Basic Drug Abuse Data Management & Analysis Training

1. Training session 5 GAP Toolkit 5Training in basic drug abuse data management and analysis Codingclosed questions

2. Objectives • To establish a set of practical coding rules for closed questions • To explain the importance of assigning numbers to characteristics • To construct a framework for recording missing values • To introduce identification numbers as a method of ensuring the anonymity of respondents, while maintaining a link between files and questionnaires

3. Components of a data file • Cases or observations • Variables • Values

4. Coding • The identification of the possible values of a variable and the assignment of numbers to those values • The numbers, representing the values, are stored in a data file

5. Closed questions/categorical variables • A limited number of values • The values are mutually exclusive • The values are collectively exhaustive • Code by assigning a number to each value

6. Example • Coding gender • Possible values: male; female • Coding scheme: 1 = Male; 2 = Female

7. Why numbers? • Efficient use of computers • Quicker to enter • Not subject to spelling mistakes

8. Why numbers? • Some statisticians define measurement as necessarily resulting in numbers • “To measure a property means to assign numbers to units as a way of representing that property.” (D. S. Moore, Statistics: Concepts and Controversies, 2nd ed. (New York, W. H. Freeman Press, 1985)).

9. Pre-code • Coding takes place before the questionnaire is delivered • The possible responses to a question are anticipated • The coding appears on the questionnaire

10. Coding rules • Codes must be: • Mutually exclusive • Collectively exhaustive • Consistent across variables (J. Fielding, “Coding and managing data”, Researching Social Life, N. Gilbert, ed. (London, Sage Publications, 1993) and D. De Vaus, Surveys in Social Research (London, Routledge, 2002)).

11. Continuous variables • Do not generally require coding as: • They are already numerical • There is a potentially infinite number of categories

12. Coding in SPSS • The Values column in Variable View is used to implement coding in SPSS • Numbers are allocated to each of the categories of a variable

13. Example: coding Drug Case summariesa • In data file Ex1.sav, a variable called Drug was defined as a string variable and a number of drugs were entered a Limited to first 100 cases.

14. Coding Drug • Decide on a set of numeric labels for the different categories, in this case drugs: • 1 = Heroin • 2 = Alcohol • 3 = Hashish • 4 = Bhang

15. Coding Drug • Create a new variable Drug2:type = numeric; width = 2; decimals = 0;label = Drug Coded • Click on the Values column and then on the three dots that appear to the right of the Values box to generate the following dialogue box:

16. Click to register code

18. Frequency count for Drug Coded: Drug Coded

19. Note • Coding data does not change the level of measurement • The level of measurement is a guide to the selection of appropriate statistics

20. SPSS • Value labels can be assigned to numeric variables and string variables of eight or fewer characters • By default, SPSS sets all numeric variables to Scale variables

21. Exercise: coding

22. Frequency count of Drug Drug

23. Frequency count of Condition Condition Coded

24. Missing values

25. Missing values: causes • The question is not applicable • The respondent does not know • The respondent refuses to answer • No response is marked on the questionnaire (i.e., truly missing and there is no clue why) (De Vaus, 2002)

26. Coding missing values • Use codes outside of the range of common values: • e.g., 9, 99, -99, 999 • If possible, retain the same codes for the various missing options for all variables • The default missing value in SPSS is a full stop . and is called the “system’s missing value”

27. SPSS: missing values • Part of the variable definition • Variable View: Missing column • Click on the Missing cell in the row defining the variable • Click on the three buttons that appear to the right of the Missing cell and the following dialogue box will appear:

29. Exercise • Three additional observations are obtained for Ex1.sav: • DAP1-0013; Alcohol; 39; ------------ • DAP1-0014; Hashish; --; Recovered • DAP1-0015; ---------; 16; Relapsed • Code necessary missing values for the variables • Run a frequency count on Drug and Condition, comparing percentage and valid percentage

30. Identification numbers

31. ID numbers: purpose • An ID number: • Ensures anonymity • Links a row in the data file to a physical questionnaire

32. ID numbers: characteristics • A unique identifier • Sometimes contains information in a compound form

33. Example • DAP1-001, DAP1-002, … : • DAP is short for Drug Assessment Programme • 001, 002 are consecutive numbers that uniquely identify each questionnaire or respondent • There must be at most 999 respondents, as space has only been made available for 999 unique ID numbers

34. Summary • Coding closed questions • Value labels • Frequency counts • Missing values • ID numbers