SPSS Basics – or how to turn item-level variables into composite variables (e.g.,
turning 10 items designed to measure extraversion into an extraversion scale)
We need to create composite variables that include our individual variables to
create scales. Before we do this part, we need to recode any reverse-scored
items to make sure that all items in the composite variables are coded in the
same direction. Next, we need to compute the new, composite variables.
Finally, it is good to compute the mean and standard deviation for newly-created
composite variables.
Syntax files, recoding variables, compute statements, out files, and the
computation of variables in SPSS.
A. Recoding Variables
When we have multiple items in a psychological measure, several items are
often worded in the opposite direction. To take this fact into account when we
score our variables, we need to “recode” such items to make them in line with the
other items. The best way to do this recoding is to use a SYNTAX (.sps) file.
To reproduce this file in SPSS, click on File, then New, then Syntax. A blank
page will appear. Type in the commands from the attached example. In a
syntax file, you use an asterisk (*) to indicate a comment to yourself (not
something read by the computer). Save this file when you are done typing the
recode statements.
To recode variables, simply highlight the commands you want to execute – then
go to the “run” command and click “selection.”
B. Computing new variables
After you have recoded all reverse-scored items, you need to compute the
composite variables. Create a new syntax file OR add to your existing syntax
file. See attached example that works for this data set. Note how the syntax for
creating these compute statements works.
After you type all these commands in (it will be good for you), run the compute
statements. After you have done this part, notice that you now have newlycreated variables in your data file. These variables are your composite variables.
C. Output files. To get a sense of completing one simple SPSS command that
produces an outfile, choose any of the composite variables you created. When
in the Data file, click “analyze,” then “descriptive statistics,” then “Descriptives.”
Now choose the variable you want to examine. The Descriptives command will
give you the mean, standard deviation, N, and range of the variable of interest.
After you choose your variable, either hit “paste” (not OK). After you hit “paste,”
this command will go into an existing syntax file (at the bottom). You can then
run the command from this syntax file by highlighting it and clicking “run” then
“selection.”
________________________________________________________
* Recoding items for an example data set – based on a mood scale administered in a prior study
* recoding the reverse-scored items from the first personality scale, the second personality scale,
* and the mood scales for all three phases
RECODE
s_bf1
s_a5 s_a6 s_a7 s_a8 s_a11 s_a16 s_a17
mood_a1 mood_a7 mood_a12 mood_a17 mood_a21 mood_a24
mood_b1 mood_b7 mood_b12 mood_b17 mood_b21 mood_b24
mood_c1 mood_c7 mood_c12 mood_c17 mood_c21 mood_c24
(1=5) (2=4) (3=3) (4=2) (5=1).
EXECUTE .
* recoding the reverse-scored items from the Self-monitoring scale
RECODE
s_sm1 s_sm2 s_sm3 s_sm7 s_sm9 s_sm11 s_sm13 s_sm15 s_sm16 s_sm18
(1=5) (2=4) (3=3) (4=2) (5=1).
EXECUTE .
* recoding the one reverse-scored item from the sociosexuality scale
RECODE
socio7
(1=9) (2=8) (3=7) (4=6) (5=5) (6=4) (7=3) (8=2) (9=1).
EXECUTE .
* recoding the one reverse-scored item from the relationship satisfaction scale
RECODE
s_d9
(1=5) (2=4) (3=3) (4=2) (5=1).
EXECUTE .
________________________________________________________
Compute statements for jealousy data
______________________________________________________
* Computing variables for the jealousy data.
* Computing composite variables for Mood Scales (higher scores mean more psychological
discomfort)
* Computing moodtot1
Compute moodtot1 = sum(mood_a1, mood_a2, mood_a3, mood_a4, mood_a5, mood_a6,
mood_a7, mood_a8, mood_a9,
mood_a10, mood_a11, mood_a12, mood_a13, mood_a14, mood_a15, mood_a16,
mood_a17, mood_a18,
mood_a19, mood_a20, mood_a21, mood_a22, mood_a23, mood_a24) .
