0
$\begingroup$

I have over 30 subjects who each did the same task about 50 times. Each of these data point can be in condition a1 or a2 of variable A or condition b1, b2, b3 or b4. So for each subject, I have datapoints for a1b1, a2b2, etc.

I have three dependent variables (v1, v2, v3) for each data point.

I understand that this is a within-subject analysis, done through a repeated measure test.

But the way I understand it, if I want to compare a1 to a2, I have to make a column for a1 with each subject's average for a1, the same for a2, and compare those two columns. And I have to do it for each dependent variable, and then again for b1-b4. And if I want to study the interaction, I have to do the same for a1b1, a2b1, a1b2, and so on, again for each dependent variable. This seems quite impractical.

As of now, my data is set with a column for subject's ID, a column for A condition, a column for B condition, a column for each dependent variable v1 v2 and v3, and a row for each data point.

Is there a way to do the repeated measure analysis as follows?

"(average of v1 when A=a1 for each subject) vs (average of v2 when A=a2 for each subjet)"
$\endgroup$
  • $\begingroup$ (note: as my knowledge of statistics and SPSS is quite limited, I understand this question might be lacking and/or not clear. please don't hesitate to suggest edits) $\endgroup$ – Cristol.GdM Jan 22 '14 at 23:43
  • $\begingroup$ Would a 2-way repeated measures ANOVA for each DV work? statistics.laerd.com/spss-tutorials/… $\endgroup$ – paul Jan 23 '14 at 0:14
  • $\begingroup$ @paul Well that's actually exactly what I am trying to do. The issue is that I need to define two new columns for a1 and a2, then four more for b1 and b2, and then 8 more for each interaction of A and B. For each DV, so that's 42 columns. I'm surprised there is not a way to group all data using another variable (for example, having a column 'subject' with each subject's ID and group all data with the same subject's ID) $\endgroup$ – Cristol.GdM Jan 23 '14 at 18:06
  • $\begingroup$ Sorry I don't understand. A 2-way ANOVA has 2 IVs (A & B). A is binary, one column, vals are 0 and 1 with labels a1 and a2. I don't see why you need two new columns for a1 and a2 If that's the case then there's a problem with your question, because a column should be one variable (A). You don't use a column per condition. B col has 4 values (again one col, nominal var, if ranked then ordinal var). You don't code the interaction as a column, it's calculated in the ANOVA (A*B). You may want to stick to 2 univariate ANOVAs. MANOVAs are harder to interpret, but you can try for v1 vs v2, $\endgroup$ – paul Jan 24 '14 at 5:42
  • $\begingroup$ @paul Well, I might be mistaken, but in the link above the example has 2 IVs and ends up having 6 columns to represent all interactions. Or is there something I'm missing? (thanks for the help by the way :)) $\endgroup$ – Cristol.GdM Jan 24 '14 at 8:11
1
$\begingroup$

I think you want to restructure the data, transforming it from the long to wide format. Here's some links to tutorials (googled):

Quoted from http://kb.iu.edu/data/bbqj.html:

"The long format uses multiple rows for each observation or participant:

ID  WEIGHT  CALORIES  TIME
1   200    3500        1
1   190    3300        2
1   180    3100        3
2   160    3000        1
2   150    2900        2
2   140    2800        3

The wide format uses one row for each observation or participant:

ID  weight1 weight2 weight3 calories1 calories2 calories3
1   200      190    180       3500     3300     3100
2   160      150    140       3000     2900     2800         "

The wizard is in Data > Restructure > Restructure selected cases into variables (second option).

Once the data is in this format you can run the two-way ANOVA for repeated measures (Analyze > General Linear Model > Repeated Measures...).

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.