I do not understand this to be a merging issue. You appear to have just
one dataset.
In Stata, -merge- requires two datasets.
Nor do I see why you think you need a dummy for every official.
My guess is that you have a problem for which -collapse- is the
solution.
However, you need to -reshape- first.
Here is a guess at an analogue to your problem.
. l
+--------------------------------------+
| Meet WhoL ScoreL WhoW ScoreW |
|--------------------------------------|
1. | 1 B 3 A 12 |
2. | 2 A 6 B 7 |
3. | 3 D 3 C 4 |
4. | 4 C 4 D 3 |
+--------------------------------------+
. reshape long Who Score, i(Meet) string
(note: j = L W)
Data wide -> long
------------------------------------------------------------------------
-----
Number of obs. 4 -> 8
Number of variables 5 -> 4
j variable (2 values) -> _j
xij variables:
WhoL WhoW -> Who
ScoreL ScoreW -> Score
------------------------------------------------------------------------
-----
. l
+-------------------------+
| Meet _j Who Score |
|-------------------------|
1. | 1 L B 3 |
2. | 1 W A 12 |
3. | 2 L A 6 |
4. | 2 W B 7 |
5. | 3 L D 3 |
|-------------------------|
6. | 3 W C 4 |
7. | 4 L C 4 |
8. | 4 W D 3 |
+-------------------------+
After that, you can -collapse- by -Who-.
Nick
[email protected]
Rufus Peabody
I'm doing an analysis of college football officiating and have a large
dataset. Each game played is an observation. There are 7 types of
officials, and I have an ID for each official. I have the 7 official
variables, but the problem is that officials are not confined
exclusively to one type---for example, one official might be "Referee"
one game and "Line Judge" another. What I did to alleviate this
problem was create dummies for each official (there are 596 in total).
Now, the major issue: I would like to somehow transform this so that
each official is an observation and I have mean(penalties),
mean(penalty yards), total games, etc. as variables. This would mean
there should be 596 total observations. Anybody have any suggestions
of how to tackle this problem?
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