1. Michael Creutzinger, Wil Meyers, Ruixuan Zhang
ANNE BAKER | SANTA CLARA UNIVERSITY
Independent vs.
Campaign Expenditures
EXPLORING THE EFFECTS OF INDEPENDENT EXPENDITURES ON
NON-INCUMBENT CAMPAIN EXPENDITURES AND CAMPAIGN
EXPENDITURE SUCCESS
Abstract:
The use of independent expenditures and “dark money” within political campaigns grew in popularity
during the presidential race of 2012. The purpose of this study is to assess the effects that usage of these
monetary means has on non-incumbent candidate’s total spending, advertisement spending, and success
of candidate spending. It was found that candidates whom spend less than $1,000 (thousand) in total, and
those whom spend less than $500 (thousand) on advertisements are affected positively in each of those
respective categories by the use of dark money. In addition, the candidates whom spent less than $1,000
(thousand) in total, and the candidates whose residual expenditure success was below negative 25 gained a
positive effect in those respective categories by total independent expenditure against the candidate.

2. 1
I. Introduction
An issue that always arises in politics is campaign spending. Since the growing prevalence of
Super PACs began in 2012, the influence of money has been greater than ever (Christenson).
Unlike before with candidate funding and involvement of Political Action Committees (PACs),
Super PACs are able to raise and spend unlimited amounts of money. The only stipulation they
must follow is that the money may not be spent on a specific politician or political party, only
certain issues regarding the political race; and the money must be reported (Baker). However,
these organizations have already begun finding loopholes to this law by producing “issue-ads”
and by using donations from other Super PACs. Therefore, instead of needing to report specific
donors, whom may or may not help the campaign, they are able to just report one other
organization (Baker).
Additionally, there are two different types of Super PACs, “normal” ones and those that
receive funding under 501(c) regulations. The advantage to 501(c) groups is that they are not
forced to report their funding in all scenarios as do normal Super PACs, but instead have limited
sets of rules. This is because 501(c) groups are considered social welfare groups under tax code
(La Raja). Due to the nature of the organizations and their ability to spend and receive freely,
501(c) groups have been nicknamed “dark money.” Furthermore, as a result of finding ways to
keep donors anonymous, there has been a tenfold increase in donations made between 2010
and 2014 (Baker). In comparison, candidate campaigns must report all funding and spending
and can only accept so much money from each individual donor.
As a way to explore the effects of Super PACs and “dark money” on campaign success in the
2012 and 2014 elections, this study measures several different control factors including
whether or not the candidate is Republican; the candidate’s share of democratic presidential
votes; whether or not the race is “competitive;” how much the opposition spends; whether or
not it was an open seat election; how much independent expenditures (super PACs and
501(c)’s) were spent for the candidate; how much independent expenditures were spent
against the candidate; and whether or not “dark money” was involved (since it is not reported,
actual dollar amounts for or against the candidate could not be calculated). An attempt was
then made to use these variables to predict total disbursements made by the candidate,
advertising disbursements made by the candidate, and a standardized measurement of
candidate disbursement success in terms of the voter margin won by the candidate (which is
found by taking the residuals from a regression of total candidate disbursements on candidate’s
voter margin). Specifically, this study looks at non-incumbent candidates, due to their already
natural state of disadvantage (Baker).
The study finds that for those candidates who spend less than $1,000 (thousand) on total
disbursements and less than $500 (thousand) on advertisements are positively affected by the
use of dark money versus not. Those candidates whom spend less than $1,000 (thousand) and
those whose residuals are less than negative 25 are positively affected by the total independent
expenditures spent against the candidate. This is found in the report through use of multiple
regression.

