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 8 The Scientific Study of Politics denominations in which the variable occurs.
It is easier to understand the process of turning concepts into variables by using an example of an entire theory. 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. 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. For instance, higher causes lower or, as the case may be, 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. These boxes are designed to help you see if you are understanding the material that we have covered up to the point where they appear.
In addition, if the answer is original and thought provoking, then you may really be on to something. 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 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.
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.
Our causal theory is that a stronger economic performance causes the incumbent vote to be higher. We can measure economic performance in a variety of ways. These measures include inflation, unemployment, real economic growth, and many others.
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 Chapters 5 and 6, which are devoted entirely to evaluating different variable measurements and variation in variables. 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.
In order to make our measure of this dependent variable comparable across cases, votes for third-party candidates have been removed from this measure. We could place each US presidential election on the graph in Figure 1. 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. 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. We can see that, at the far left end of the horizontal axis, the value is — This would mean that the US economy had shrunk by 20 percent over the past year, which would represent a very poor performance to say the least.
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.
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. 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. 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. A positive relationship is one for which higher values of the independent variable tend to coincide with higher values of the dependent variable. The best way to do so is to draw a picture like Figure 1. This is what we have in Figure 1. As we move from left to right on the horizontal axis in Figure 1.
Two hypothetical cases 0 10 20 30 40 50 60 70 80 90 Unemployment Percentage Figure 1. 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? 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 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. 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, 1. The best way to translate our theories into hypotheses is to draw a picture like Figure 1. The first step is to label the horizontal axis with the variable label for the independent variable as operationalized and then label the left and 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 minimum and maximum 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 the two resulting points on our figure, we can tell whether our hypothesized relationship is positive or negative. YOUR TURN: Developing another hypothesis to test the theory of economic voting Think of a measure of the economy that is different from the two, economic growth and unemployment, that we have considered so far. Draw a picture like those in Figures 1. 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.
Chapters 8—12 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. Take a minute or two to think about what other variables, aside from economic performance, you believe might be causally related to US 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. What other variables, aside from economic performance, might be causally related to US presidential election outcomes? 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 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. Rogers, , p. 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. As we do this, try to keep in mind our larger purpose — trying to advance the state of scientific knowledge about politics.
Consider only empirical evidence. Avoid normative statements. Pursue both generality and parsimony. Focus on Causality 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 is the case that, when we observe higher values of variable A, we generally also observe lower values of variable B. More on this in Chapter 3. Suppose that we are looking at data on the murder rate number 7 A closely related term is correlation. For now we use these two terms interchangeably. In Chapter 8, 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.
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.
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?
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. We are likely to be somewhat familiar with empirical patterns relating to the dependent variables for which we are developing causal theories.
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 dependent variable. Although this strategy for theory development relies on data, it should not be done without thinking about the underlying causal processes. A logical extension of this is that your ideology or partisan identification or metaphysics cannot be a source of proof that something is or is not true.
And closely related to this, as scientists, we should avoid normative statements. Normative statements are statements about how the world ought to be. 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. 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 the United States of homelessness normative , we need to understand the pathways 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. 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. These two goals can come into conflict. 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. So, if we are comparing two theories, the theory that is simpler would be the more parsimonious. 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.
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 the second section of this book, we expand on the basic tools that political scientists need to test their theories. The third and final section of this book introduces the critical concepts of the regression model.
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? Think about something in the political world that you would like to better understand. Try to think about this as a variable with high and low values.
This is your dependent variable at the conceptual level. Now think about what might cause the values of your dependent variable to be higher or lower. Try to phrase this in terms of an independent variable, also at the conceptual level. Write a paragraph about these two variables and your theory about why they are causally related to each other. Identify something in the world that you would like to see happen normative. What scientific knowledge empirical and causal would help you to pursue this goal?
In a society in which people have more political rights, the victims of corrupt business practices will work through the system to get things corrected. As a result, countries in which people have more political rights will have less corruption. In countries in which there is less corruption, there will be more economic investment and more economic success.
Identify at least two causal claims that have been made in the preceding statement. For each causal claim, identify which variable is the independent variable and which variable is the dependent variable. These causal claims should be stated in terms of one of the following types of phrases in which the first blank should be filled by the independent variable and the second blank should be filled by the dependent variable: causes higher causes lower higher causes higher 6.
