1 The Scientiﬁc Study of Politics
OVERVIEW
Most political science students are interested in the substance of politics
and not in its methodology. We begin with a discussion of the goals of this
book and why a scientiﬁc approach to the study of politics is more interesting
and desirable than a “just-the-facts” approach. In this chapter we provide an
overview of what it means to study politics scientiﬁcally. We begin with an
introduction to how we move from causal theories to scientiﬁc knowledge,
and a key part of this process is thinking about the world in terms of models
in which the concepts of interest become variables that are causally linked
together by theories. We then introduce the goals and standards of political
science research that will be our rules of the road to keep in mind throughout
this book. The chapter concludes with a brief overview of the structure of
this book.
Doubt is the beginning, not the end, of wisdom.
– Chinese proverb
1.1 POLITICAL SCIENCE?
“Which party do you support?” “When are you going to run for ofﬁce?”
These are questions that students often hear after announcing that they
are taking courses in political science. Although many political scientists
are avid partisans, and some political scientists have even run for elected
ofﬁces or have advised elected ofﬁcials, for the most part this is not the
focus of modern political science. Instead, political science is about the
scientiﬁc study of political phenomena. Perhaps like you, a great many of
today’s political scientists were attracted to this discipline as undergraduates
because of intense interests in a particular issue or candidate. Although we
are often drawn into political science based on political passions, the most
1
2 The Scientiﬁc Study of Politics
respected political science research today is conducted in a fashion that
makes it impossible to tell the personal political views of the writer.
Many people taking their ﬁrst political science research course are surprised
to ﬁnd out how much science and, in particular, how much math are
involved. We would like to encourage the students who ﬁnd themselves in
this position to hang in there with us – even if your answer to this encouragement
is “but I’m only taking this class because they require it to graduate,
and I’ll never use any of this stuff again.” Even if you never run a regression
model after you graduate, having made your way through these materials
should help you in a number of important ways. We have written this book
with the following three goals in mind:
• To help you consume academic political science research in your other
courses. One of the signs that a ﬁeld of research is becoming scientiﬁc
is the development of a common technical language. We aim to make
the common technical language of political science accessible to you.
• To help you become a better consumer of information. In political
science and many other areas of scientiﬁc and popular communication,
claims about causal relationships are frequently made. We want you
to be better able to evaluate such claims critically.
• To start you on the road to becoming a producer of scientiﬁc research
on politics. This is obviously the most ambitious of our goals. In our
teaching we often have found that once skeptical students get comfortable
with the basic tools of political science, their skepticism turns into
curiosity and enthusiasm.
To see the value of this approach, consider an alternative way of learning
about politics, one in which political science courses would focus on
“just the facts” of politics. Under this alternative way, for example, a course
offered in 1995 on the politics of the European Union (EU) would have
taught students that there were 15 member nations who participated in
governing the EU through a particular set of institutional arrangements
that had a particular set of rules. An obvious problem with this alternative
way is that courses in which lists of facts are the only material would probably
be pretty boring. An even bigger problem, though, is that the political
world is constantly changing. In 2011 the EU was made up of 27 member
nations and had some new governing institutions and rules that were different
from what they were in 1995. Students who took a facts-only course
on the EU back in 1995 would ﬁnd themselves lost in trying to understand
the EU of 2011. By contrast, a theoretical approach to politics helps us to
better understand why changes have come about and their likely impact on
EU politics.
3 1.2 Approaching Politics Scientiﬁcally
In this chapter we provide an overview of what it means to study politics
scientiﬁcally. We begin this discussion with an introduction to how
we move from causal theories to scientiﬁc knowledge. A key part of this
process is thinking about the world in terms of models in which the concepts
of interest become variables1 that are causally linked together by
theories. We then introduce the goals and standards of political science
research that will be our rules of the road to keep in mind throughout this
book. We conclude this chapter with a brief overview of the structure of
this book.
1.2 APPROACHING POLITICS SCIENTIFICALLY: THE SEARCH FOR
CAUSAL EXPLANATIONS
I’ve said, I don’t know whether it’s addictive. I’m not a doctor. I’m not a
scientist.
– Bob Dole, in a conversation with Katie Couric about tobacco during
the 1996 U.S. presidential campaign
The question of “how do we know what we know” is, at its heart, a
philosophical question. Scientists are lumped into different disciplines that
develop standards for evaluating evidence. A core part of being a scientist
and taking a scientiﬁc approach to studying the phenomena that interest
you is always being willing to consider new evidence and, on the basis of
that new evidence, change what you thought you knew to be true. This
willingness to always consider new evidence is counterbalanced by a stern
approach to the evaluation of new evidence that permeates the scientiﬁc
approach. This is certainly true of the way that political scientists approach
politics.
So what do political scientists do and what makes them scientists? A
basic answer to this question is that, like other scientists, political scientists
develop and test theories. A theory is a tentative conjecture about the causes
of some phenomenon of interest. The development of causal theories about
the political world requires thinking in new ways about familiar phenomena.
As such, theory building is part art and part science. We discuss this
in greater detail in Chapter 2, “The Art of Theory Building.”