3. 2
II. Data
A total of 758 non-incumbents from the 2012, and 2014 elections were included in the
study. The total disbursements and advertising disbursements were acquired through
candidate profiles provided by the U.S. Federal Election Commission (FEC), and through further
expenditure investigation on whether or not funds were used for ads (Baker). Whether or not
“dark money” was involved in the election was determined through multi-media searches,
including sources such as Lexus Nexus (Baker). Measurement of such was represented as 1 for
involvement of “dark money” and 0 for not. The competitiveness of the campaign is given by
Rothenberg Political Reports and is measured by 1 for competitive and 0 if not. Further
research was done to acquire more information about each candidate including the following: a
dummy variable for whether or not the candidate is experienced, a dummy variable for
whether or not the candidate is Republican, the share of two-party presidential vote as a
measure of district ideology, a dummy variable for whether or not the race is for an open seat,
measurements of total disbursements made by the opposing candidate, and measurements of
how much independent expenditures were used both for and against the candidate. The
variables are summarized in the following table (Table 1).
Table 1: Summaries of variables used in study (the three response variables are listed last)
Variable Name Description
open Open-seat Measurement
• 1 for open
• 0 for not
dpres Share of democratic presidential vote
• Measured 0-100 as percentages
experienced Experience of Candidate
• 1 for experienced
• 0 for not
competitive Competitiveness of candidate
• 1 for competitive
• 0 for not
republican Republican candidate or not
• 1 for Republican
• 0 for not
year2012 Year of election
• 1 for election was in 2012
• 0 for not
year2014 Year of election
• 1 for election was in 2014
• 0 for not
oppdisb Total disbursements made by the opposition in thousands of dollars
dark Use of dark money or not
• 1 for dark money used
• 0 for not
totalagn Total independent expenditures used against the candidate in thousands of
dollars

4. 3
totalfor Total independent expenditures used for the candidate in thousands of
dollars
totaldisb Candidate’s total disbursements in thousands of dollars
advert Amount of candidate’s total disbursements used for advertisements in
thousands of dollars
res Residuals found from regression of candidate’s vote total disbursements in
thousands of dollars on candidate’s vote margins
Additionally, it was found that the majority of observations fall below $1,000 (thousand)
for totaldisb, $500 (thousand) for advert, and -15 for res (or the approximate third quartile of
each response variable). Due to this, the data was split into seven different frames, one for
each subset of data according to the above cutoff points, plus one more dichotomization of the
subgroup for res. For instance, one data frame was created using only those observations that
fall below $1,000 (thousand) totaldisb, which will then be used to regress on totaldisb. These
data frames are named and summarized the following way for the report:
Table 2: New Data frames and their descriptions
Data Frame Name Description
totalDisbLessThan1000 Subset of the data that only includes observations whose
total disbursement is less than $1,000 (thousand)
totalDisbGreaterThan1000 Subset of the data that only includes observations whose
total disbursements is greater than (inclusive) $1,000
(thousand)
advertLessThan500 Subset of the data that only includes observations whose
total advertisement disbursements are less than $500
(thousand)
advertGreaterThan500 Subset of the data that only includes observations whose
total advertisement disbursements are greater than
(inclusive) $500 (thousand)
resLessThanNeg25 Subset of the data that only includes observations whose
residuals are less than negative 25
resGreaterThanNeg25LessThan15 Subset of the data that only includes observations whose
residuals are greater than (inclusive) negative 25 and less
than negative 15
resGreaterThanNeg15 Subset of the data that only includes observations whose
residuals are greater than (inclusive) negative 15

5. 4
III. Exploratory Data Analysis
The following plot demonstrates the distribution of candidates in the full data set according
to each categorical variable of interest (Figure 1). By viewing the distributions within each
category, it helps to identify potential relations between each variable such as dark and open.
Figure 1: Barplot of distribution of candidates throughout each categorical variable
However, it could be by random chance that the distributions between dark and open or
between open and experienced look similar. Therefore, a set of statistical tests of independence
are more accurate in identifying relations.
With the goal of using multiple regression to predict three different responses in the data
(totaldisb, advert, and res), association between each possible predictor variable was
investigated. One method of analyzing association between two different two-level, categorical
variables is to use a Fisher’s Exact Test, which acquires its name because the test does not rely
on approximations through model assumption. Table 2 shows a collection of the significant p-
values found through calculations of Fisher’s Exact Tests between all pairwise categorical
variables within each new data frame (rows that contained zero significant p-values were
removed from the table; the full table is provided in the appendix, Table A.1, section A). The
significant values (p-value <= .050) highlighted in green show that each of those pairs of
categorical variables are not independent of each other. For example, open and experienced
within advertLessThan500 are dependent of each other with a p-value of 0.027. Therefore, it

