Metric Unfolding Program (MLSMU6_2010.F95) -- Can easily
be compiled with gfortran.
unfold_1968.ctl -- Control Card File for Metric Unfolding
Program
1968 Election DataThe 1968 Election Data file contains the same variables that we have used in the past plus the thermometer scores and voting information for the respondents. The variables are:
idno respondent id number partyid strength of party id -- 0 to 6 income raw income category incomeq income quintile -- 1 to 5 race 0 = white, 1 = black sex 0 = man, 1 = woman south 0 = north, 1 = south education 1=HS, 2=SC, 3=College age age in years uulbj lbj position urban unrest uuhhh humphrey pos urban unrest uunixon nixon position urban unrest uuwallace wallace pos urban unrest uuself self placement urban unrest vnmlbj lbj pos vietnam vnmhhh hhh pos vietnam vnmnixon nixon pos vietnam vnmwallace wallace pos vietnam vnmself self placement vietnam voted 1=voted, 5=did not vote votedfor who voted for -- 1 = humphrey, 2= nixon, 3=wallace wallace wallace therm humphrey humphrey thermometer nixon nixon thermometer mccarthy mccarthy thermometer reagan reagan thermometer rockefeller rockefeller thermometer lbj lbj thermometer romney romney thermometer kennedy robert kennedy thermometer muskie muskie thermometer agnew agnew thermometer lemay "bombs away with Curtis LeMay" thermometerThe control card file for the metric unfolding procedure is shown below. The first line has the name of the data file. The first number in the second line is the number of stimuli, the next two numbers are the minimum and maximum number of dimensions to estimate, and the "10" is the number of iterations.
The third line contains some "antique" options we will never use. The only numbers that matter on this line are the "4" which indicates the number of identifying characters to read off each line of the data file (e.g., the respondent id number), and the "2" at the end. This is the number of missing data codes which appear in the sixth line.
The first number in the fourth line is a tolerance value -- leave it as is. The next three numbers are parameters to transform the input data into squared distances. In this case, let amx=-.02, bmx=2.0, and cmx=2.0. The following equation transforms the thermometers into squared distances:
d2 = (amx*t+bmx)cmx
where t = input data. This formula takes a linear transformation of the input data to the power cmx. With amx = -.02, bmx = 2.0, and cmx = 2.0, this is equivalent to subtracting the thermometer score from 100, dividing by 50, and then squaring. This converts t from a 0-100 scale to a 4-0 scale. If the data, t, are distances, set amx = 1.0, bmx = 0.0, and cmx = 2.0. If the data are correlations, set amx = -1.0, bmx = 1.0, and cmx=2.0 or 1.0 if the correlations are initially regarded as unsquared or squared distances respectively.
The next value, "1.5", is the maximum absolute expected coordinate value on any dimension. It is used for plotting purposes. If the squared distances are confined to a 4-0 scale, xmax=1.5 is usually sufficient. The last two numbers, "0.0" and "100.0", are the minimum and maximum expected values of the input data. These are used to catch coding errors in the input data. Anything out of range is treated as missing data.
The fifth line is the format of the data file and the sixth line contains the missing data codes.
Finally, the last 12 lines are labels for the stimuli.
OLS68B.DAT
12 2 2 10 0 0
1 1 0 4 2
.001 -0.02 2.0 2.0 1.5 0.0 100.0
(1X,4A1,60X,12F3.0)
98 99
WALLACE
HUMPHREY
NIXON
MCCARTHY
REAGAN
ROCKEFELLER
LBJ
ROMNEY
R.KENNEDY
MUSKIE
AGNEW
LEMAY
-
Put OLS68B.DAT into Excel and compute the correlation matrix between the 12
sets of candidate feeling thermometers. Turn in the correlation matrix (note that the
correlation matrix will not be entirely accurate because of the missing data codes, 98 and
99!).
Put OLS68B.DAT into Stata and define the variables appropriately.
Run MLSMU6. It will produce an output file called FORT.22. The first 20 lines look like this:
WALLACE 1.2646 0.5154 217.4823 0.5541 1242.0000
HUMPHREY -0.5559 0.3738 114.7892 0.6968 1252.0000
NIXON 0.1480 -0.5415 123.2209 0.5319 1250.0000
MCCARTHY -0.6251 -0.4938 151.8926 0.3854 1204.0000
REAGAN 0.3080 -0.8895 131.8091 0.4380 1212.0000
ROCKEFELLER -0.5579 -0.5995 148.1413 0.3724 1229.0000
LBJ -0.5223 0.4905 147.0334 0.5573 1253.0000
ROMNEY -0.4736 -0.7866 111.3147 0.3434 1167.0000
R.KENNEDY -0.4245 0.2351 148.8571 0.5418 1242.0000
MUSKIE -0.6611 0.1660 126.0836 0.4862 1177.0000
AGNEW 0.2341 -0.8706 114.1418 0.4675 1180.0000
LEMAY 1.1901 0.4267 174.3242 0.4601 1188.0000
1681 -0.0285 0.2555 0.7918 0.6824 12.0000
1124 -0.1768 0.2692 1.4788 0.6992 12.0000
78 0.5707 -0.1514 3.5611 0.2141 12.0000
553 0.1376 0.1064 0.1597 0.7047 9.0000
7 0.2542 0.1235 1.2634 0.0116 12.0000
412 0.2781 0.0867 0.1024 0.6197 12.0000
631 0.5017 0.1088 1.1196 0.0742 12.0000
1316 0.2175 -0.5842 1.1568 0.8577 12.0000
The first two columns after the names are the two dimensional coordinates. The first
12 lines are the coordinates for the political candidates and lines 13 onward are the
coordinates for the respondents. Use SPSS to plot the 12 candidates in two
dimensions. Merge the two dimensional coordinates of the respondents into your Stata file. Turn in the results of the d and summ commands. Be sure that you have defined everything properly!
Create three new variables -- the squared distances from each respondent to Wallace, Humphrey, and Nixon. Turn in the summ command for these variables.
Use Probit and Logit to test the following models:
Voted For Humphrey = f(partyid, income quintile, race, sex, south, education, age, squared distance to Wallace, squared distance to Humphrey, squared distance to Nixon)
Voted For Nixon = f(partyid, income quintile, race, sex, south, education, age, squared distance to Wallace, squared distance to Humphrey, squared distance to Nixon)
Voted For Wallace = f(partyid, income quintile, race, sex, south, education, age, squared distance to Wallace, squared distance to Humphrey, squared distance to Nixon)
The dependent variables are "1" if the respondent voted and voted for Humphrey/Nixon/Wallace respectively, and "0" otherwise.
What should the signs be on the independent variables? Why?
Run the specifications in part (f) using only for those respondents who actually voted (if voted==1).
Paste the dataset into EVIEWS and replicate the probits and logits.