1 When we introduce an important new term in this book, that term appears in boldface
type. At the end of each chapter, we will provide short deﬁnitions of each bolded term that
was introduced in that chapter. We discuss variables at great length later in this and other
chapters. For now, a good working deﬁnition is that a variable is a deﬁnable quantity that
can take on two or more values. An example of a variable is voter turnout; researchers
usually measure it as the percentage of voting-eligible persons in a geographically deﬁned
area who cast a vote in a particular election.
4 The Scientiﬁc Study of Politics
Causal theory
Hypothesis
Empirical test
Evaluation of hypothesis
Evaluation of causal theory
Scientific knowledge
Figure 1.1. The road to
scientiﬁc knowledge.
Once a theory has been developed, like all
scientists, we turn to the business of testing our
theory. The ﬁrst step in testing a particular theory
is to restate is as one or more testable hypotheses.
A hypothesis is a theory-based statement about a
relationship that we expect to observe. For every
hypothesis there is a corresponding null hypothesis.
A null hypothesis is also a theory-based statement
but it is about what we would observe if
there were no relationship between an independent
variable and the dependent variable. Hypothesis
testing is a process in which scientists evaluate systematically
collected evidence to make a judgment
of whether the evidence favors their hypothesis or
favors the corresponding null hypothesis. The process
of setting up hypothesis tests involves both
logical reasoning and creative design. In Chapter
3, “Evaluating Causal Relationships,” we focus on
the logical reason side of this process. In Chapter 4, “Research Design,” we
focus on the design part of this process. If a hypothesis survives rigorous
testing, scientists start to gain conﬁdence in that hypothesis rather than in
the null hypothesis, and thus they also gain conﬁdence in the theory from
which they generated their hypothesis.
Figure 1.1 presents a stylized schematic view of the path from theories
to hypotheses to scientiﬁc knowledge.2 At the top of the ﬁgure, we begin
with a causal theory to explain our phenomenon of interest. We then derive
one or more hypotheses about what our theory leads us to expect when we
measure our concepts of interest (which we call variables – as was previously
discussed) in the real world. In the third step, we conduct empirical tests of
our hypotheses.3 From what we ﬁnd, we evaluate our hypotheses relative
to corresponding null hypotheses. Next, from the results of our hypothesis
tests, we evaluate our causal theory. In light of our evaluation of our theory,
we then think about how, if at all, we should revise what we consider to be
scientiﬁc knowledge concerning our phenomenon of interest.
A core part of the scientiﬁc process is skepticism. On hearing of a
new theory, other scientists will challenge this theory and devise further
tests. Although this process can occasionally become quite combative, it is
a necessary component in the development of scientiﬁc knowledge. Indeed,
2 In practice, the development of scientiﬁc knowledge is frequently much messier than this
step-by-step diagram. We show more of the complexity of this approach in later chapters.
3 By “empirical” we simply mean “based on observations of the real world.”
5 1.2 Approaching Politics Scientiﬁcally
a core component of scientiﬁc knowledge is that, as conﬁdent as we are in a
particular theory, we remain open to the possibility that there is still a test
out there that will provide evidence that makes us lose conﬁdence in that
theory.
It is important to underscore here the nature of the testing that scientists
carry out. One way of explaining this is to say that scientists are not
like lawyers in the way that they approach evidence. Lawyers work for a
particular client, advocate a particular point of view (like “guilt” or “innocence”),
and then accumulate evidence with a goal of proving their case
to a judge or jury. This goal of proving a desired result determines their
approach to evidence. When faced with evidence that conﬂicts with their
case, lawyers attempt to ignore or discredit such evidence. When faced with
evidence that supports their case, lawyers try to emphasize the applicability
and quality of the supportive evidence. In many ways, the scientiﬁc and legal
approaches to evidence couldn’t be further apart. Scientiﬁc conﬁdence in a
theory is achieved only after hypotheses derived from that theory have run a
gantlet of tough tests. At the beginning of a trial, lawyers develop a strategy
to prove their case. In contrast, at the beginning of a research project, scientists
will think long and hard about the most rigorous tests that they can
conduct. A scientist’s theory is never proven because scientists are always
willing to consider new evidence.
The process of hypothesis testing reﬂects how hard scientists are on
their own theories. As scientists evaluate systematically collected evidence to
make a judgment of whether the evidence favors their hypothesis or favors
the corresponding null hypothesis, they always favor the null hypothesis.
Statistical techniques allow scientists to make probability-based statements
about the empirical evidence that they have collected. You might think that,
if the evidence was 50–50 between their hypothesis and the corresponding
null hypothesis, the scientists would tend to give the nod to the hypothesis
(from their theory) over the null hypothesis. In practice, though, this is
not the case. Even when the hypothesis has an 80–20 edge over the null
hypothesis, most scientists will still favor the null hypothesis. Why? Because
scientists are very worried about the possibility of falsely rejecting the null
hypothesis and therefore making claims that others ultimately will show to
be wrong.
Once a theory has become established as a part of scientiﬁc knowledge
in a ﬁeld of study, researchers can build upon the foundation that this
theory provides. Thomas Kuhn wrote about these processes in his famous
book The Structure of Scientiﬁc Revolutions. According to Kuhn, scientiﬁc
ﬁelds go through cycles of accumulating knowledge based on a set of
shared assumptions and commonly accepted theories about the way that
the world works. Together, these shared assumptions and accepted theories
6 The Scientiﬁc Study of Politics
form what we call a paradigm. Once researchers in a scientiﬁc ﬁeld have
widely accepted a paradigm, they can pursue increasingly technical questions
that make sense only because of the work that has come beforehand.
