Posted by
Maguin, Eugene on
URL: http://spssx-discussion.165.s1.nabble.com/How-to-enter-cross-sectional-time-series-data-into-SPSS-for-correlation-tp5726681p5726964.html
Sorry, I can't resist. This will be an entirely inadequate response.
Question: In a seven day period, how many of the articles on Ukraine published in the morning on outlet A subsequently appeared in the evening on outlet B.
The data.
Day UkraineA UkraineB
1 3 2
2 2 1
3 15 13 (guess what happened this day)
4 10 10
5 7 4
6 6 5
7 8 6
Correlation UkraineA with UkraineB.
Completely inadequate response!
Gene Maguin
-----Original Message-----
From: SPSSX(r) Discussion [mailto:
[hidden email]] On Behalf Of dave
Sent: Thursday, August 14, 2014 9:33 AM
To:
[hidden email]
Subject: Re: How to enter cross-sectional time-series data into SPSS for correlation
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?
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