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I. Political Science as a Social Science
I. POLITICAL SCIENCE AS A SOCIAL SCIENCE
Subtopics
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Introduction
Social Science
The Social Sciences and the Natural Sciences
The Social Sciences and the Humanities
Key Concepts
Exercises
For Further Study
Introduction
Political Science is in part a social science, and in part a humanity. Both are important. In this
topic, we'll look at the basics of social science inquiry, and then proceed to show how this differs
from, on the one hand, inquiry in the natural sciences and, on the other, inquiry in the
humanities.
Social Science
Social science inquiry seeks to develop empirical theory. "Empirical" refers to things that can
be experienced through the five senses of seeing, hearing, touching, tasting, or (in the case of
political corruption) smelling. “Theory” basically means explanation. An empirical theory of
politics, then, is an explanation of why people behave the way they do politically.
While this approach is only part of political science, it has become a very important part. In
2001, for example, almost three-quarters of the articles in arguably the top three scholarly
journals in the discipline (the American Political Science Review, the American Journal of
Political Science, and the Journal of Politics) included analysis of empirical data.[1]
If a social scientist (or anyone else) observes people engaging in political behavior, he or she will
need to focus on certain characteristics of the people being observed. The observer may wonder
why some people differ from others in their political characteristics. Why, for example, are
some people Democrats while others are Republicans?
A characteristic that differs from one individual (or "aggregate," such as state, country, etc.) to
another is called a variable. One that does not is called a constant. Constants are generally less
interesting than variables. There is not much point in trying to explain voting choices in a
country in which only one party appears on the ballot. Of course, we might then ask why some
countries have only one party whereas others have multi-party systems, but now we are treating
“number of parties” as a variable.
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I. Political Science as a Social Science
Variables take on different values. These may or may not be mathematical values. If we are
comparing party systems of different countries, the values of the variable may be the number of
parties the country has. On the other hand, if we are studying individual party identification, the
values of our variable might be “Democrat,” “Republican,” and so on.
The observer may notice that the values that a variable takes on are not random, but are related to
the values of another variable. For example, one-party political systems may be more common
in countries with low levels of literacy.
A statement positing a relationship between two variables is called a hypothesis. Hypotheses
have three elements:
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A dependent variable. This is the variable we are trying to explain. We want to find out
why the variable takes on the values that it does.
An independent variable. This is the variable thought, directly or indirectly, to influence
the value of the dependent variable.
An indication of how the two variables are thought to be related, and a tentative
explanation as to why. It is insufficient, for example, to hypothesize that the type of
party system in a country tends to depend upon the level of literacy. The hypothesis must
specify, for example, that one-party systems are related to lower levels of literacy
(perhaps because a literate population is more likely to be informed about what is going
on politically).
The terms “dependent variable” and “independent variable” are similar to the terms “effect” and
“cause” respectively. The fact that two variables are related, however, does not necessarily
mean that one causes the other, even indirectly.
Everyday language is full of what are, in effect, hypotheses about political behavior. For
example, talk about a “gender gap” in voting hypothesizes that vote (the dependent variable) is in
part a function of gender (the independent variable), with women more likely to vote for
Democrats and men more likely to vote Republican.
Social science research differs in two ways from everyday discussion that attempts to explain
politics. The first is where hypotheses come from. Anyone who follows politics will likely carry
around in his or her head a lot of ideas about what explains political behavior. Such ideas may
come from personal experience, from conversations with others, or from following politics
through the mass media. This is true as well for the ways social scientists think about politics. In
addition, however, social scientists develop hypotheses more systematically by studying the
scholarly literature for the results of previous research. This is important for at least a couple of
reasons.
For one thing, it is usually the case that the more you learn what is already known about a
subject, the more new questions you are likely to have. A review of the literature helps generate
new hypotheses. For another thing, and even more importantly, social science seeks not merely
to describe raw facts, but to explain why people behave the way that they do. To accomplish
this, we need to put our ideas into a broader theoretical context that offers such an explanation.
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I. Political Science as a Social Science
It is a fact that, from 1936 through 2000, the incumbent party had always won the presidency
whenever the Washington Redskins (who were the Boston Redskins in 1936) won their last
home game before the election, and lost whenever the Redskins lost. However, since there is no
reasonable explanation for why this should be the case, it is merely an interesting bit of trivia,
and no serious observer of politics would rely on it in analyzing the next presidential contest.
