Contents
Composite scores
- One of the main flexibilities with a statistical package like
SPSS (besides being able to conduct statistical analyses and
obtain tabular and graphical output) is the capacity for
manipulating data.
- One important data manipulation is the creation of new
variables, which are some mathematical function of other
variables.
- Psychological research often involves measuring fuzzy
constructs, such as personality traits, by gathering responses to
multiple items (such as questions in a survey) which are combined
to provide a (hopefully) reliable and valid measure of a broader
construct (such as extraversion).
- These "composite scores" can be:
- Unit-weighted, with the data from each item being equally
weighted (by either adding all items together or calculating the
average of each item), or
- Regression-weighted, e.g., from a factor analysis (not
explained here).
Via pull-down menus
- Make sure you have the data file open, then go to Data View (.sav)
- Enter new variable name in "Target Variable"
- Enter formula for creating composite score in "Numeric
Expression"
-
To compute the score, click OK.
-
Scroll to the right-hand side of the data file (in Data
View) and you should see a new column.
-
If the the new variable doesn't have any data, try
saving your data file (which executes any pending calculations).
-
If data appears, then its a good idea to run
frequencies, descriptives, and/or an appropriate graph to check whether
you have the kind of data you intended to create.
Via syntax
-
An easy way to create the syntax commands is to follow
the instructions above, but on the last step, click Paste instead of OK.
-
The syntax can then be conveniently copied and edited -
this is especially useful if you have many composite scores to
calculate.
-
It also allows the
syntax commands to be saved (.sps) and recalled for later use.
-
e.g.,
compute satistotl = satis+satis2+satis3+satis4+satis5.
compute satismean = mean(satis1,satis2,satis3,satis4,satis5).
Allow for missing values
-
You can allow for missing values by adding ".X" after
"mean", where X is the minimum number of variables that need
to have data for a case in order to calculate a mean.
-
For example, this syntax will
calculate a satis score for a case as long as it has at least 3 values
of the variables listed:
compute satismean = mean.3(satis1,satis2,satis3,satis4,satis5).
-
If a case has data for less than three of the variables, satis
will be system missing (.).
-
Otherwise, a mean will be created using data from all
available variables.
See also
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