1. 2016-17 Workshop Series May 17, 2017 Brandon Aragon
Understanding SPSS
Workshop Series
May 17, 2017
Brandon Aragon

2. Office of Institutional Research (IR)
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3. IR Staff AVP for Institutional Effectiveness and Director of IR
Muriel Lopez-Wagner
Assistant Director
Tanner Carollo
Institutional Effectiveness Associate
Joanna Oxendine
Research Analyst
Akira Kanatsu
Research Technicians
Brandon Aragon, Mariela Monge
Administrative Support Coordinator
Monica Villarruel

4. What is SPSS? Statistical analysis software
Descriptive statistics and frequencies
T-Tests, ANOVA, correlations, etc.
Tabulated reports, graphs, and charts.
Can also be used for data management

5. SPSS or Excel? Some benefits of using SPSS over Excel include:
Quick and easy access to descriptives
Variety of charts and graphs
Flexible pivot tables
Easy to create subsets
Easy value labeling
User friendly with output that is easy to understand

6. Opening Excel Files Open SPSS Open a dataset Save File Opening Window
Recent Files
Open a dataset
File -> Open -> Data
File Type:
SPSS Statistics (*.sav)
Excel (*.xls, *.xlsx, *.xlsm)
Read Variable names from the first row of data
Save File
When you open the excel file in SPSS, the data will appear in the main SPSS window which includes a data editor.

7. Data View Data View Each row is a different case
Each column is a different variable
Can drag and move variables (unlike Excel)
Variables can be used to group participants
(ex. “program”)
Change to Variable View
Click Variable View tab
Double-click variable
Used for entering data.
Each row represents data from one entity (ex. participant, business etc.), while each column represents a different variable.

8. Variable View: Describing your Data
Edit Variables
Each row is a variable
Name
Edit variable names
No special characters (spaces, $, /, etc.)
First character has to be a letter
Type (String = alphanumeric, Numeric = numbers only)
Width
Change GPA to 2 decimal places
No decimals for ID
Label:
Full Name from Excel
Name = will appear at the top of the corresponding column for each variable; you cannot use spaces or symbols that have other uses in SPSS.
Type = Numeric means that the variable contains numbers and is assumed by SPSS; String consist of stings of letters (ex. Name).
Width = Default is 8, but more can be used for very precise calculations.
Decimals = Default is 2.
Label = Can be longer with no restrictions on names.

9. Variable View: Describing your Data (cont’d)
Value Labels
Gives a label to simple values
Gender: 1 = Male, 2 = Female
Missing Values
Force SPSS to exclude a value
EX: GPA = 99, Class Level missing value
Align
Similar to Excel
Measure
Nominal – Gender
Ordinal – Level
Scale – GPA
Can copy/paste Labels, Values, and Missing
Change to Data View
Click Data View tab
Double-click variable number
Values = for assigning numbers to groups of people.
Missing = Assigning numbers to missing data.
Columns = How many characters are displayed in the column of data view.
Align = Like excel
Measure = Define the level at which the variable is measured.

10. Search (case/variable)
Data View
Click GPA -> Ctrl+F
Variable View
Click Name/Label -> Ctrl+F

11. Frequencies and Descriptives
Analyze -> Descriptive Statistics -> Frequencies
Class level, GPA, Gender, Program
Statistics and Charts buttons
Interpret (Frequency, Percent, Missing ClassLevel)
Descriptives
Analyze -> Descriptive Statistics -> Descriptives
GPA, Gender
Interpret (N, Minimum, Maximum, Mean, Gender)
Remove Missing value from GPA
Right click variable -> Descriptives
Interpret (Different way to get information, different mean because of Missing)

12. Program Exercise
Rename the “Program” variable so that it represents a program that is relevant to your area of expertise.
Enter a number for each case (values)
1 = Participant, 2 = non-Participant
Add variable values
Check the characteristics of the variable to make sure they are appropriate.

13. Select/Split Cases Data -> Split Files Data -> Select Cases
Move program into the box, check ‘Compare groups’
Obtain the Descriptives of GPA
Data -> Select Cases
If condition is satisfied
Gender = 1 AND ClassLevel = ‘Seniors‘
Filter out unselected cases
Copy Selected cases to a new dataset
Delete unselected cases
Descriptives of GPA

14. Crosstabs Analyze -> Descriptive Statistics -> Crosstabs
Select and drag row/column/layer variables
Statistics
Cells – Percentages, Expected Values
Create separate tables to analyze:
Class Level by Program Participants
Gender by Program Participants

15. Computing New Variables/Recoding
Transform -> Compute Variable
Target Variable – New Variable
Compute Honors if GPA >= 3.5
Recode Missing Value
Tranform -> Recode into Same Var.
Old & New Values
Old Value = System-missing
New Value = 0
Click Add

16. Thank You! Questions/comments? Contact Us AD-170 909-537-5052