag *ZPRED into the X box, check Normal Probability plot and click Continue

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

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.

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

You must discuss assumptions for multiple regression. These assumptions are: normality, linearity, homoscedasticity and multi-collinearity:

Identify each assumption, decide if the assumption is met or not met, and explain the rational for your decision. Use the SPSS Output for Estimate, Model Fit, Collinearity diagnostics and Plots to support your decision.

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.

Interpret the coefficients for the model, specifically commenting on the dummy variable.”

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