# Testing for Correlation and Bivariate Regression (SPSS)

For this Assignment, you will examine correlation and bivariate regression testing.

**To prepare for this Assignment:**

- Review this week’s Learning Resources and media program related to regression and correlation.
- Using the SPSS software, open the Afrobarometer dataset or the High School Longitudinal Study dataset (whichever you choose) found in the Learning Resources for this week.
- Based on the dataset you chose, construct a research question that can be answered with a Pearson correlation and bivariate regression.
- Once you perform your correlation and bivariate regression analysis, review Chapter 11 of the Wagner text to understand how to copy and paste your output into your Word document.

**For this Assignment:**

Write a 2- to 3-paragraph analysis of your correlation and bivariate regression 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.

**Note from Professor on how to complete assignment:**

**“**Use only the Afrobarometer (AB) dataset for this assignment.

Do not use the High School Longitudinal Study dataset for this assignment.

Identify an independent variable (IV) and state its Level of Measurement. The IV must be interval or ratio.

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

Write *2* research questions: question 1 is for a Pearson correlation and question 2 is for a bivariate regression. Use these formats:

- What is the relationship between the IV (use the IV name) and the DV (use the DV name)?
- Does the IV predict the DV?

Write a null hypothesis for RQ 1 and a null hypothesis for RQ2. Use these formats:

- (HO1) There is no relationship between the IV and the DV.
- (HO2) The IV does not predict the DV.

Open the AB data set, select *Analyze*, select *Correlation*, select *Bivariate*, drag an interval or ratio DV into the *Variables* box, drag an interval or ratio IV into the *Variables* box, click Continue, click OK.

Review the Sig. value in the *Correlation* output and decide to reject or fail to reject HO1. If you reject the HO1:

1.*Report *the Pearson correlation, , and explain its meaning in terms of the direction of the relationship positive (direct) or negative (inverse).

*2.**Report *the strength (effect size) of the relationship. For a correlation the strength of the relationship is measured by the Coefficient of Determination = the Pearson correlation squared or

*[Very important note:* if you fail to reject the null hypothesis, return to the data set and identify *2* new interval or ratio variables that are likely to be significantly related and run a new correlation.]

Since your Pearson correlation is significant, run a regression analysis to test HO2:

Select *Analyze*, select *Regression*, select *Linear, *drag the DV to the *Dependent* box, drag the IV to the *Independent(s)* box, click okay.

Examine the Sig. value in the ANOVA output and make a decision to reject or fail to reject HO2. If HO2 is rejected, 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 value directly below the *Constant* value. Write your regression equation in this format:

DV = *Constant* value + IV(coefficient value), but substitute the names of the IV and DV and the actual *Constant* value and coefficient value.”