Draw a graph like Figure 1. For each of your figures, do the following: Start on the left-hand side of the horizontal axis of the figure. This should represent a low value of the independent variable.
What value of the dependent variable would you expect to find for such a case? Put a dot on your figure that represents this expected location. Now do the same for a case with a high value of the independent variable. Draw a line that connects these two points and write a couple of sentences that describe this picture. Find an article in a political science journal that contains a model of politics. Provide the citation to the article, and answer the following questions: a What is the dependent variable?
For each of the following statements, identify which, if any, rule s of the road to scientific knowledge about politics has been violated: 24 The Scientific Study of Politics a This study of the relationship between economic development and the level of autocracy is important because dictatorships are bad and we need to understand how to get rid of them.
Unfortunately there is no magical formula or cookbook for developing good theories about politics. But there are strategies that will help you to develop good theories.
We discuss these strategies in this chapter. Amat victoria curam. Victory loves preparation. In other words, a good theory is one that changes the way that we think about some aspect of the political world.
We also know from our discussion of the rules of the road that we want our theories to be causal, not driven by data alone, empirical, nonnormative, general, and parsimonious. Instead, what we can offer you is a set of strategies.
The first step is to look at a weather map and find an area where there is thunderstorm activity; and if you were to go to such an area, you would increase your likelihood of getting struck. You would be even more likely to get struck by lightning if, once in the area of thunderstorms, you climbed to the top of a tall barren hill.
But you would be still more likely to get struck if you carried with you a nine iron and, once on top of the barren hill, in the middle of a thunderstorm, you held that nine iron up to the sky.
The point here is that, although there are no magical formulae that make the development of a good theory or getting hit by lightning a certain event, there are strategies that you can follow to increase the likelihood of it happening. So a reasonable place to begin to answer the question of how one evaluates a new theory is to think about how that theory, if supported in empirical testing, would contribute to scientific knowledge. One of the main ways in which theories can be evaluated is in terms of the questions that they answer.
If the question being answered by a theory is interesting and important, then that theory has potential. Most of the influential research in any scientific field can be distilled into a soundbite-sized statement about the question to which it offers an answer, or the puzzle for which it offers a solution.
Consider, for example, the ten most-cited articles published in the American Political Science Review between and It is worth noting that, of these ten articles, all but one has as its main motivation the answer to a question or the solution 2 This list comes from an article Sigelman, published by the editor of the journal in which well-known researchers and some of the original authors reflected on the influence of the 20 most-cited articles published in the journal during that time period.
How do innovations in governance spread across US states? How do economic conditions impact US national elections? How do constituent attitudes influence the votes of US representatives? How do institutions shape politics? What are the necessary conditions for stable democratic politics? What models should researchers use when they have pooled time-series data?
Why has the government share of economic activity increased in some nations? How does social mobilization shape politics in developing nations? It also provides a useful way of evaluating any theory that we are developing.
As we consider different strategies for developing theories, we will refer back to this basic idea of answering questions. Because theories are designed to explain variation in the dependent variable, identifying some variation that is of interest to you is a good jumping-off point. In Chapter 4 we present a discussion of two of the most common research designs — cross-sectional and time-series observational studies — in some detail.
For now, it is useful to give a brief description of each in terms of the types of variation in 3 The Beck and Katz paper, which is one of the most influential technical papers in the history of political science, is the exception to this. These should help clarify the types of variation to consider as you begin to think about potential research ideas. When we think about measuring our dependent variable, the first things that we need to identify are the time and spatial dimensions over which we would like to measure this variable.
The time dimension identifies the point or points in time at which we would like to measure our variable. Depending on what we are measuring, typical time increments for political science data are annual, quarterly, monthly, or weekly measures. The spatial dimension identifies the physical units that we want to measure. There is a lot of variability in terms of the spatial units in political science data. If we are looking at survey data, the spatial unit will be the individual people who answered the survey known as survey respondents.
If we are looking at data on US state governments, the typical spatial unit will be the 50 US states. Data from international relations and comparative politics often take nations as their spatial units. Throughout this book, we think about measuring our dependent variable such that one of these two dimensions will be static or constant. This means that our measures of our dependent variable will be of one of two types. The first is a crosssectional measure, in which the time dimension is the same for all cases and the dependent variable is measured for multiple spatial units.