Despite Washington's loss to the Green Bay Packers two days earlier, President Bush managed
to win reelection in 2004 (though the Redskins did lose to the Pittsburgh Steelers the night before
Barak Obama won the presidency in 2008).[2]
A second difference is that, for many people, ideas about patterns of political behavior remain
merely assumptions. Social science insists that the validity of assumptions must be tested
against data.
Testing a hypothesis requires, among other things, defining its terms. This needs to be done at
two different levels.
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Conceptual definition. We need to know, and be able to communicate to others, what
our independent and dependent variables mean. What, in other words, is the idea in our
mind when we use a term? Definitions found in dictionaries are examples of conceptual
definitions. Sometimes, the idea that is in our mind when we use a term will be obvious,
but often it will not. Many concepts used in political science are anything but clear. If
we are to study political ideology, for example, we need to spell out with as much
precision as possible what that concept means in the context of our research.
Operational definition. For hypotheses to be tested, we will need to come up with
measurements of our variables. An operational definition is one stated in a way that can
be directly measured by data.
We strive for a consistent one-to-one correspondence between our conceptual definitions and our
measurements (operational definitions) of them. If we succeed, then our measurements have
validity and reliability.
Data needed to provide operational definitions of our variables come from a wide variety of
sources. We may gather the data ourselves. Analysis of data that we gather in order to test
hypotheses that we have formulated is called primary analysis. Often, however, this approach
would be totally beyond our resources of time, money, and expertise. A nationwide survey of
public opinion, for example, would take months to design and carry out, would cost many
thousands of dollars, and would require the services of a large survey research organization.
Often, secondary analysis of data (that is, analysis of data originally gathered for other
purposes) will suit our needs far better. Indeed, a number of very important databases are used
almost exclusively for secondary analysis. The United States Census is a good example. (Its
primary purpose is to provide the basis for apportioning seats in the U.S. House of
Representatives among the states.) The General Social Survey was created for the express
purpose of providing quality survey data for secondary analysis. The bibliography for the
American National Election Study[3] includes thousands of entries, the bulk of them employing
secondary analysis.
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I. Political Science as a Social Science
To facilitate secondary analysis, the Inter-university Consortium for Political and Social
Research (ICPSR)[4] was established in 1962, providing an archive for social science data.
Today, there are approximately 700 member institutions, mostly colleges and universities, from
all over the world. Students and faculty at these institutions obtain datasets that provide the
basis for numerous scholarly books, articles, and conference papers, graduate theses and
dissertations, and undergraduate term papers.
We also often distinguish between individual data (for example, a survey of prospective voters)
and aggregate data (for example, information about states or nations). POWERMUTT includes
individual data files (the subsets of the American National Election Study and the General Social
Survey), aggregate data files (the files on American states and on countries), and files containing
both type of data (the files on the U.S. Congress and the California legislature, which include
data about both legislative districts as aggregates and about members as individuals).
The Social Sciences and the Natural Sciences
What we have described as the social science method – the effort to explain empirical
phenomena by developing and testing hypotheses – could as easily be called simply “the
scientific method,” without the “social” qualifier. There are, however, important differences
between social sciences, including political science, and the natural sciences.
One difference is that the natural sciences rely much more heavily on experimental design, in
which subjects are assigned randomly to groups and in which the researcher is able to manipulate
the independent variable in order to measure its impact on the dependent variable. Often, when
people think about the scientific method, what they have in mind are these sorts of controlled
experiments. In political science, we for the most part are not able to carry out experimental
designs. If, for example, we wish to study the impact of party affiliation on decisions by judges,
we cannot very well assign judges to different parties, but rather have to take the data as they
come to us from observing judges in their natural setting.
Experimental design, however, does not define the natural sciences, nor does its absence define
the social sciences. Astronomy, for example, must of necessity rely on observation of things that
cannot be manipulated. “Epidemiological” medical research relies on non-experimental data.
Conversely, the social science discipline of social psychology has been built in large part from
experiments in small group laboratories. In political science, a great deal of laboratory research
on the impact of campaign commercials has been carried out in recent years. Field experiments
are also common, as when survey researchers test the impact of alternative question wordings by
splitting their sample and administering different questionnaire forms to different subsets of
respondents. Nevertheless, it is fair to say that experimental designs are much less common in
the social sciences, including political science, than in the natural sciences. Most of our research
design is, in effect, an effort to approximate the logic of experimental design as closely as
possible.
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I. Political Science as a Social Science
Other differences, also differences in degree, have to do with lower levels of consensus in the
social sciences.
