Coding closed questions

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

• Training session 5

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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

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Components of a data file

• Cases or observations
• Variables
• Values

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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

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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

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Example

• Coding gender
• Possible values male female
• Coding scheme 1 Male 2 Female

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Why numbers?

• Efficient use of computers
• Quicker to enter
• Not subject to spelling mistakes

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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)).

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Pre-code

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

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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)).

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Continuous variables

• Do not generally require coding as
• They are already numerical
• There is a potentially infinite number of
  categories

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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

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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

Drug
1 Heroin
2 Alcohol
3 Hashish
4 Bhang
5 Heroin
6 Hashish
Total N 6
a Limited to first 100 cases.
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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

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Coding Drug

• Create a new variable Drug2type numeric
  width 2 decimals 0label 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

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Click to register code
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(No Transcript)
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Frequency count for Drug Coded
Drug Coded
Frequency Percentage Valid percentage Cumulative percentage
Valid Heroin 2 33.3 33.3 33.3
Alcohol 2 33.3 33.3 66.7
Hashish 1 16.7 16.7 83.3
Bhang 1 16.7 16.7 100.0
Total 6 100.0 100.0
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Note

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

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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

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Exercise coding
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Frequency count of Drug
Drug
Frequency Percentage Valid percentage Cumulative percentage
Valid Alcohol 3 25.0 25.0 25.0
Bhang 1 8.3 8.3 33.3
Hashish 3 25.0 25.0 58.3
Heroin 2 16.7 16.7 75.0
Mandrax 3 25.0 25.0 100.0
Total 12 100.0 100.0
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Frequency count of Condition
Condition Coded
Frequency Percentage Valid Percentage Cumulative percentage
Valid Recovered 5 41.7 41.7 41.7
Relapsed 7 58.3 58.3 100.0
Total 12 100.0 100.0
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Missing values
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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)

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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 systems missing value

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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

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(No Transcript)
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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

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Identification numbers
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ID numbers purpose

• An ID number
• Ensures anonymity
• Links a row in the data file to a physical
  questionnaire

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ID numbers characteristics

• A unique identifier
• Sometimes contains information in a compound form

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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

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Summary

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