1. Each row of the data sheet represents a single case.
2. Each column represents a different score or observation.
For ease of data review and editing, it generally would be advisable to have a logical ordering of variables within the data sheet. For example, if you had three observations of participant weight in a weight loss study, it would make sense to have them sequenced (observation 1 in one column, observation 2 in a following column, and observation 3 in a final column). If you had four observations of each of two dependent variables, you could order them as either
Var1/Obs1, Var1/Obs2...Var1/Obs4, Var2/Obs1, Var2/Obs2, etc.
The SPSS General Linear Model (repeated measures) subprogram offers the opportunity to specify individual variables and repeated observations of individual variables from within the dialog boxes. Which means, all you really have to do is be sure to identify to the subprogram the correctly matched variables to the intended variables/repeated observations.
As Mehmet indicates, your question is a bit open-ended, but perhaps this will help.
More specifically, I have two substrates (Maize and wheat) in which either no or single or double dose of protozoa were added. The parameter values were recorded at 8, 16 or 24 h after incubation.
I try to understand your answer and made arrangements like this
Sample caudatum Lac8 Lac16 Lac24
1 2 1.471404 1.648616 1.739775
Do you if that would be appropriate?
Could you please guide how to proceed with the SPSS program afterwards?
2. In the first dialog box, specify Time (or something else meaningful) as your within-subjects factor, with 3 levels. Then click "Define"
3. Put the three time measures (Lac8,...) in the Within subjects box on the next dialog box, and specify your between-subjects factors just below (substrate, dose). Under "Options", you'll certainly want to see descriptive statistics, homogeneity tests, effect size, and perhaps other info.
4. Execute the analysis.
You'll see in the output multivariate tests first, followed by the univariate, repeated-measures tests (both a table for within-subjects effects and another one for between-subjects effects).