POLITICAL SCIENCE AS A SOCIAL SCIENCE

 

Subtopics

• Introduction
• Social Science
• The Social Sciences and the Natural Sciences
• The Social Sciences and the Humanities
• 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 will 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 attempt to explain why people behave the way they do politically. 

 

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 Liberals while others are Conservatives and still others are New Democrats?

 

Characteristics that differ from one person to another are called one variables.  Those that do not are called constants.  Constants are generally less interesting than variables.  There is not much point in trying to explain voting behavior 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.

 

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 “Liberal,” “Conservative,” 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:

 

• 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 to, directly or indirectly, influence the value of the dependent variable. 
• The nature of the relationship between the two variables.  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 that one-party systems are related to lower levels of literacy.

 

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 the Liberals or New Democrats and men more likely to vote Conservative.

 

Social science research differs from everyday discussion of politics in two ways.  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.  Even more important, 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.  It is a fact that in the United States, from 1936 through 2000, the incumbent party has always won the presidency whenever the Washington Redskins won their last home game before the election, and lost whenever the Redskins lost[1].  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.

 

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.

 

• 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 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, very important databases are used almost exclusively in secondary analysis.  The Census Canada data is a good example. Other surveys such as the Canadian National Election Study and the General Social Survey were created, in part, for the express purpose of providing quality survey data for secondary analysis by students of Canadian politics.  Indeed much of the work using the Canadian National is based on secondary analysis.

 

To facilitate secondary analysis, the University of Toronto Data Library, and other university-based data archives have been established throughout the world. The largest of these is the Inter-university Consortium for Political and Social Research (ICPSR)[3] established in 1962.  Today, over 500 colleges and universities from all over the world, including the University of Toronto are member institutions.  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.

 

 

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, differences between social sciences, including political science, and the natural sciences.  Though these are differences in degree, they are important.

 

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 laboratory 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 also 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 will 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.

 

Other differences, also differences in degree, have to do with lower levels of consensus in the social sciences.

 

• 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 mean pretty much the same thing when they use the term.
• There is less consensus about operational definition.  Chemists also agree on how to measure the atomic weight of a molecule.  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 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 predictably as molecules.  Philosophers are not in agreement on this point, but 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), we have tendencies.  The absence of laws greatly limits our ability to develop theories.  When we can say with a high degree of confidence that “if A, then B,” and “if B, then C,” we can predict with similar confidence that “if A, then C.”  When we can merely say “if A, probably B,” and “if B, probably C,” it does not even necessarily follow that “if A, probably C.”  This considerably weakens the power of social science theories.  The fact, for example, that the outcomes of past federal 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 elegant systems of deduction, but considerable use of statistics, which provides us with valuable tools for dealing with probabilities.

 

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 technique.

 

 

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.  A central part of political science 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.  It is concerned with ends rather than with means.  Examples include just war theory, or 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 parts of the discipline, a dispute most recently manifested in the arguments for and against the “Perestroika” movement[4].  It can certainly be argued, however, that both approaches 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).

 

Not all empirical study of politics involves the scientific method as described above.  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.

 

Descriptive Analysis.  What, for want of a better term, will be called “descriptive analysis” remains an essential part of the discipline.  It differs from political science as a social science in several important ways.

 

• Social science is analytic, breaking down problems into relatively small components for study.  Descriptive analysis is holistic, more likely to focus on the big picture.  
• Where social science looks for typical patterns in human behavior, descriptive analysis emphasizes behavior that is unique.  A social science research project may involve survey research, in which a sample of a larger population is interviewed in order to find out how people in the population usually 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 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 has led 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 behavior, which involve analyzing patterns of behavior within entire electorates that may consist of millions of people.  On the other hand, because a country has only one prime minister at a time (or one president, chancellor, dictator, or monarch), 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 seeking to find patterns in voting alignments.

 

As with empirical theory and normative theory, there need be no quarrel between empirical theorists and those doing descriptive analysis.  Rigorous efforts 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.

 

 

Exercises

 

These are group exercise for the class as a whole, or for small groups.

 

1.         Develop a rough and ready theory of party identification.  Taking the case of Canada, and considering only those who identify with one of the three major parties, brainstorm to come up with some hypotheses that help to explain why some people think of themselves as Liberals, while others think of themselves as Conservatives and New Democrats.  The dependent variable in each hypothesis will be party identification (with the values being “Liberal,” “Conservative” and “New Democrat”).  What are some independent variables?  Keep a few things in mind:

 

·        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 (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 Liberals, or are they more likely to be Conservatives.

·        Provide a rationale for your hypotheses.  (“Senior citizens are more likely than younger people to be Conservatives because they are more worried about higher taxes.”  “Senior citizens are more likely than younger people to be Liberals because they are more worried about Social Assistance.”)

·        Do not worry at this point about whether your hypothesis is valid.  Edward Lascher, a political scientist at Sacramento State University, remarked recently that “finding out that you are wrong is almost never uninteresting.”   

 

2.         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.