Scoring Syntax

As a courtesy to researchers using the CCAPS data and CCMH counseling center members that are interested in running further analyses on their local CCAPS data, CCMH has provided two .txt files with SPSS syntax for the CCAPS:

CCAPS Subscale Scoring Syntax
Scores all CCAPS Subscale Scores
For those wishing to analyze CCAPS data in SPSS, the
following syntax can be used to (a) reverse score the
appropriate items, (b) select only valid administrations,
and then (c) compute the subscale raw scores.
Subscale administrations are considered valid if more than
66% of the items on that subscale were completed, if more
than 50% of the total items on the CCAPS were filled out,
and if all of the item values are not the same (i.e. not a zero
variance).

In the syntax, syntax labeled “Number Missing”
counts the number of missing items in each subscale and
the total number of missing items on the administration.
The syntax labeled “Variance” calculates the variance for
the administration, and the administration will not be
scored if the variance is zero. The syntax labeled “Invalid
Administrations” labels each subscale administration as
invalid (1) or valid (0). Only valid administrations will be
scored.

Please note:

  • Each variable must be named CCAPS_01,
    CCAPS_02, through CCAPS_62 for this syntax to
    work.
  • IMPORTANT: It is critical to choose your syntax
    based on whether your data is already numbered as
    CCAPS-62, CCAPS-34, or even legacy CCAPS-70
    data.

CCAPS Change SPSS Syntax
Calculates average change per subscale using data from all clients in a dataset.
For those wishing to analyze CCAPS change overtime,
this syntax can be used to calculate the
average change per subscale using data from all clients
in a dataset. The syntax accomplishes this by
filtering out clients who only have one administration of
the CCAPS and then scoring all CCAPS administrations
as CCAPS 34. It then combines the data into a new
dataset in which each client has one line of data
with variables indicating their subscale scores from
their first and last administrations of the CCAPS.
From that new dataset, the syntax then calculates
a change score for each client on each subscale by
subtracting their first score from their last. For these
change scores, positive values represent improvement.
Before running, ensure that your data set has:

  • ID variable representing the unique client ID
    Date variable
  • CCAPS items labeled CCAPS_01 through
  • CCAPS_62. (Syntax to convert variable names from
    the old CCAPS 70 is available upon request.)