http://spssx-discussion.165.s1.nabble.com/How-to-enter-cross-sectional-time-series-data-into-SPSS-for-correlation-tp5726681p5726968.html
decades ago by Campbell and Kenny. It was soon discredited by
critiques raised by various authors. A chapter of LINEAR PANEL
(Academic Press, out of print) is devoted to this issue. David F.
> Thanks for your reply, Andy. Just don't abandon me, guys!
>
> What you are saying actually was my initial question: How should I enter my
> data if the goal is to do a cross-correlation between two time series? Its
> exactly the problem that I do not know how the data file should look in the
> end.
>
> I got some feedback here re my initial question and entered T1 for Outlet1
> and T2 for Outlet2 respectively to these suggestions. I didn't proceed with
> coding the other Ts yet (I ll have 14, two per day for seven days, in the
> end) because I wanted to have the structure fixed first in order to avoid
> restructuring afterwards. But probably the lack of time points is the reason
> for the vars being constant?
>
> Maybe a citation of someone who did what I need to do but is speaking about
> it only in general terms could help (its a bit long one but it is very
> clear):
>
> "Cross-correlation is a measure between two variables separated by the
> appropriate amount of time lag for variable 1, which is believed to have an
> effect on variable 2, the proposed effect. This model produces two pairs of
> three different sets of correlations totaling six correlations. The first
> set is the synchronous correlation, the correlation between variable 1,
> cause, and variable 2,effect, measured at concurrent times (PX1Y1 and
> PX2Y2). The second set of correlations is the autocorrelation which is the
> correlation between the same variable at two different times (PX1X2 and
> Y1Y2). The third set is the cross-lagged correlation and is the correlation
> between variable 1 and variable 2 at different times (PX1Y2 and PY1X2). The
> logic behind using this model in its origin is that if the model has been
> built with the correct cause and effect identified then the correlation
> between variable 1, cause, and variable 2,effect, over time (PX1Y2) should
> be greater than the correlation between variable 2, effect, and variable 1,
> cause, over time (PY1X2). The two relationships of interest to scholars then
> are the cross-correlations as they indicate the level of influence between
> variable 1 and variable 2."
>
> This may be a bit complicated to read, so you may also have look at this
> visualizing: Unbenannt.png
> <
http://spssx-discussion.1045642.n5.nabble.com/file/n5726960/Unbenannt.png>
>
> Does this clarify my burden?
>
>
>
> --
> View this message in context:
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