# Comparing affected sides instead of just left and right.

#### orangemasa

##### New Member
Hi.
I'm relatively new to statistics and SPSS.

I've been testing the shoulder strength of 200 shoulder patients divided into two groups. I've been testing both of their shoulders, so I could have a reference value.

I would like to compare the affected shoulder of group 1 to the affected shoulder of group 2. The problem is, I entered the data as left and right instead of affected side and unaffected side.

I have entered the affected side for each patients as a categoric value (0=right and 1= left), but I can't figure out how I combine this, so I can compare the affected side of group 1 to the affected side of group 2 - instead of just comparing left and right.
I'm using the independent T-test.

Any help is much appreciated. Thank you.

#### helicon

##### Member
If I've understood correctly, your data looks something like this, right?

Code:
id     grp     left   right  aff_side
1	1	62	23	1
2	2	78	34	0
3	1	35	75	1
4	2	43	54	1
5	1	37	87	0
6	2	53	97	0
If that's correct, then you need to use some if statements to create your DV like so:

Code:
if aff_side = 0 my_dv = right.
if aff_side = 1 my_dv = left.
exe.
Then you can run your comparison with group as the IV and my_dv as the DV.

#### orangemasa

##### New Member
If I've understood correctly, your data looks something like this, right?

Code:
id     grp     left   right  aff_side
1	1	62	23	1
2	2	78	34	0
3	1	35	75	1
4	2	43	54	1
5	1	37	87	0
6	2	53	97	0
If that's correct, then you need to use some if statements to create your DV like so:

Code:
if aff_side = 0 my_dv = right.
if aff_side = 1 my_dv = left.
exe.
Then you can run your comparison with group as the IV and my_dv as the DV.
Thank you. It makes sense. Would you care to elaborate how I do this exactly?

#### helicon

##### Member
Open your dataset then open a syntax window by going to File -> New -> Syntax. Paste the following into the syntax window and change 'aff_side', 'right', and 'left' into whatever you've called those variables in your dataset.

Code:
if aff_side = 0 my_dv = right.
if aff_side = 1 my_dv = left.
exe.
Then press Ctrl-A to highlight all of the syntax, then Ctrl-R to run it.

#### GretaGarbo

##### Human
I would suggest to first take the individual differences. That way you would eliminate the the individual factor and the individual would act as her own control. Just as usual in a paired t-test.

If the data are recorded like in variables like "strength_left" and "strength_right", then I would take the difference (and create the variable "ind_dif"):

ind_dif = strength_left - strength_right

If there is no difference between affected and not affected side, then you would expect numbers around zero, both positive and negative values.

(I guess here that the non-affected side is stronger than the other side on the average)
if the right side is the affected one, then you would expect positive numbers like:

ind_dif = strength_left - strength_right
10 = 21 - 11

if the left side is the affected one, then you would expect negative numbers like:

ind_dif = strength_left - strength_right
-11 = 12 - 23

So then you need to change the sign on "ind_dif" by multiplying by minus one ( -1).

That can be done by transforming the "affected" variable which so far takes values (0=right and 1= left).

affected_new =2*(affected - 0.5)

The affected_new will take value = +1 for left side and -1 for right side.

Now you need to multiply "ind_dif" by affected_new to:

well_vs_affected = ind_dif*affected_new

If you get many positive values, that is the mean on "well_vs_affected" is positive, then there is indication that the "well side" is stronger than the affected side.

But you also had two treatments. Then you can run the variable "well_vs_affected" with a two sample independent t-test. (An equivalent result will be given if you run an one way analysis of variance (anova) with "well_vs_affected" as dependent variable and treatment as an explanatory/independent variable.)