6. 5
will need to be considered as an interactive term while searching for a significant model in
regression.
Table 3: Table of p-values found from Fisher's Exact Test between categorical pairs; highlighted means <=.050
p-values
total
DisbLess
Than1000
total
DisbGreater
Than1000
advert
Less
Than500
advert
Greater
Than500
res
Less
Than-25
res
Greater
ThanNeg25
LessThan15
res
Greater
Than-15
open,
experienced 0.107 0.188 0.027 0.125 0.505
0.505 0.646
open,
competitive 0.042 0.000 0.234 0.002 0.000
1.000 0.000
open,
dark 0.416 0.046 0.356 0.226 0.205
0.235 0.053
experienced,
dark 0.002 1.000 0.000 0.881 0.656
0.061 1.000
competitive,
republican 1.000 0.481 0.026 0.369 0.220
1.000 0.557
competitive,
year2012 1.000 0.022 0.099 0.034 0.487
1.000 0.037
competitive,
year2014 1.000 0.022 0.099 0.034 0.487
1.000 0.037
competitive,
dark 1.000 0.000 0.000 0.000 1.000
1.000 0.000
year2012
,year2014 0.000 0.000 0.000 0.000 0.000
0.000 0.000
year2012,
dark 0.000 0.011 0.001 0.036 0.178
0.000 0.054
year2014,
dark 0.000 0.011 0.001 0.036 0.178
0.000 0.054
Measuring the level of relationship between a numerical variable and a categorical variable
can be found through one-way ANOVA tests, which specifically measure the difference of
means between categorical levels to see if they are statistically significant or not. If so, it can be
interpreted that the categorical variable shows association with the numerical variable. A
collection of the significant p-values found through one-way ANOVA tests can be found in Table
3 (only those rows with at least one significant p-value were given; the remaining p-values are
given in the appendix, section A).

7. 6
Table 4: One-way ANOVA Test p-values between numerical and categorical predictor variables
p-values
total
DisbLess
Than1000
total
Disb
Greater
Than1000
advert
Less
Than500
advert
Greater
Than500
res
Less
Than-
25
res
Greater
Than
Neg25
LessThan15
res
Greater
Than-15
dpres~open 0.877 0.117 0.634 0.195 0.739 0.581 0.050
dpres~republican 0.000 0.017 0.000 0.529 0.000 0.163 0.005
oppdisb~open 0.031 0.000 0.036 0.003 0.286 0.881 0.200
oppdisb~competitive 0.401 0.020 0.170 0.046 0.655 0.942 0.053
oppdisb~year2012 0.066 0.403 0.066 0.476 0.515 0.012 0.407
oppdisb~dark 0.121 0.925 0.008 0.773 0.975 0.156 0.789
totalagn~open 0.493 0.003 0.351 0.014 0.548 0.162 0.003
totalagn~experienced 0.017 0.203 0.004 0.138 0.145 0.880 0.219
totalagn~competitive 0.949 0.000 0.000 0.000 0.911 0.016 0.000
totalagn~dark 0.000 0.000 0.000 0.000 0.120 0.000 0.000
totalfor~open 0.936 0.259 0.866 0.362 0.658 0.935 0.295
totalfor~experienced 0.011 0.953 0.016 0.643 0.076 0.256 0.982
totalfor~competitive 0.776 0.001 0.000 0.007 0.946 0.003 0.005
totalfor~republican 0.881 0.002 0.291 0.006 0.632 0.125 0.003
totalfor~year2012 0.427 0.077 0.295 0.052 0.275 0.112 0.098
totalfor~dark 0.006 0.000 0.000 0.000 0.189 0.000 0.000
Within the table, the pairwise comparisons include numerical predictor variables against
each other. Significance between response variables and predictor variables indicate that those
predictor variables may be of most significance in a predictive model later, whereas significance
between two predictor variables shows the potential need for interactive terms. Due to the
number of comparisons being made, however, the probability of at least one of the significant
tests being due to random chance is relatively high. However, if an interactive term included in
model searching was originally created by two variables not truly dependent of each other,
then the interactive term will show insignificant, and thus be dropped, while building a model.