This state of research under an accepted paradigm is referred to as normal
science. When a major problem is found with the accepted theories
and assumptions of a scientiﬁc ﬁeld, that ﬁeld will go through a revolutionary
period during which new theories and assumptions replace the old
paradigm to establish a new paradigm. One of the more famous of these
scientiﬁc revolutions occurred during the 16th century when the ﬁeld of
astronomy was forced to abandon its assumption that the Earth was the
center of the known universe. This was an assumption that had informed
theories about planetary movement for thousands of years. In the book
On Revolutions of the Heavenly Bodies, Nicolai Copernicus presented his
theory that the Sun was the center of the known universe. Although this
radical theory met many challenges, an increasing body of evidence convinced
astronomers that Coperinicus had it right. In the aftermath of this
paradigm shift, researchers developed new assumptions and theories that
established a new paradigm, and the affected ﬁelds of study entered into
new periods of normal scientiﬁc research.
It may seem hard to imagine that the ﬁeld of political science has gone
through anything that can compare with the experiences of astronomers in
the 16th century. Indeed, Kuhn and other scholars who study the evolution
of scientiﬁc ﬁelds of research have a lively and ongoing debate about
where the social sciences, like political science, are in terms of their development.
The more skeptical participants in this debate argue that political
science is not sufﬁciently mature to have a paradigm, much less a paradigm
shift. If we put aside this somewhat esoteric debate about paradigms and
paradigm shifts, we can see an important example of the evolution of scientiﬁc
knowledge about politics from the study of public opinion in the
United States.
In the 1940s the study of public opinion through mass surveys was in
its infancy. Prior to that time, political scientists and sociologists assumed
that U.S. voters were heavily inﬂuenced by presidential campaigns – and,
in particular, by campaign advertising – as they made up their minds about
the candidates. To better understand how these processes worked, a team
of researchers from Columbia University set up an in-depth study of public
opinion in Erie County, Ohio, during the 1944 presidential election. Their
study involved interviewing the same individuals at multiple time periods
across the course of the campaign. Much to the researchers’ surprise, they
found that voters were remarkably consistent from interview to interview
in terms of their vote intentions. Instead of being inﬂuenced by particular
events of the campaign, most of the voters surveyed had made up their minds
7 1.3 Variables and Causal Explanations
about how they would cast their ballots long before the campaigning had
even begun. The resulting book by Paul Lazarsfeld, Bernard Berelson, and
Hazel Gaudet, titled The People’s Choice, changed the way that scholars
thought about public opinion and political behavior in the United States.
If political campaigns were not central to vote choice, scholars were forced
to ask themselves what was critical to determining how people voted.
At ﬁrst other scholars were skeptical of the ﬁndings of the 1944 Erie
County study, but as the revised theories of politics of Lazarsfeld et al. were
evaluated in other studies, the ﬁeld of public opinion underwent a change
that looks very much like what Thomas Kuhn calls a “paradigm shift.” In
the aftermath of this ﬁnding, new theories were developed to attempt to
explain the origins of voters’ long-lasting attachments to political parties in
the United States. An example of an inﬂuential study that was carried out
under this shifted paradigm is Richard Niemi and Kent Jenning’s seminal
book from 1974, The Political Character of Adolescence: The Inﬂuence
of Families and Schools. As the title indicates, Niemi and Jennings studied
the attachments of schoolchildren to political parties. Under the pre–Erie
County paradigm of public opinion, this study would not have made much
sense. But once researchers had found that voter’s partisan attachments
were quite stable over time, studying them at the early ages at which they
form became a reasonable scientiﬁc enterprise. You can see evidence of
this paradigm at work in current studies of party identiﬁcation and debates
about its stability.
1.3 THINKING ABOUT THE WORLD IN TERMS OF VARIABLES AND
CAUSAL EXPLANATIONS
So how do political scientists develop theories about politics? A key element
of this is that they order their thoughts about the political world in terms of
concepts that scientists call variables and causal relationships between variables.
This type of mental exercise is just a more rigorous way of expressing
ideas about politics that we hear on a daily basis. You should think of each
variable in terms of its label and its values. The variable label is a description
of what the variable is, and the variable values are the denominations
in which the variable occurs. So, if we’re talking about the variable that
reﬂects an individual’s age, we could simply label this variable “Age” and
some of the denominations in which this variable occurs would be years,
days, or even hours.
It is easier to understand the process of turning concepts into variables
by using an example of an entire theory. For instance, if we’re thinking
about U.S. presidential elections, a commonly expressed idea is that the
8 The Scientiﬁc Study of Politics
incumbent president will fare better when the economy is relatively healthy.
If we restate this in terms of a political science theory, the state of the
economy becomes the independent variable, and the outcome of presidential
elections becomes the dependent variable. One way of keeping the lingo of
theories straight is to remember that the value of the “dependent” variable
“depends” on the value of the “independent” variable. Recall that a theory
is a tentative conjecture about the causes of some phenomenon of interest.