The second is a time-series measure, in which the spatial dimension is the same for all cases and the dependent variable is measured at multiple points in time.
Although it is possible for us to measure the same variable across both time and space, we strongly recommend thinking in terms of variation across only one of these two dimensions as you attempt to develop a theory about what causes this variation. We can tell that this variable is measured cross-sectionally, because it varies across spatial units nations but does not vary across time it is measured for the year for each case. When we measure variables across spatial units like this, we have to be careful to choose appropriate measures that are comparable across spatial units.
To better understand this, imagine that we had measured our dependent variable as the amount of money that each nation spent on its military. The problem would be that country 4 As we mentioned in Chapter 1, we will eventually theorize about multiple independent variables simultaneously causing the same dependent variable to vary. Confining variation in the dependent variable to a single dimension helps to make such multivariate considerations tractable.
We would need to know the currency exchange rates in order to make these comparable across nations. Using currency exchange rates, we would be able to convert the absolute amounts of money that each nation had spent into a common measure. Why, you might ask, would we want to measure military spending as a percentage of GDP? The answer is that this comparison is our attempt to measure the percentage of the total budgetary effort available that a nation is putting into its armed forces. Some nations have larger economies than others, and this measure allows us to answer the question of how much of their total economic activity each nation is putting toward its military.
We would start to get into real problems if we plotted pairs of variables and then developed a theory only once we observed a pair of variables that varied together. If this still seems like we are getting too close to our data before developing our theory, we could develop a theory about military spending using Figure 2.
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. We aim to make the common technical language of political science accessible to you. We want you to be better able to evaluate such claims critically. 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.
Under this alternative way, for example, a course offered in 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 prob- ably be pretty boring.
An even bigger problem, though, is that the political world is constantly changing. In the EU is made up of 27 member nations and has some new governing institutions and rules that are different from what they were in By contrast, a theoretical approach to politics helps us to better understand why changes have come about and their likely impact on EU politics.
Whitten Excerpt More information 3 1. 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 conclude this chapter with a brief overview of the structure of this book. Scientists are lumped into different disciplines that develop standards for evaluating evidence. 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. Once a theory has been developed, we can restate it into 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 expect to observe if our theory was incorrect.
Kellstedt, P. Kellstedt, Paul M. There are different options as regards the type and number of cases needed to develop a research design. Can you think of a research Author : Paul M. Whitten's best-selling textbook. Built in parallel with the main text, this workbook teaches students to apply the techniques they learn in each chapter by reproducing the analyses and results from each lesson using R.
Students will also learn to create all of the tables and figures found in the textbook, leading to an even greater mastery of the core material. This accessible, informative, and engaging companion walks through the use of R step-by-step, using command lines and screenshots to demonstrate proper use of the software.
With the help of these guides, students will become comfortable creating, editing, and using data sets in R to produce original statistical analyses for evaluating causal claims. End-of-chapter exercises encourage this innovation by asking students to formulate and evaluate their own hypotheses. Includes all testable terms, concepts, persons, places, and events. This item is printed on demand. Virtually all of the testable terms, concepts, persons, places, and events from the textbook are included.
Built in parallel with the main text, this workbook teaches students to apply the techniques they learn in each chapter by reproducing the analyses and results from each lesson using Stata. This accessible, informative, and engaging companion walks through the use of Stata step-by-step, using command lines and screenshots to demonstrate proper use of the software. With the help of these guides, students will become comfortable creating, editing, and using data sets in Stata to produce original statistical analyses for evaluating causal claims.
Kellstedt Publisher: Cambridge University Press ISBN: Category: Political Science Page: View: Read Now » This textbook introduces the scientific study of politics, supplying students with the basic tools to be critical consumers and producers of scholarly research. Using case studies to explain the fundamentals of the research process, the authors tell how to formulate hypotheses, devise measurement strategies, develop a research design, conduct a literature review, make empirical observations, and write a research report.
They also discuss survey research techniques, such as mailed questionnaires and personal and telephone interviews; analysis of written records; ethical issues when subjects are indirectly observed; and univariate, bivariate, and multivariate data analysis.
Science and the study of the presidency 3. The presidency: background and foundations 4. Theories of presidential power 5. The presidential selection process 6.
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