8. 7
IV. Methods
The interest of this study is to use multiple different control variables to attempt to predict
the output of different response variables, specifically totaldisb, advert, and res. One method of
doing this is to use multiple regression, simple or not. Since multiple different predictor
variables show relationships with one another, it will be necessary in this study to utilize non-
simple, linear regression, which takes the following form (1):
1 𝑌 = 𝛽& + 𝛽( ∗ 𝑋( + 𝛽+ ∗ 𝑋+ + ⋯ + 𝛽- ∗ 𝑋- + 𝛽-.( ∗ 𝑋/: 𝑋1 + ⋯
where Y represents the response variable; 𝛽& is the intercept of the model or the value of the
response when all else equals zero; 𝛽/ to 𝛽- is the partial effect of variables 𝑋( to 𝑋- while
holding all else constant, since 𝑋/ may or may not be in an interactive term; and 𝛽-.( is the
combined effect of 𝑋/ and 𝑋1 (with i and j being between 1 and n). If the interactive term is
made up of categorical variables, then the term only exists when both are set equal to one (or
when each categorical variable exemplifies the attribute given).
When attempting to build a multiple regression model using a list of available predictor
variables, it is common to use either a “step forward,” “step backward,” or “both directions”
method, which involves adding and discarding variables based off the significance level of the
variable. The significance level is found through a t-test (2) calculated by dividing the variable
estimate by the standard error of the estimate. This t-value is then used to compute a p-value.
2 𝑡 =
𝐵/
𝑆𝐸(𝐵/)
The build method utilized for this project was “step backward.” Specifically, this is done by
fitting the full model for the response variable, and then by removing each predictor variable
one at a time by least significance until all variables and the model itself is found to be
significant.
However, all of these methods are only tangible if the model assumptions hold: normality of
errors, constant variance of errors, and independence of errors. If at least one of the
assumptions fails, it is necessary to consider a model transformation such as logarithm of the
response variable or a box-cox transformation. The worst case scenario is that even a
transformation of the model will not correct the model assumptions.
V. Analysis and Results
By using a “step backwards” method for model building, a significant model was found for
totaldisb within totalDisbLessThan100, advert within advertLessThan500, and res within both
resLessThan-15 and resGreaterThan-15. Models were not found, however, for totaldisb within
totalDisbGreaterThan1000 and for advert within advertGreaterThan500, because either the

9. 8
variables provided were insignificant for the response, or the model did not meet the
assumptions. These are the significant models:
3.1 For data frame totalDisbLessThan1000:
𝒕𝒐𝒕𝒂𝒍𝒅𝒊𝒔𝒃 = 𝛽& + 𝛽( ∗ 𝑑𝑝𝑟𝑒𝑠 + 𝛽+ ∗ 𝑜𝑝𝑝𝑑𝑖𝑠𝑏 + 𝛽K ∗ 𝑡𝑜𝑡𝑎𝑙𝑎𝑔𝑛 + 𝛽P ∗ 𝑡𝑜𝑡𝑎𝑙𝑓𝑜𝑟 +
𝛽R ∗ 𝑒𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒𝑑 + 𝛽U ∗ 𝑟𝑒𝑝𝑢𝑏𝑙𝑖𝑐𝑎𝑛 + 𝛽W ∗ 𝑦𝑒𝑎𝑟2012 + 𝛽Z ∗ 𝑜𝑝𝑒𝑛 + 𝛽[ ∗ 𝑑𝑎𝑟𝑘 +
𝛽(& ∗ 𝑑𝑝𝑟𝑒𝑠: 𝑟𝑒𝑝𝑢𝑏𝑙𝑖𝑐𝑎𝑛
3.2 For data frame advertLessThan500:
log 𝒂𝒅𝒗𝒆𝒓𝒕 = 𝛽& + 𝛽( ∗ 𝑑𝑝𝑟𝑒𝑠 + 𝛽+ ∗ 𝑟𝑒𝑝𝑢𝑏𝑙𝑖𝑐𝑎𝑛 + 𝛽K ∗ 𝑜𝑝𝑒𝑛 + 𝛽P ∗ 𝑑𝑎𝑟𝑘 +
𝛽R ∗ 𝑑𝑝𝑟𝑒𝑠: 𝑟𝑒𝑝𝑢𝑏𝑙𝑖𝑐𝑎𝑛
3.3 For data frame resLessThanNeg25:
𝒓𝒆𝒔 = 𝛽& + 𝛽( ∗ 𝑒𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒𝑑 + 𝛽+ ∗ 𝑜𝑝𝑝𝑑𝑖𝑠𝑏 + 𝛽K ∗ 𝑡𝑜𝑡𝑎𝑙𝑎𝑔𝑛
3.4 For data frame resGreaterThanNeg25LessThanNeg25:
𝒓𝒆𝒔 = 𝛽& + 𝛽( ∗ 𝑜𝑝𝑒𝑛 + 𝛽+ ∗ 𝑐𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑖𝑣𝑒
3.5 For data frame resGreaterThanNeg25:
𝒓𝒆𝒔 = 𝛽& + 𝛽( ∗ 𝑑𝑝𝑟𝑒𝑠 + 𝛽+ ∗ 𝑟𝑒𝑝𝑢𝑏𝑙𝑖𝑐𝑎𝑛 + 𝛽K ∗ 𝑜𝑝𝑝𝑑𝑖𝑠𝑏 + 𝛽P ∗ 𝑑𝑝𝑟𝑒𝑠: 𝑟𝑒𝑝𝑢𝑏𝑙𝑖𝑐𝑎𝑛
The output from each significant model is given in the following table (3), where column
(1) is model (3.1), column (2) is model (3.2), and so on. Additionally, the “constant” is the
intercept of each model.