In other words, a theory is a conjecture that the independent variable is
causally related to the dependent variable; according to our theory, change
in the value of the independent variable causes change in the value of the
dependent variable.
This is a good opportunity to pause and try to come up with your own
causal statement in terms of an independent and dependent variable; try
ﬁlling in the following blanks with some political variables:
causes
Sometimes it’s easier to phrase causal propositions more speciﬁcally in terms
of the values of the variables that you have in mind. For instance,
higher causes lower
or
higher causes higher
Once you learn to think about the world in terms of variables you will be
able to produce an almost endless slew of causal theories. In Chapter 4 we
will discuss at length how we design research to evaluate the causal claims
in theories, but one way to initially evaluate a particular theory is to think
about the causal explanation behind it. The causal explanation behind a
theory is the answer to the question, “why do you think that this independent
variable is causally related to this dependent variable?” If the answer
is reasonable, then the theory has possibilities. In addition, if the answer is
original and thought provoking, then you may really be on to something.
Let’s return now to our working example in which the state of the economy
is the independent variable and the outcome of presidential elections
is our dependent variable. The causal explanation for this theory is that
we believe that the state of the economy is causally related to the outcome
of presidential elections because voters hold the president responsible for
management of the national economy. As a result, when the economy has
been performing well, more voters will vote for the incumbent. When the
9 1.3 Variables and Causal Explanations
Independent variable
(concept) Causal theory
Dependent variable
(concept)
(Operationalization)
Hypothesis
Dependent variable
(measured)
(Operationalization)
Independent variable
(measured)
Figure 1.2. From theory to hypothesis.
economy is performing poorly, fewer voters will support the incumbent
candidate. If we put this in terms of the preceding ﬁll-in-the-blank exercise,
we could write
economic performance causes presidential election outcomes,
or, more speciﬁcally, we could write
higher economic performance causes higher incumbent vote.
For now we’ll refer to this theory, which has been widely advanced and
tested by political scientists, as “the theory of economic voting.”
To test the theory of economic voting in U.S. presidential elections, we
need to derive from it one or more testable hypotheses. Figure 1.2 provides
a schematic diagram of the relationship between a theory and one of its
hypotheses. At the top of this diagram are the components of the causal
theory. As we move from the top part of this diagram (Causal theory) to
the bottom part (Hypothesis), we are moving from a general statement
about how we think the world works to a more speciﬁc statement about a
relationship that we expect to ﬁnd when we go out in the real world and
measure (or operationalize) our variables.4
4 Throughout this book we will use the terms “measure” and “operationalize” interchangeably.
It is fairly common practice in the current political science literature to use the term
“operationalize.”
10 The Scientiﬁc Study of Politics
At the theory level at the top of Figure 1.2, our variables do not need to
be explicitly deﬁned. With the economic voting example, the independent
variable, labeled “Economic Performance,” can be thought of as a concept
that ranges from values of very strong to very poor. The dependent variable,
labeled “Incumbent Vote,” can be thought of as a concept that ranges
from values of very high to very low. Our causal theory is that a stronger
economic performance causes the incumbent vote to be higher.
Because there are many ways in which we can measure each of our
two variables, there are many different hypotheses that we can test to ﬁnd
out how well our theory holds up to real-world data. We can measure
economic performance in a variety of ways. These measures include inﬂation,
unemployment, real economic growth, and many others. “Incumbent
Vote” may seem pretty straightforward to measure, but here there are also
a number of choices that we need to make. For instance, what do we do in
the cases in which the incumbent president is not running again? Or what
about elections in which a third-party candidate runs? Measurement (or
operationalization) of concepts is an important part of the scientiﬁc process.
We will discuss this in greater detail in Chapter 5, which is devoted
entirely to evaluating different variable measurements and variation in variables.
For now, imagine that we are operationalizing economic performance
with a variable that we will label “One Year Real Economic Growth Per
Capita.” This measure, which is available from ofﬁcial U.S. government
sources measures the one-year rate of inﬂation-adjusted (thus the term
“real”) economic growth per capita at the time of the election. The adjustments
for inﬂation and population (per capita) reﬂect an important part
of measurement – we want our measure of our variables to be comparable
across cases. The values for this variable range from negative values for
years in which the economy shrank to positive values for years in which
the economy expanded. We operationalize our dependent variable with a
variable that we label “Incumbent Party Percentage of Major Party Vote.”
This variable takes on values based on the percentage of the popular vote,
as reported in ofﬁcial election results, for the party that controlled the presidency
at the time of the election and thus has a possible range from 0 to
100. In order to make our measure of this dependent variable comparable
across cases, votes for third party candidates have been removed from this
measure.5
5 If you’re questioning the wisdom of removing votes for third party candidates, you are
thinking in the right way – any time you read about a measurement you should think about
different ways in which it might have been carried out. And, in particular, you should focus
on the likely consequences of different measurement choices on the results of hypothesis
tests. Evaluating measurement strategies is a major topic in Chapter 5.
11 1.3 Variables and Causal Explanations
0102030405060708090100
Incumbent-PartyPercentageofMajorPartyVote
−20 −10 0 10 20
One-Year Real Economic Growth Per Capita
Figure 1.3. What would you expect to see based on the theory of economic voting?
