I'm trying to analyze political effects in US Presidential elections.
Red States= Republican wins by >=5%
Blue States = Democrat wins by >=5%
Battleground States = In-Between
Hypotheses: H1 Null Ho Red vs Battleground is more Signif than Blue vs Battleground in affecting Y
H1 Alternate HA Blue vs Battleground is more Signif than Red vs Battleground in affecting Y
H2 Null Ho Political Extremism in State (ie EITHER Red or Blue) is more Signif than Battleground in affecting Y
H2 Alternate HA Battleground is more Signif than Political Extremism in State (ie EITHER Red or Blue) in affecting Y
How would I set up dummy or categorical explanatory variables for this type of model? It seems perplexing.
I've thought of 2 possible solutions, but they both seem wrong:
a) I have a dummy variable RED (=1 if state is Red State; =0 if Blue or Battleground),
I have a dummy variable BATTLE (=1 if state is Battleground; =0 if not Battleground).
b) I have a categorical variable RED (=1 if state is Red State; =0 if Battleground; =-1 if Blue State),
I have a dummy variable BATTLE (=1 if state is Battleground; =0 if not Battleground).
What if I instead have variables like these:
c) I have a dummy variable RED (=1 if state is Red State; =0 if Blue or Battleground),
I have a dummy variable REDBLUE (=1 if state is Red or Blue; =0 if IS Battleground).
Then the following combinations of RED,REDBLUE would mean the following:
1,1 Red State
0,1 Blue State
0,0 Battleground
Comparing the significances of the RED coefficient (vs 0) helps us resolve Hypothesis H1;
and of the REDBLUE coefficient, Hypothesis H2.
Am I making the correct conclusion here?
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