10. 9
Table 5: Estimates of significant models along with their standard errors and p-values, as well as the full model's statistics
Dependent variable:
log(totaldisb) log(advert) res
(1) (2) (3) (4) (5)
open 0.667
***
0.783
***
1.190
**
(0.204) (0.227) (0.564)
dpres 0.029
***
0.051
***
0.544
**
(0.009) (0.010) (0.217)
experienced 0.469
***
0.328
**
(0.147) (0.131)
republican 4.395
***
5.318
***
35.118
**
(0.620) (0.675) (14.684)
year2012 0.269
**
(0.125)
oppdisb 0.0001
***
0.0001
***
0.001
**
(0.00004) (0.00004) (0.001)
dark 0.714
***
0.805
***
(0.259) (0.265)
totalagn 0.001
**
0.001
***
(0.0003) (0.0003)
dpres:republican -0.079
***
-0.106
***
-0.707
**
(0.012) (0.013) (0.287)
competitive 2.697
**
(1.211)
Constant 2.543
***
0.978
**
-28.528
***
-20.820
***
-32.091
***
(0.371) (0.415) (0.078) (0.294) (11.670)
Observations 516 517 410 136 180
R
2
0.230 0.183 0.069 0.068 0.061
Adjusted R
2
0.216 0.175 0.062 0.054 0.040
Residual Std. Error 1.398 (df = 506) 1.668 (df = 511) 1.044 (df = 406) 2.899 (df = 133) 14.814 (df = 175)
F Statistic 16.787
***
(df = 9; 506) 22.940
***
(df = 5; 511) 9.954
***
(df = 3; 406) 4.868
***
(df = 2; 133) 2.849
**
(df = 4; 175)
Note:
*
p<0.1
**
p<0.05
***
p<0.01

11. 10
The individual variables in each model can be interpreted as normally would be for multiple
regression, except for those variables that are included in an interactive term, namely dpres
and republican. In order to interpret their effects on the model, it is necessary to hold all else
constant and determine the different values for each variable. By doing so for model (3.1), it
can be simplified as:
4 log 𝒕𝒐𝒕𝒂𝒍𝒅𝒊𝒔𝒃
= 2.543 + 0.029 ∗ 𝑑𝑝𝑟𝑒𝑠 + 4.395 ∗ 𝑟𝑒𝑝𝑢𝑏𝑙𝑖𝑐𝑎𝑛 − 0.079 ∗ 𝑑𝑝𝑟𝑒𝑠: 𝑟𝑒𝑝𝑢𝑏𝑙𝑖𝑐𝑎𝑛
Since dpres is continuous from zero to one hundred and republican is an indicator variable with
a potential value of zero (not republican) and one (republican), the interpretation can be
broken apart. For instance, if the candidate is not republican (4.1),
4.1 log 𝒕𝒐𝒕𝒂𝒍𝒅𝒊𝒔𝒃 = 2.543 + 0.029 ∗ 𝑑𝑝𝑟𝑒𝑠
then it can be seen that for each percentage increase of dpres, the candidate spends an extra
0.029 units of log(totaldisb). In other words, the candidate’s expenditures increase by a factor
of $1.029 (thousand). If, on the other hand, the candidate is republican, then things become
more complex (4.2).
4.2 log 𝒕𝒐𝒕𝒂𝒍𝒅𝒊𝒔𝒃 = 2.543 + 0.029 ∗ 𝑑𝑝𝑟𝑒𝑠 + 4.395 ∗ 1 − 0.079 ∗ 𝑑𝑝𝑟𝑒𝑠: (1)
which can be further simplified to (4.3):
4.3 log 𝒕𝒐𝒕𝒂𝒍𝒅𝒊𝒔𝒃 = 2.543 − 0.050 ∗ 𝑑𝑝𝑟𝑒𝑠 + 4.395
Thus, if a candidate is republican, they exemplify an initial 4.395 unit increase in log(totaldisb),
but then a 0.050 decrease in log(totaldisb) for each percentage increase of dpres. In other
words, a republican candidate shows an initial increase in totaldisb by a factor of $81.045
(thousand), but then a decrease in totaldisb by a factor of $𝑒l&.&R&∗mnopq
(thousand), where
dpres increases from zero to one hundred.
As mentioned prior, those variables not included in an interactive term may be interpreted
normally. For example, the variable open shows a 0.667-unit increase in log(totaldisb) for when
the seat is open versus when the seat is not, or that totaldisb increases by a factor of $1.948
(thousand) when the seat is open versus not open.
The model assumptions for each significant model passed and the details for each can be
found in the appendix, section A.