Execute.
* Computing moodtot2
Compute moodtot2 = sum(mood_b1, mood_b2, mood_b3, mood_b4, mood_b5, mood_b6,
mood_b7, mood_b8, mood_b9,
mood_b10, mood_b11, mood_b12, mood_b13, mood_b14, mood_b15, mood_b16,
mood_b17, mood_b18,
mood_b19, mood_b20, mood_b21, mood_b22, mood_b23, mood_b24) .
Execute.
* Computing moodtot3
Compute moodtot3 = sum(mood_c1, mood_c2, mood_c3, mood_c4, mood_c5, mood_c6,
mood_c7, mood_c8, mood_c9,
mood_c10, mood_c11, mood_c12, mood_c13, mood_c14, mood_c15, mood_c16,
mood_c17, mood_c18,
mood_c19, mood_c20, mood_c21, mood_c22, mood_c23, mood_c24) .
Execute.
* Computing composite variables for the five subscales of Personality scale 1
* 1. computing openness
Compute s_o = sum(s_bf3, s_bf8, s_bf13, s_bf18, s_bf23, s_bf28, s_bf33).
Execute.
* 2. computing conscientiousness
Compute s_c = sum(s_bf5, s_bf10, s_bf15, s_bf20, s_bf25, s_bf30, s_bf35).
Execute.
* 3. computing extraversion
Compute s_e = sum(s_bf2, s_bf7, s_bf12, s_bf17, s_bf22, s_bf27, s_bf32).
Execute.
* 4. computing agreeableness
Compute s_a = sum(s_bf4, s_bf9, s_bf14, s_bf19, s_bf24, s_bf29, s_bf34).
Execute.
* 5. computing neuroticism
Compute s_n = sum(s_bf1, s_bf6, s_bf11, s_bf16, s_bf21, s_bf26, s_bf31).
Execute.
* Computing composite variables for Personality Scale 2
*** Computing attachment variables
* 1. computing dependency
Compute s_de = sum(s_a2, s_a3, s_a5, s_a13, s_a16, s_a17).
Execute.
* 2. computing anxiety
Compute s_an = sum(s_a1, s_a4, s_a9, s_a11, s_a14, s_a15).
Execute.
* 3. computing closeness
Compute s_cl = sum(s_a6, s_a7, s_a8, s_a10, s_a12, s_a18).
Execute.
* Computing self-monitoring composite score (higher scores mean one is more likely to pay
attention to how he or she
* presents him or herself in social situations)
Compute sm = sum(s_sm1, s_sm2, s_sm3, s_sm4, s_sm5, s_sm6, s_sm7, s_sm8, s_sm9,
s_sm10,
s_sm11, s_sm12, s_sm13, s_sm14, s_sm15, s_sm16, s_sm17, s_sm18).
Execute.
* Computing SOCIOSEXUALITY composite score (higher scores mean more promiscuity)
Compute sociosex = sum((5*socio1), socio2, (5*socio3), (4*socio4), (2*(socio5+socio6+socio7))).
Execute.
* COMPUTING MULTIDIMENSIONAL JEALOUSY composite SUBSCALES:
* this variable represents the tendency to have jealous thoughts
Compute jeal_cog = sum(mdj1, mdj2, mdj3, mdj4, mdj5, mdj6, mdj7).
Execute.
* this variable represents the tendency to have jealous emotions
Compute jeal_emt = sum(mdj8, mdj9, mdj10, mdj11, mdj12, mdj13, mdj14, mdj15).
Execute.
* this variable represents the tendency to demonstrate jealous behaviors
Compute jeal_beh = sum(mdj16, mdj17, mdj18, mdj19, mdj20, mdj21, mdj22, mdj23).
Execute.
* Computing relationship satisfaction composite score
* higher scores mean happier in the relationship.
Compute rel_sat = sum(s_d7, s_d8, s_d9).
Execute.