This guide walks you through conducting a multiple regression analysis. Utilizing Astin's I-E-O model as a framework, you will interpret the results to provide a write-up of analysis and findings. 

Learning Objective: 

conduct a multiple regression analysis, assess and analyze collinearity, and utilize Astin's I-E-O model to explain the relationships between variables. 

Step 1:

Click on Analyze, Regression, then Linear

Step 2:

Select the CSS Academic Self-Concept Score [ACADEMIC_SELFCONCEPT] variable

Step 3:

Click on the top arrow button to add it to the Dependent field

Step 4:

Select the Your sex: [SEX] variable

Step 5:

Click on the (second from top) arrow to add it to the Independent(s) field in Block 1 of 1

Step 6:

Repeat Steps 4 and 5 to add the following variables:

• Race: White or Person of Color [RACE1]
• First-generation college student [FIRSTGEN_TFS]
• Academic Self-Concept at college entry [ACADEMIC_SELFCONCEPT_TFS]

Step 7:

Click on the Next button to move to Block 2

The block will update to show 'Block 2 of 2'

Step 8:

Select the Discussed course content with students outside of class [ACT14] variable

Step 9:

Click on the (second from top) arrow to add it to the Block 2 of 2 field

Step 10:

Repeat Steps 8 and 9 to add the following variables:

• Hours per week studying/homework [HPW17]
• Faculty provide intellectual challenge and stimulation [FACPRV16]

Step 11:

Click on the Statistics button

Step 12:

Ensure the following checkboxes are checked:

• Model fit
• R squared change
• Descriptives
• Collinearity diagnostics

Step 13:

Click the Continue button 

The results will populate in your Output window

To interpret the values, you will be analyzing any values below .40. A value of .40 or less indicates a potential multicollinearity problem. To remedy, this would require some extra analysis including removing it from the model and rerunning it. 

Step 1:

Review the Coefficients table, the Standardized Coefficients Beta column and the Sig. (P-Value) column to determine significance.

Step 2:

Review the Model Summary table, the R Square column to asses how well your factors your score.

Example: 

The 7 variables explained 57.9% (R²=.579) of the total variance of the academic self-concept score.

Utilize Astin's (202) I-E-O model to guide your analysis and write-up of the regression analysis. Connect the research question: What is the relationship with student background characteristics, college experiences, and academic self-concept? following the sample write-up provided below.

Alexander Astin's I-E-O- Model

Astin, A. W. (2012). Assessment for excellence: The philosophy and practice of assessment and evaluation in higher education. Rowman & Littlefield Publishers.