I don't have SPSS or Statistica, so I can't tell you the exact "push-this-button" kind of steps, but perhaps this will help.
Cosinor analysis is fitting a cosine (or sine) curve with a known period. The main idea is that the non-linear problem of fitting a cosine function can be reduced to a problem that is linear in its parameters if the period is known. I will assume that your period T=24 hours.
- You should already have two variables: Time at which the measurement is taken, and Value of the measurement (these, of course, might be called something else).
- Now create two new variables: SinTime = sin(2 x pi x Time / 24) and CosTime = cos(2 x pi x Time / 24) - this is desribed on p.11 of the presentation you linked (x is multiplication). Use pi=3.1415 if the exact value is not built-in.
- Run multiple linear regression with Value as outcome and SinTime and CosTime as two predictors. You should get estimates of their coefficients, which we will call A and B.
- The intercept term of the regression model is the MESOR.
- The AMPLITUDE is sqrt(A^2 + B^2) [square root of A squared plus B squared]
- The ACROPHASE is arctan(- B / A), where arctan is the inverse function of tan. The last two formulas are from p.14 of the presentation.
- The regression model should also give you an R-squared value to see how well the 24 hour circadian pattern fits the data, and an overall p-value that tests for the presence of a circadian component with period 24 hrs.
- One can get standard errors on amplitude and phase using standard error propogation formulas, but that is not included in the presentation.