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There is less consensus as to what concepts are important. If you are about to take an
introductory chemistry class, you are going to have to learn about molecules. It won’t
make any difference whether the instructor is a liberal or a conservative. If you are about
to take an introductory political science course, you can be much less confident about
what concepts you will study. Consider “power.” Some regard this as the central
concept that distinguishes political science from other disciples, but others see it as of
much more peripheral importance. In a sense, it is literally true that we do not know (or
at least do not agree on) what we are talking about!
There is less consensus about conceptual definition. Even if we agree that power is a key
concept for the study of politics, we may not agree on what power means. Chemists, on
the other hand, not only agree that molecules are important, they also all mean pretty
much the same thing when they use the term.
There is less consensus about operational definition. Chemists agree on how to measure
the atomic weight of a module. Social scientists are far from unanimous in the ways they
go about measuring power.
It bears repeating that these differences are ones of degree. In the natural sciences there are also
disputes at the frontiers of the various disciplines about what concepts are important, what they
mean, and how they should be measured. In the social sciences, consensus is likely to break
down from the start.
Even if we can agree that a particular concept is important, on what it means, and on how it
should be measured, we will usually encounter far larger problems of measurement error than
those in the natural sciences, where measurement is not without error, but is typically much more
precise.
Finally, remember that we are involved in trying to explain human behavior. People do not seem
to behave as well as molecules. It may be that human behavior is inherently less predictable.
All of these things mean that our theories are less rigorous and complete than many that have
been developed in the natural sciences. Instead of laws (that is, statements that predict with great
accuracy what will happen under certain given conditions, such as Newton’s laws of dynamics or
Einstein's theory of relativity), we have tendencies. The absence of laws greatly limits our
ability to develop theories. The fact, for example, that the outcomes of past presidential elections
have been closely correlated with the state of the economy (or, as Clinton campaign manager
James Carville famously put it, “it’s the economy, stupid”), does not mean that the same will
necessarily hold in the next election.
The fact that we deal with tendencies rather than with laws means that, for the most part (and
despite impressive work by “rational choice” theorists to develop formal mathematical models of
political behavior), political science makes relatively little use of geometry, with its elegant
systems of deduction, but considerable use of statistics, which provides us with tools for dealing
with probabilities.
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I. Political Science as a Social Science
Despite its unavoidable limitations, political science as a social science has produced an
explosion in our knowledge about politics. This has had important practical consequences. For
example, no serious aspirant for a major elected office in an economically developed democracy
would consider embarking on a campaign without consulting experts in survey research, a
signature social science method.
The Social Sciences and the Humanities
In addition to being, in part, a social science, political science is also in part a humanity.
Political science as a humanity means at least a couple of different things.
Normative theory. Because social science is limited to studying that which can be tested
empirically, there are many vital questions about politics that are beyond its scope. Also central
to political science, therefore, is what is called “political philosophy” or “normative theory.”
Whereas empirical social theory seeks to explain why people behave the way that they do,
normative theory seeks standards for judging how we ought to behave. Examples include just
war theory and theories about the equitable distribution of wealth, power, or other resources.
Empirical and normative theorists have often squabbled about the relative value and validity of
their respective parts of the discipline, a dispute manifested in recent years in the arguments for
and against the “Perestroika” movement.[5] Both approaches, however, are needed for a
comprehensive study of politics. Well over two thousand years ago, Aristotle managed to
combine both approaches in his study of Greek city-states, classifying them in terms of whether
their regimes were just (a concern of normative theory) and whether power was in the hands of
the one, the few, or the many (an empirical question).
Descriptive analysis. Not all empirical study of politics involves the methodology of social
science. While Aristotle's classification of city-states had an empirical component, he did not
develop or test hypotheses. This is hardly surprising, since the scientific method as we know it
was not developed until about 400 years ago though the writings of Francis Bacon (1561-1626)
and others, did not spread to the social sciences until the 1800s, and did not become a major part
of the political science mainstream until well into the Twentieth Century. What, for want of a
better term, might be called “descriptive analysis” remains an essential part of the discipline. It
differs from political science as a social science in several important ways.
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Social science is analytic, breaking down problems into relatively small components for
study. Descriptive analysis is more holistic, more likely to focus on the big picture.
Where social science usually looks for typical patterns in human behavior, descriptive
analysis emphasizes behavior that is unique. A social science research project might
involve survey research, in which a sample of a larger population is interviewed in order
to find out how and why people in the population think and act. A descriptive study, on
the other hand, might consist of a biography of a leader who is “one of a kind.”