12. 11
VI. Conclusions
The main focus of the study is to view the effects of dark money and independent
expenditures on the political campaigns in 2012 and 2014. The use of dark money was only
found to be significant when modeling totaldisb and advert (models (3.1) and (3.2)). However,
dark was also found to be related with several of the control variables in the model and
therefore may have dropped out of the res models, because it’s effect was masked by another
variable in the model.
Looking at the model for totaldisb (3.1) (within the totalDisbLessThan1000 data frame), the
use of dark money in the campaign and the use of independent expenditures against the non-
incumbent in the campaign caused an increase in totaldisb, specifically a factor of $2.042
(thousand) increase in campaign expenditures when dark money is used and a factor of
$𝑒&.&&(∗rsrtutv-
(thousand) increase in totaldisb for each additional $1 (thousand) increase in
independent expenditures against the candidate. This supports the hypothesis that the use of
dark money and independent expenditures against the candidate has a significant effect on
how much the candidate campaign spends.
For advert (3.2), the model shows almost the same, except that the use of independent
expenditures against the campaign no longer has a significant effect, only the use of dark
money does. On average, the use of dark money within a campaign not only shows the
candidate to spend more money in total, but also more money specifically on advertisements
(advert). Explicitly, a factor of $2.237 (thousand) increase in advert is seen when dark money is
used versus when it is not.
According the models for res (residuals from the regression of total disbursements on voter
margin), the use of dark money has no true effect on the candidate’s success of disbursement
usage as measured by res. However, for those candidates within resLessThanNeg25, the use of
independent expenditures does have a slight positive effect on res (model 3.3). This means that
for every $1 (thousand) expended by independent groups against the candidate, when the
candidate’s res is less than negative 25, there is a 0.001 increase in res, which shows that
independent expenditures against this subgroup actually helps the candidate’s success rate of
campaign spending.
Therefore, even though the effects of dark money and independent expenditures cannot be
heavily seen in all areas of response, there still exists enough of an effect by either dark money
or independent expenditures against the candidate in each of the responses to conclude that
dark money and independent expenditures have a disruptive effect on campaigns.
However, an area of concern for the data and research is that the upper quartile for both
totaldisb and advert were unable to be predicted using the variables given. This may, however,
just be a result of too few of data points spread out too far, so that no given variable is capable
of predicting the values. Another potential issue may be the way in which res was made into a
trichotomy. They were divided in such a way that the distributions of each subgroup appeared
more gathered than the group as a whole. However, this can sometimes to be referred to as “p-
hacking,” or dividing the data frame as many times as necessary until significant results are
found. A way to overcome this, would be to gather more data, so that divisions of the data are
no longer necessary.

13. 12
VII. References
Baker, Anne, Dr. "The Effectiveness of House Campaign Expenditures in an Age of Outside
Spending and Dark Money Dominance." Thesis. Santa Clara University, 2016. Print.
Christenson, Dino P., and Corwin D. Smidt. "Following The Money: Super Pacs And The 2012
Presidential Nomination." Presidential Studies Quarterly 44.3 (2014): 410-430. Political
Science Complete. Web. 28 Apr. 2016.
"How To Find Relationship Between Variables, Multiple Regression." Multiple Regression. Dell,
n.d. Web. 09 May 2016. <http://www.statsoft.com/Textbook/Multiple-
Regression#general>.
La Raja, Raymond J. "Why Super Pacs: How The American Party System Outgrew The Campaign
Finance System." Forum (2194-6183) 10.4 (2012): 91-104. Political Science Complete.
Web. 28 Apr. 2016.
"Super PAC." Ballotpedia. Ed. Policy Desk. Lucy Burns Institute, n.d. Web. 28 Apr. 2016.
<https://ballotpedia.org/Super_PAC>.