Figure 1.3 shows the axes of the graph that we could produce if we
collected the measures of these two variables. We could place each U.S.
presidential election on the graph in Figure 1.3 by identifying the point
that corresponds to the value of both “One-Year Real Economic Growth”
(the horizontal, or x, axis) and “Incumbent-Party Vote Percentage” (the
vertical, or y, axis). For instance, if these values were (respectively) 0 and
50, the position for that election year would be exactly in the center of
the graph. Based on our theory, what would you expect to see if we collected
these measures for all elections? Remember that our theory is that
a stronger economic performance causes the incumbent vote to be higher.
And we can restate this theory in reverse such that a weaker economic
performance causes the incumbent vote to be lower. So, what would this
lead us to expect to see if we plotted real-world data onto Figure 1.3? To
get this answer right, let’s make sure that we know our way around this
graph. If we move from left to right on the horizontal axis, which is labeled
“One-Year Real Economic Growth,” what is going on in real-world terms?
We can see that, at the far left end of the horizontal axis, the value is –20.
This would mean that the U.S. economy had shrunk by 20% over the past
year, which would represent a very poor performance (to say the least). As
we move to the right on this axis, each point represents a better economic
performance up to the point where we see a value of +20, indicating that
the real economy has grown by 20% over the past year. The vertical axis
depicts values of “Incumbent-Party Vote Percentage.” Moving upward on
12 The Scientiﬁc Study of Politics
this axis represents an increasing share of the popular vote for the incumbent
party, whereas moving downward represents a decreasing share of the
popular vote.
Now think about these two axes together in terms of what we would
expect to see based on the theory of economic voting. In thinking through
these matters, we should always start with our independent variable. This is
because our theory states that the value of the independent variable exerts a
causal inﬂuence on the value of the dependent variable. So, if we start with
a very low value of economic performance – let’s say –15 on the horizontal
axis – what does our theory lead us to expect in terms of values for the
incumbent vote, the dependent variable? We would also expect the value of
the dependent variable to be very low. This case would then be expected to
be in the lower-left-hand corner of Figure 1.3. Now imagine a case in which
economic performance was quite strong at +15. Under these circumstances,
our theory would lead us to expect that the incumbent-vote percentage
would also be quite high. Such a case would be in the upper-right-hand corner
of our graph. Figure 1.4 shows two such hypothetical points plotted on
the same graph as Figure 1.3. If we draw a line between these two points, this
line would slope upward from the lower left to the upper right. We describe
such a line as having a positive slope. We can therefore hypothesize that
the relationship between the variable labeled “One-Year Real Economic
Growth” and the variable labeled “Incumbent-Party Vote Percentage” will
be a positive relationship. A positive relationship is one for which higher
*
*
0102030405060708090100
Incumbent-PartyPercentageofMajorPartyVote
−20 −10 0 10 20
One-Year Real Economic Growth Per Capita
Figure 1.4. What would you expect to see based on the theory of economic voting? Two
hypothetical cases.
13 1.3 Variables and Causal Explanations
values of the independent variable tend to coincide with higher values of
the dependent variable.
Let’s consider a different operationalization of our independent variable.
Instead of economic growth, let’s use “Unemployment Percentage” as
our operationalization of economic performance. We haven’t changed our
theory, but we need to rethink our hypothesis with this new measurement or
operationalization. The best way to do so is to draw a picture like Figure 1.3
but with the changed independent variable on the horizontal axis. This is
what we have in Figure 1.5. As we move from left to right on the horizontal
axis in Figure 1.5, the percentage of the members of the workforce who are
unemployed goes up. What does this mean in terms of economic performance?
Rising unemployment is generally considered a poorer economic
performance whereas decreasing unemployment is considered a better economic
performance. Based on our theory, what should we expect to see
in terms of incumbent vote percentage when unemployment is high? What
about when unemployment is low?
Figure 1.6 shows two such hypothetical points plotted on our graph
of unemployment and incumbent vote from Figure 1.5. The point in the
upper-left-hand corner represents our expected vote percentage when unemployment
equals zero. Under these circumstances, our theory of economic
voting leads us to expect that the incumbent party will do very well. The
point in the lower-right-hand corner represents our expected vote percentage
when unemployment is very high. Under these circumstances our theory
0102030405060708090100
Incumbent-PartyPercentageofMajorPartyVote
0 10 20 30 40 50 60 70 80 90 100
Unemployment Percentage
Figure 1.5. What would you expect to see based on the theory of economic voting?
14 The Scientiﬁc Study of Politics
*
*
0102030405060708090100
Incumbent-PartyPercentageofMajorPartyVote
0 10 20 30 40 50 60 70 80 90 100
Unemployment Percentage
Figure 1.6. What would you expect to see based on the theory of economic voting? Two
hypothetical cases.
of economic voting leads us to expect that the incumbent party will do very
poorly. If we draw a line between these two points, this line would slope
downward from the upper-left to the lower-right. We describe such a line as
having a negative slope. We can therefore hypothesize that the relationship
between the variable labeled “Unemployment Percentage” and the variable
labeled “Incumbent-Party Vote Percentage” will be a negative relationship.