In order to be as precise as possible in carrying out analysis, social science tends to pay a
great deal of attention to questions of methodology – great care is taken to obtain
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I. Political Science as a Social Science
accurate measures and conduct careful analysis. This in turn has made the social sciences
much more quantitative. While the desire for precision is admirable, critics have a point
when they argue that at times this quest leads political scientists to focus on the most
readily operationalized aspects of a question, rather than on those aspects that are most
important.
Some areas of political science lend themselves more to social science inquiry than others. At
one end of the spectrum, social scientific approaches dominate studies of voting, which involve
analyzing patterns of behavior within entire electorates that may consist of millions of people.
On the other hand, President Lyndon Johnson once responded to critics by noting that “I’m the
only president you’ve got.” Because most countries have only one president (or one prime
minister, chancellor, dictator, or monarch) at a time, studies of chief executives tend with notable
exceptions to take a more holistic, humanities-oriented approach. Studies of legislative bodies
fall someplace in between – biographies of legislators and case histories of individual bills will
combine with roll-call analyses that seek to find patterns in voting alignments. Studies of
industrialized nations generally lend themselves more to social science analysis than those of
developing nations because of the relative lack of reliable data in the latter.
As with empirical theory and normative theory, there need be no quarrel between social science
theorists and those doing descriptive analysis. Rigorous efforts that develop valid
generalizations about political behavior though analysis of large databases are complemented by
the rich context and detail found in studies of unique individuals and events.
Key Concepts
aggregate data
constant
conceptual definition
dependent variable
descriptive analysis
empirical theory
hypothesis
independent variable
individual data
measurement
normative theory
operational definition
primary analysis
secondary analysis
variable
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I. Political Science as a Social Science
Exercises
These are group exercises for the class as a whole, or for small groups.
1.
Develop a rough and ready theory of party identification. Taking the case of the United
States, and considering only those who identify with one of the two major parties,
brainstorm to come up with some hypotheses that help to explain why some people think
of themselves as Republicans, while others think of themselves as Democrats. The
dependent variable in each hypothesis will be party identification (with the values being
“Democrat” and "Republican”).What are some independent variables? Keep a few things
in mind:
o
o
o
o
o
2.
Choose variables that could reasonably be measured (though a survey or some
other means).
Consider carefully issues of both conceptual and operational definition. For
example, if you posit religion as an independent variable, do you mean that there
will be partisan differences between people of different faiths, or do you mean
that people who are more religious (you also need to consider what that means
and how you might measure it) will differ from those who are less so.
Specify the nature of the relationship. Do not merely say that party identification
is related to age. Are older people more likely than younger people to be
Democrats, or are they more likely to be Republicans?
Provide rationales for your hypotheses. (“Senior citizens are more likely than
younger people to be Republicans because they are more worried about higher
taxes.” “Senior citizens are more likely than younger people to be Democrats
because they are more worried about Social Security.”)
Don't worry at this point about whether your hypothesis is valid. Edward
Lascher, a political scientist at Sacramento State University , has remarked that
“finding out that you are wrong is almost never uninteresting.”
Repeat the first exercise, but with a different dependent variable.
For Further Study
“Elementary Concepts in Statistics,” StatSoft. http://www.statsoft.com/textbook/esc.html.
[1]John L. Korey, “Political Science,” in Kimberley Kempf-Leonard (ed.). Encyclopedia of
Social Measurement. San Diego: Academic Press, 2004: Vol. II, 99-108. This number does not
include articles using "analytical theory," most of which employed highly mathematical formal
models of politics but which contained no actual data. These made up almost half of the
remaining articles.
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I. Political Science as a Social Science
[2]David Juran,“Continuous Distributions and Portfolio Analysis,” Managerial Statistics
http://www.columbia.edu/~dj114/part3.doc, 105. Accessed August 23, 2010 ; "Winning
Tradition," Snopes.com http://www.snopes.com/politics/ballot/redskins.asp. November 4, 2008.
Accessed August 23, 2010.
[3]The NES Bibliography
.http://www.electionstudies.org/resources/papers/reference_library.htm. Accessed August 28,
2006.
[4]Originally the ICPR. “Social” was added in 1976.
[5]D.W.Miller, “Storming the Palace in Political Science,” The Chronicle of Higher Education;
Stephen Earl Bennett, “’Perestroika’Lost: Why the Latest ‘Reform’ Movement in Political
Science Should Fail,” PS (June, 2002).
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