ResearchMethods/ResearchDesign/Basics
Contents
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. Broadly speaking, research design in political science can be divided into exploratory and explanatory. The differences between these types of research design are summarised in the table below.
| Types of research design | ||
|---|---|---|
| Exploratory research design | Explanatory research design | |
| Research questions | What, where, who, how much, how many | Why, How |
| Example of research questions |
|
|
| Common design | Survey, Archival | Comparative, Narrative, Case study |
| Common methods | Experimental, Statistical | Thematic analysis |
Basics of research design
A research project is meant to contribute to theory. You have learned about the two dominant forms of theory in political science, descriptive and normative. Whether it contributes to descriptive theory or normative theory, we should have a look at how such theories are structured, and how your research would contribute to them.
let's look at a research question asked as part of a descriptive theory: Why do some people vote for the Labour party, while others vote for the Greens?. The researcher would look for differences between the two groups of voters.
A characteristic that differs one individual (or "aggregate," such as state, country, etc.) from another is called a variable. If a characteristic does not differ them, it 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.
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 “Labour,” “Green,” and so on.
The researcher 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, 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).
In a way, the “independent variable” is like the cause, and the “dependent variable” is like the effect. But just because the two variables are related, however, does not necessarily mean that one causes the other, even indirectly.
Compare your answer with the suggested solution below.
Scientific hypotheses versus opinions
You can find plenty of 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 than men to vote for Greens and men more likely than women to vote Liberals.
Scientific research is different from everyday discussions that attempt to explain politics in two ways: the source of hypotheses, and their testing.
Source of hypotheses Anyone who follows politics will probably form 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. But social scientists develop hypotheses more systematically by studying the scholarly literature for the results of previous research, as well as the other sources mentioned. Why? Two main reasons:
- Questions are based on prior knowledge: Usually, 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 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. It is a fact that, in the US, 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 analysing the next presidential contest. [1][2] In addition to not being theory-driven, the pattern did not hold in 2004 or 2012, when Presidents Bush and Obama won reelection despite Redskin losses at home on the Sundays preceding election day.
- Results are tested: 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.
Definitions for hypothesis testing
Testing a hypothesis requires, among other things, defining the terms in the hypothesis. This needs to be done at two different levels.
- Conceptual definition. We need to know, and communicate to others, what our independent and dependent variables mean. What 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 far from clear. If we are to study political ideology, for example, we need to spell out with as much precision as possible what the concept of ideology means in the context of our research.
- Operational definition. For hypotheses to be tested, we need to come up with measurements of our variables. An operational definition is 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 sources
The data used in research can come from a wide variety of sources. If we gather the data ourselves, the analysis of those data in order to test hypotheses that we have formulated is called primary analysis. Often, however, this approach is 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 organisation. Often, secondary analysis of data (that is, analysis of data originally gathered for other purposes) will suit our needs far better. There are many sources of data which can be used for that. You can start with the ones we cover here later in the subject.
To facilitate secondary analysis, the US Inter-university Consortium for Political and Social Research (ICPSR)[3] 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).
Political sciences and natural sciences
There are several differences between political science and the natural sciences. These differences mean that theories in political sciences are less 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), political science has tendencies. Because there are no laws, it gets much harder to to develop theories. For example, just because the outcomes of past US presidential elections have been closely correlated with the state of the economy, does not mean that the same will necessarily hold in the next US election.
Dealing with tendencies rather than with laws means that, usually, (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, "the science of uncertainty," [4] which provides us with 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, anyone who runs for a major elected office in an economically developed democracy would not consider embarking on a campaign without consulting experts in survey research, a signature social science method.
Research design basics: summary of key terms
- Variable: A characteristic that differs one individual (or "aggregate," such as state, country, etc.) from another.
- Constant: A characteristic that does not differ between individuals or aggregates.
- Hypothesis: A statement positing a relationship between two variables. Hypotheses have three elements:
- A dependent variable: The variable that the research is trying to explain.
- 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.
- Conceptual definition of a variable: What the variable (independent and dependent) means. What is the idea in our mind when we use a term?
- Operational definition of a variable: Measurements of the variables, stated in a way that can be directly measured by data.
References
- ↑ David Juran,“Continuous Distributions and Portfolio Analysis,” Managerial Statistics http://www.columbia.edu/~dj114/part3.doc, 105. Accessed August 23, 2010
- ↑ Snopes.com, "Winning Tradition," Politics http://www.snopes.com/politics/ballot/redskins.asp, November 4, 2008. Accessed August 23, 2010
- ↑ Originally the ICPR. “Social” was added in 1976.
- ↑ Harold Wainer, “Require a Statistics Course,” Academic Questions.25 (Winter 2012): 526
Credits
John L. Korey 2013, POLITICAL SCIENCE AS A SOCIAL SCIENCE, Introduction to Research Methods in Political Science: The POWERMUTT* Project, [1]
