United Nations

1. GAP Toolkit 5
Training in basic drug abuse data management
and analysis
Training session 7
SPSS: Recode and Compute

2. Objectives
• To introduce and demonstrate SPSS tools for recoding
variables and creating new variables
• To introduce and demonstrate the use of SPSS
command syntax
• To introduce and demonstrate Help features in SPSS
from a dialogue box

3. Types of recode
• A categorical variable into a new set of categories
• A continuous variable into a categorical variable

4. Recode approaches
• Into pre-defined categories of interest
• Into categories of equal size

5. Example: pre-defined categories
• Recode weekly expenditure on drugs into the following
categories:
– Low expenditure <= £100 a week
– High expenditure >= £101 a week

7. select variable for
recoding and click

9. click here to map
old  new values

12. Frequency Percent Valid
percent
Cumulative
percent
Valid Low 31 62.0 62.0 62.0
High 19 38.0 38.0 100.0
Total 50 100.0 100.0
Weekly drug expenditure

13. Example: quantiles
• Recode weekly expenditure into the following:
– 4 categories, each with approximately the same number of
cases (quartiles)
– 10 categories, each with approximately the same number of
cases (deciles)

15. Frequency Percent Valid percent Cumulative percent
Valid <=£90 12 24.0 24.0 24.0
£91 <= X <= £98 12 24.0 24.0 48.0
£99 <= X <= £105 13 26.0 26.0 74.0
£106 <= X >= £116 13 26.0 26.0 100.0
Total 50 100.0 100.0
NTILES of Expend

16. Frequency Percentage Valid percentage Cumulative
percentage
Valid 1 5 10.0 10.0 10.0
2 4 8.0 8.0 18.0
3 7 14.0 14.0 32.0
4 4 8.0 8.0 40.0
5 4 8.0 8.0 48.0
6 6 12.0 12.0 60.0
7 5 10.0 10.0 70.0
8 5 10.0 10.0 80.0
9 6 12.0 12.0 92.0
10 4 8.0 8.0 100.0
Total 50 100.0 100.0
NTILES of EXPEND

17. Compute
• Creates new variables using:
– Standard mathematical operators
– A full range of functions
– Conditional statements

18. Transform/Compute
click here to define variable type

19. Type and Label

20. Yexpend=Expend*52

21. Explore: Yexpend
Statistic Std. error
Yearly drug expenditure Mean 5062.72 77.468
95% Confidence Interval
for Mean
Lower Bound 4907.04
Upper Bound 5218.40
5% Trimmed Mean 5072.89
Median 5148.00
Variance 300062.3
Std. Deviation 547.779
Minimum 3848
Maximum 6032
Range 2184
Interquartile Range 754.00
Skewness -.333 .337
Kurtosis -.446 .662
Descriptives

22. Example 2: Age
• Convert date of birth to age in years

23. (DATE.DMY(01,01,2002) - dob) / (365.25*24*60*60)

24. Explore: Age
Statistic Std. error
AGE Mean 25.43 1.219
95% Confidence Interval
for Mean
Lower Bound 22.97
Upper Bound 27.88
5% Trimmed Mean 25.21
Median 22.84
Variance 72.867
Std. Deviation 8.536
Minimum 11
Maximum 44
Range 32
Interquartile Range 12.77
Skewness .579 .340
Kurtosis -.518 .668
Descriptives

25. Help
• Context-sensitive Help:
– Right click on any part of a dialogue box to obtain help
• Dialogue box Help button

28. Command language
• Command-line rather than Windows interface
• More flexible
• Reusable
• Saved as text files

29. Syntax window
• Enter SPSS commands:
– Type directly
– Paste from a dialogue box
• Run SPSS commands
• Save as a text file

30. The Paste button
click to copy command
into Syntax Window

32. The Run menu

33. Syntax for Age

34. Summary
• Recode:
– Into same variable
– Into different variable
• Compute:
– Mathematical operators
– Functions
• Dialogue box Help:
– Context-sensitive Help
– Dialogue box Help button
• Command language:
– Syntax Window
– Dialogue box Paste button