A negative relationship is one for which higher values of the independent
variable tend to coincide with lower values of the dependent variable.
In this example we have seen that the same theory can lead to a hypothesis
of a positive or a negative relationship. The theory to be tested, together
with the operationalization of the independent and the dependent variables,
determines the direction of the hypothesized relationship. The best way to
translate our theories into hypotheses is to draw a picture like Figure 1.3
or 1.5. The ﬁrst step is to label the vertical axis with the variable label for
the independent variable (as operationalized) and then label the low (left)
and high (right) ends of the axis with appropriate value labels. The second
step in this process is to label the vertical axis with the variable label for
the dependent variable and then label the low and high ends of that axis
with appropriate value labels. Once we have such a ﬁgure with the axes and
low and high values for each properly labeled, we can determine what our
expected value of our dependent variable should be if we observe both a
low and a high value of the independent variable. And, once we have placed
15 1.4 Models of Politics
the two resulting points on our ﬁgure, we can tell whether our hypothesized
relationship is positive or negative.
Once we have ﬁgured out our hypothesized relationship, we can collect
data from real-world cases and see how well these data reﬂect our expectations
of a positive or negative relationship. This is a very important step
that we can carry out fairly easily in the case of the theory of economic voting.
Once we collect all of the data on economic performance and election
outcomes, we will, however, still be a long way from conﬁrming the theory
that economic performance causes presidential election outcomes. Even if
a graph like Figure 1.3 produces compelling visual evidence, we will need
to see more rigorous evidence than that. Chapters 7–11 focus on the use
of statistics to evaluate hypotheses. The basic logic of statistical hypothesis
testing is that we assess the probability that the relationship we ﬁnd could
be due to random chance. The stronger the evidence that such a relationship
could not be due to random chance, the more conﬁdent we would be in our
hypothesis. The stronger the evidence that such a relationship could be due
to random chance, the more conﬁdent we would be in the corresponding
null hypothesis. This in turn reﬂects on our theory.
We also, at this point, need to be cautious about claiming that we
have “conﬁrmed” our theory, because social scientiﬁc phenomena (such as
elections) are usually complex and cannot be explained completely with
a single independent variable. Take a minute or two to think about what
other variables, aside from economic performance, you believe might be
causally related to U.S. presidential election outcomes. If you can come up
with at least one, you are on your way to thinking like a political scientist.
Because there are usually other variables that matter, we can continue to
think about our theories two variables at a time, but we need to qualify our
expectations to account for other variables. We will spend Chapters 3 and
4 expanding on these important issues.
1.4 MODELS OF POLITICS
When we think about the phenomena that we want to better understand as
dependent variables and develop theories about the independent variables
that causally inﬂuence them, we are constructing theoretical models. Political
scientist James Rogers provides an excellent analogy between models
and maps to explain how these abstractions from reality are useful to us as
we try to understand the political world:
The very unrealism of a model, if properly constructed, is what makes it
useful. The models developed below are intended to serve much the same
function as a street map of a city. If one compares a map of a city to the real
topography of that city, it is certain that what is represented in the map
16 The Scientiﬁc Study of Politics
is a highly unrealistic portrayal of what the city actually looks like. The
map utterly distorts what is really there and leaves out numerous details
about what a particular area looks like. But it is precisely because the map
distorts reality – because it abstracts away from a host of details about what
is really there – that it is a useful tool. A map that attempted to portray
the full details of a particular area would be too cluttered to be useful
in ﬁnding a particular location or would be too large to be conveniently
stored. (2006, p. 276, emphasis in original)
The essential point is that models are simpliﬁcations. Whether or not they
are useful to us depends on what we are trying to accomplish with the
particular model. One of the remarkable aspects of models is that they
are often more useful to us when they are inaccurate than when they are
accurate. The process of thinking about the failure of a model to explain
one or more cases can generate a new causal theory. Glaring inaccuracies
often point us in the direction of fruitful theoretical progress.
1.5 RULES OF THE ROAD TO SCIENTIFIC KNOWLEDGE
ABOUT POLITICS
In the chapters that follow, we will focus on particular tools of political science
research. As we do this, try to keep in mind our larger purpose – trying
to advance the state of scientiﬁc knowledge about politics. As scientists, we
have a number of basic rules that should never be far from our thinking:
• Make your theories causal.
• Don’t let data alone drive your theories.
• Consider only empirical evidence.
• Avoid normative statements.
• Pursue both generality and parsimony.
1.5.1 Make Your Theories Causal
All of Chapter 3 deals with the issue of causality and, speciﬁcally, how we
identify causal relationships. When political scientists construct theories, it
is critical that they always think in terms of the causal processes that drive
the phenomena in which they are interested. For us to develop a better
understanding of the political world, we need to think in terms of causes and
not mere covariation. The term covariation is used to describe a situation in
which two variables vary together (or covary). If we imagine two variables,
A and B, then we would say that A and B covary if it is the case that,
when we observe higher values of variable A, we generally also observe
higher values of variable B. We would also say that A and B covary if it
17 1.5 Rules of the Road
is the case that, when we observe higher values of variable A, we generally
also observe lower values of variable B.6 It is easy to assume that when we
observe covariation we are also observing causality, but it is important not
to fall into this trap.
