A Guide to SPSS/PC+ by Neil Frude (auth.)

By Neil Frude (auth.)

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Whichever categorization system is adopted, a comprehensive list of categories must be devised, and a unique coding number assigned to each category. Coding systems should be both exclusive (no categories overlap) and exhaustive (all valid responses fall into one or other of the categories provided). A general category (such as 'Other') might be provided to ensure that the category system is exhaustive. Standard coding schemes are available for some variables (including the classificatory system used to code occupation in terms of social class, and certain systems used to classify racial origin) but for many variables the researcher may need to establish an original coding scheme.

If preliminary SPSS/ PC+ analyses of a dataset then reveal the presence of values outside the valid range (and not corresponding to an extra value assigned to missing data) this will indicate that coding or typing errors have been made. (vi) Value labels: When drawing up the codebook listing of possible categories for any variable, together with the relevant code numbers, it is useful to bear in mind any value labels that you may wish to include in the SPSS/PC+ data definition file. Like variable labels, these are entirely optional and have no effect on SPSS/PC+ analyses, but SPSS/PC+ output is more easily intelligible if labels are provided for some or all of the values which the variable may take.

Each variable, therefore, should be assigned the appropriate number of single-digit columns within the data matrix. This might be reported in the codebook as 'sex-! column', 'age-2 columns' (unless we feel that we have to allow for the possibility of respondents over 99-years old, in which case 3 columns should be assigned), 'income--4 columns', etc. Remember in each case to allow for a value to represent any missing data, so that a variable which can have I 0 valid values will have 11 possible values altogether and would therefore need to be assigned two columns within the data matrix.

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