1. Organizing Your Data for Statistical Analysis in SPSS
Edward A. Greenberg, PhD
ASU HEALTH SOLUTIONS DATA LAB
Revised January 4, 2013

2. SPSS Data Sets

3. SPSS Data Sets

4. SPSS Data Sets Rows are cases or observations
Columns are variables (measurements)
Up to columns (2,147,493,647)
No limit on the number of cases

5. Variable Types Numeric (40 character maximum length)
Dates and times (various formats)
Other variations of numeric (currency, comma, scientific notation, etc.)
String (32,767 maximum length)

6. Variable Names Variable names must be unique.
Variable names may be up to 64 characters in length.
Names can contain letters, numbers, or special characters.
Names must start with a letter #, or $.

7. Unit of Analysis What constitutes a “case?” A person A household
An organization
An experimental trial

8. Level of Measurement
Nominal
Ordinal
Interval
Ratio
} Scale

9. Labeling Data Variable names may be short and cryptic.
Variable labels can be up to 255 characters.
SPSS procedures display at least 40 characters of variable labels.
Value labels can be up to 120 characters.

10. Order of Variables
The order of variables in the SPSS data file normally should be the same as the order of items in the questionnaire.
Use variable names that help you identify the scale or instrument to which they apply.

11. Case Numbers Each case in an SPSS file should include a case number.
Often this will be the first variable in the file.
The case number does not identify the subject but it links the data record to the subject’s questionnaire.
Useful for correcting data entry errors

12. Create a Codebook
When preparing to enter your data into SPSS, prepare a codebook for the data set.
The codebook documents all of the items to be entered in the data set:
Variable names and labels
Variable types and formats
Coded values for categorical items
Missing values

13. Sample Codebook VARIABLE NAME TYPE & LENGTH
DESCRIPTION / VARIABLE LABEL / CODED VALUE / VALUE LABEL
CASENO
NUM 3
Case number
SEX
STR 1
6. I am:
M Male
F Female
AGE
NUM 2
7. My age is:
(Code actual age in years)
EDUC
NUM 1
8. What is the highest level of education that you have completed?
Education level
1 No formal education
2 Some grade school
3 Completed grade school
4 Some high school
5 Completed high school
6 Some college
7 Completed college
8 Some graduate work
9 A graduate degree

14. Missing Data Data may be missing for several reasons: Don’t know
Refused to answer
Not applicable
Skipped a question
Instrument problem
Data entry omission

15. Missing Values
SPSS provides several ways of designating numeric data as “missing values.”
A blank cell is treated as “system missing,” represented by a dot (“.”) in the SPSS Data Editor.
Specific values can be declared as “user missing” values.

16. Missing Values
Up to three “user missing” values can be declared for a variable.
Or, a range of values plus one additional value can be declared to be missing.

17. Missing Values

18. Missing Values
In this example, variable AGEWED has three labeled values that are to be treated as missing

19. Missing Values
The three values are declared to be missing in the Missing Values dialog.

20. Missing Values Expressions handle missing values in different ways.
The result of (var1+var2+var3)/3 is missing if any of the three variables is missing.
The result of MEAN(var1, var2, var3) is missing if all three of the variables are missing.

21. Missing Values in Procedures
The FREQUENCIES procedure excludes cases with missing values from computations.

22. Multiple Responses
Multiple-response items are questions that can have more than one value for each case.
Two ways of coding:
For each response, a variable can have one of two values e.g., 1=Yes and 2=No (“multiple-dichotomy” method)
Create a series of variables for 1st choice, 2nd choice, etc. (“multiple categories” method)

23. MULT RESPONSE Procedure
In the MULT RESPONSE procedure, multiple response variables are combines into groups.
The MULT RESPONSE procedure counts responses in multiple response groups in frequency or cross tabular tables.
Total percentages of responses generally will exceed 100%.

24. Repeated Measures
Data that are recorded on more than one occasion for each subject
Some procedures, such as GLM, require that all measurements for a case be on the same data record.
Other procedures, such as the MIXED procedure, may expect one data record per occasion.

25. Repeated Measures
One data record per subject, one variable per occasion on which it is measured

26. Repeated Measures
One data record per occasion per subject

27. Repeated Measures
The good news is that SPSS allows you to easily restructure a data set
Restructure selected variables into cases
Restructure selected cases into variables
Transpose all data