1.5.2 Don’t Let Data Alone Drive Your Theories
This rule of the road is closely linked to the ﬁrst. A longer way of stating
it is “try to develop theories before examining the data on which you will
perform your tests.” The importance of this rule is best illustrated by a silly
example. Suppose that we are looking at data on the murder rate (number
of murders per 1000 people) in the city of Houston, Texas, by months of
the year. This is our dependent variable, and we want to explain why it
is higher in some months and lower in others. If we were to take as many
different independent variables as possible and simply see whether they
had a relationship with our dependent variable, one variable that we might
ﬁnd to strongly covary with the murder rate is the amount of money spent
per capita on ice cream. If we perform some verbal gymnastics, we might
develop a “theory” about how heightened blood sugar levels in people who
eat too much ice cream lead to murderous patterns of behavior. Of course, if
we think about it further, we might realize that both ice cream sales and the
number of murders committed go up when temperatures rise. Do we have
a plausible explanation for why temperatures and murder rates might be
causally related? It is pretty well known that people’s tempers tend to fray
when the temperature is higher. People also spend a lot more time outside
during hotter weather, and these two factors might combine to produce a
causally plausible relationship between temperatures and murder rates.
What this rather silly example illustrates is that we don’t want our
theories to be crafted based entirely on observations from real-world data.
We are likely to be somewhat familiar with empirical patterns relating to
the dependent variables for which we are developing causal theories. This
is normal; we wouldn’t be able to develop theories about phenomena about
which we know nothing. But we need to be careful about how much we let
what we see guide our development of our theories. One of the best ways to
do this is to think about the underlying causal process as we develop our theories
and to let this have much more inﬂuence on our thinking than patterns
that we might have observed. Chapter 2 is all about strategies for developing
theories. One of these strategies is to identify interesting variation in our
6 A closely related term is correlation. For now we use these two terms interchangeably.
In Chapter 7, you will see that there are precise statistical measures of covariance and
correlation that are closely related to each other but produce different numbers for the
same data.
18 The Scientiﬁc Study of Politics
dependent variable. Although this strategy for theory development relies on
data, it should not be done without thinking about the underlying causal
processes.
1.5.3 Consider Only Empirical Evidence
As we previously outlined, we need to always remain open to the possibility
that new evidence will come along that will decrease our conﬁdence in
even a well-established theory. A closely related rule of the road is that, as
scientists, we want to base what we know on what we see from empirical
evidence, which, as we have said, is simply “evidence based on observing
the real world.” Strong logical arguments are a good start in favor of a
theory, but before we can be convinced, we need to see results from rigorous
hypothesis tests.7
1.5.4 Avoid Normative Statements
Normative statements are statements about how the world ought to be.
Whereas politicians make and break their political careers with normative
statements, political scientists need to avoid them at all costs. Most
political scientists care about political issues and have opinions about how
the world ought to be. On its own, this is not a problem. But when normative
preferences about how the world “should” be structured creep into
their scientiﬁc work, the results can become highly problematic. The best
way to avoid such problems is to conduct research and report your ﬁndings
in such a fashion that it is impossible for the reader to tell what are your
normative preferences about the world.
This does not mean that good political science research cannot be used
to change the world. To the contrary, advances in our scientiﬁc knowledge
about phenomena enable policy makers to bring about changes in an effective
manner. For instance, if we want to rid the world of wars (normative),
we need to understand the systematic dynamics of the international system
that produce wars in the ﬁrst place (empirical and causal). If we want to rid
America of homelessness (normative), we need to understand the pathways
7 It is worth noting that some political scientists use data drawn from experimental settings to
test their hypotheses. There is some debate about whether such data are, strictly speaking,
empirical or not. We discuss political science experiments and their limitations in Chapter
4. In recent years some political scientists have also made clever use of simulated data to
gain leverage on their phenomena of interest, and the empirical nature of such data can
certainly be debated. In the context of this textbook we are not interested in weighing in
on these debates about exactly what is and is not empirical data. Instead, we suggest that
one should always consider the overall quality of data on which hypothesis tests have been
performed when evaluating causal claims.
19 1.6 A Quick Look Ahead
into and out of being homeless (empirical and causal). If we want to help our
favored candidate win elections (normative), we need to understand what
characteristics make people vote the way they do (empirical and causal).
1.5.5 Pursue Both Generality and Parsimony
Our ﬁnal rule of the road is that we should always pursue generality and
parsimony. These two goals can come into conﬂict. By “generality,” we
mean that we want our theories to be applied to as general a class of phenomena
as possible. For instance, a theory that explains the causes of a
phenomenon in only one country is less useful than a theory that explains
the same phenomenon across multiple countries. Additionally, the more
simple or parsimonious a theory is, the more appealing it becomes.8
In the real world, however, we often face trade-offs between generality
and parsimony. This is the case because, to make a theory apply more
generally, we need to add caveats. The more caveats that we add to a theory,
the less parsimonious it becomes.
1.6 A QUICK LOOK AHEAD
You now know the rules of the road. As we go through the next 11 chapters,
you will acquire an increasingly complicated set of tools for developing and
testing scientiﬁc theories about politics, so it is crucial that, at every step
along the way, you keep these rules in the back of your mind. The rest of this
book can be divided into three different sections. The ﬁrst section, which
includes this chapter through Chapter 4, is focused on the development of
theories and research designs to study causal relationships about politics. In
Chapter 2, “The Art of Theory Building,” we discuss a range of strategies
for developing theories about political phenomena. In Chapter 3, “Evaluating
Causal Relationships,” we provide a detailed explanation of the logic
for evaluating causal claims about relationships between an independent
variable, which we call “X,” and a dependent variable, which we call “Y.”
In Chapter 4, “Research Design,” we discuss the research strategies that
political scientists use to investigate causal relationships.
In the second section of this book, we expand on the basic tools that
political scientists need to test their theories. Chapter 5, “Getting to Know
Your Data: Evaluating Measurement and Variations,” is a detailed discussion
of how we measure (or operationalize) our variables, along with an
8 The term “parsimonious” is often used in a relative sense. So, if we are comparing two
theories, the theory that is simpler would be the more parsimonious. Indeed, this rule of
the road might be phrased “pursue both generality and simplicity.” We use the words
“parsimony” and “parsimonious” because they are widely used to describe theories.
20 The Scientiﬁc Study of Politics
introduction to a set of tools that can be used to summarize the characteristics
of variables one at a time. Chapter 6, “Probability and Statistical
Inference,” introduces both the basics of probability theory as well as the
logic of statistical hypothesis testing. In Chapter 7, “Bivariate Hypothesis
Testing,” we begin to apply the lessons from Chapter 6 to a series of
empirical tests of the relationship between pairs of variables.
The third and ﬁnal section of this book introduces the critical concepts
of the regression model. Chapter 8, “Bivariate Regression Models,”
introduces the two-variable regression model as an extension of the concepts
from Chapter 7. In Chapter 9, “Multiple Regression: The Basics,” we
introduce the multiple regression model, with which researchers are able
to look at the effects of independent variable X on dependent variable Y
while controlling for the effects of other independent variables. Chapter 10,
“Multiple Regression Model Speciﬁcation,” and Chapter 11, “Limited
Dependent Variables and Time-Series Data,” provide in-depth discussions
of and advice for commonly encountered research scenarios involving multiple
regression models. Lastly, in Chapter 12, “Putting It All Together to
Produce Effective Research,” we discuss how to apply the lessons learned
in this book to begin to produce original research of your own.
CONCEPTS INTRODUCED IN THIS CHAPTER9
• causal – implying causality. A central focus of this book is on theories
about “causal” relationships.
• correlation – a statistical measure of covariation which summarizes the
direction (positive or negative) and strength of the linear relationship
between two variables.
• covary (or covariation) – when two variables vary together, they
are said to “covary.” The term “covariation” is used to describe
circumstances in which two variables covary.
• data – a collection of variable values for at least two observations.
• dependent variable – a variable for which at least some of the variation
is theorized to be caused by one or more independent variables.
• empirical – based on real-world observation.
• hypothesis – a theory-based statement about what we would expect
to observe if our theory is correct. A hypothesis is a more explicit
statement of a theory in terms of the expected relationship between a
9 At the end of each chapter, we will provide short deﬁnitions of each bolded term that was
introduced in that chapter. These short deﬁnitions are intended to help you get an initial
grasp of the term when it is introduced. A full understanding of these concepts, of course,
can only be gained through a thorough reading of the chapter.
21 Exercises
measure of the independent variable and a measure of the dependent
variable.
• hypothesis testing – the act of evaluating empirical evidence in order
to determine the level of support for the hypothesis versus the null
hypothesis.
• independent variable – a variable that is theorized to cause variation
in the dependent variable.
• measure – a process by which abstract concepts are turned into realworld
observations.
• negative relationship – higher values of the independent variable tend
to coincide with lower values of the dependent variable.
• normal science – scientiﬁc research that is carried out under the shared
set of assumptions and accepted theories of a paradigm.
• normative statements – statements about how the world ought to be.
• null hypothesis – a theory-based statement about what we would
observe if there were no relationship between an independent variable
and the dependent variable.
• operationalize – another word for measurement. When a variable
moves from the concept-level in a theory to the real-world measure
for a hypothesis test, it has been operationalized.
• paradigm – a shared set of assumptions and accepted theories in a
particular scientiﬁc ﬁeld.
• paradigm shift – when new ﬁndings challenge the conventional wisdom
of a paradigm to the point where the set of shared assumptions and
accepted theories in a scientiﬁc ﬁeld is redeﬁned.
• parsimonious – synonym for simple or succinct.
• positive relationship – higher values of the independent variable tend
to coincide with higher values of the dependent variable.
• theoretical model – the combination of independent variables, the
dependent variable, and the causal relationships that are theorized to
exist between them.
• theory – a tentative conjecture about the causes of some phenomenon
of interest.
• variable – a deﬁnable quantity that can take on two or more values.
• variable label – the label used to describe a particular variable.
• variable values – the values that a particular variable can take on.
EXERCISES
1. Pick another subject in which you have taken a course and heard mention of
scientiﬁc theories. How is political science similar to and different from that
subject?