Paul M. Kellstedt

Personal efficacy, the information environment, and attitudes toward global warming and climate change in the United States.

Despite the growing scientific consensus about the risks of global warming and climate change, the mass media frequently portray the subject as one of great scientific controversy and debate. And yet previous studies of the mass publics subjective assessments of the risks of global warming and climate change have not sufficiently examined public informedness, public confidence in climate scientists, and the role of personal efficacy in affecting global warming outcomes. By examining the results of a survey on an original and representative sample of Americans, we find that these three forces-informedness, confidence in scientists, and personal efficacy-are related in interesting and unexpected ways, and exert significant influence on risk assessments of global warming and climate change. In particular, more informed respondents both feel less personally responsible for global warming, and also show less concern for global warming. We also find that confidence in scientists has unexpected effects: respondents with high confidence in scientists feel less responsible for global warming, and also show less concern for global warming. These results have substantial implications for the interaction between scientists and the public in general, and for the public discussion of global warming and climate change in particular.

Policy Mood and Political Sophistication: Why Everybody Moves Mood

This article presents evidence that both micro (individual level) and macro (aggregate level) theories of public opinion overstate the importance of political sophistication for opinion change. It is argued that even the least politically sophisticated segment of society receives messages about the economy and uses this information to update attitudes about political issues. To test this hypothesis, the authors have used General Social Survey data to construct a 31-item measure of policy mood, disaggregated by political sophistication, that spans from 1972 to 2004. They found that all the subgroups generally changed opinion at the same time, in the same direction, and to about the same extent. Furthermore, they show that groups at different sophistication levels change opinions for predominantly the same reasons.

The Fundamentals of Political Science Research: Descriptive Statistics and Graphs

OVERVIEW Descriptive statistics and descriptive graphs are what they sound like – they are tools that describe variables. These tools are valuable, because they can summarize a tremendous amount of information in a succinct fashion. In this chapter we discuss some of the most commonly used descriptive statistics and graphs, how we should interpret them, how we should use them, and their limitations. KNOW YOUR DATA In Chapter 5 we discussed the measurement of variables. A lot of thought and effort goes into the measurement of individual variables. Once measurement has been conducted, it is important for the researcher to get a good idea of the types of values that the individual variables take on before moving to testing for causal connections between two or more variables. What do “typical” values for a variable look like? How tightly clustered (or widely dispersed) are the these values? Before proceeding to test for theorized relationships between two or more variables, it is essential understand the properties and characteristics of each variable. To put it differently, we want to learn something about what the values of each variable “look like.” How do we accomplish this? One possibility is to list all of the observed values of a measured variable.

The Fundamentals of Political Science Research: 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 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 todays political scientists were attracted to this discipline as undergraduates because of intense interests in a particular issue or candidate.

The Fundamentals of Political Science Research: Getting to Know Your Data: Evaluating Measurement and Variations

OVERVIEW Although what political scientists care about is discovering whether causal relationships exist between concepts, what we actually examine is statistical associations between variables. Therefore it is critical that we have a clear understanding of the concepts that we care about so we can measure them in a valid and reliable way. In this chapter we focus on two critical tasks in the process of evaluating causal theories: measurement and descriptive statistics. As we discuss the importance of measurement, we use several examples from the political science literature, such as the concept of political tolerance. We know that political tolerance and intolerance is a “real” thing – that it exists to varying degrees in the hearts and minds of people. But how do we go about measuring it? What are the implications of poor measurement? Descriptive statistics and descriptive graphs, which represent the second focus of this chapter, are what they sound like – they are tools that describe variables. These tools are valuable because they can summarize a tremendous amount of information in a succinct fashion. In this chapter we discuss some of the most commonly used descriptive statistics and graphs, how we should interpret them, how we should use them, and their limitations. I know it when I see it. – Associate Justice of the United States Supreme Court Potter Stewart, in an attempt to define “obscenity” in a concurring opinion in Jacobellis v. Ohio (1964) These go to eleven. – Nigel Tufnel (played by Christopher Guest), describing the volume knob on his amplifier, in the movie This Is Spinal Tap

The Fundamentals of Political Science Research: Evaluating Causal Relationships

OVERVIEW Modern political science fundamentally revolves around establishing whether there are causal relationships between important concepts. This is rarely straightforward, and serves as the basis for almost all scientific controversies. How do we know, for example, if economic development causes democratization, or if democratization causes economic development, or both, or neither? To speak more generally, if we wish to evaluate whether or not some X causes some Y , we need to cross four causal hurdles: (1) Is there a credible causal mechanism that connects X to Y ? (2) Can we eliminate the possibility that Y causes X ? (3) Is there covariation between X and Y ? (4) Have we controlled for all confounding variables Z that might make the association between X and Y spurious? Many people, especially those in the media, make the mistake that crossing just the third causal hurdle – observing that X and Y covary – is tantamount to crossing all four. In short, finding a relationship is not the same as finding a causal relationship, and causality is what we care about as political scientists. I would rather discover one causal law than be King of Persia. – Democritus (quoted in Pearl 2000) CAUSALITY AND EVERYDAY LANGUAGE Like that of most sciences, the discipline of political science fundamentally revolves around evaluating causal claims. Our theories – which may be right or may be wrong – typically specify that some independent variable causes some dependent variable.

The Fundamentals of Political Science Research: The Art of Theory Building

OVERVIEW In this chapter we discuss the art of theory building. Unfortunately there is no magical formula or cookbook for developing good theories about politics. But there are strategies for developing theories that will help you to develop good theories. We discuss these strategies in this chapter. GOOD THEORIES COME FROM GOOD THEORY-BUILDING STRATEGIES In Chapter 1 we discussed the role of theories in developing scientific knowledge. From that discussion, it is clear that a “good” theory is one that, after going through the rigors of the evaluation process, makes a contribution to scientific knowledge. 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, empirical, nonnormative, general, and parsimonious. This is a tall order, and a logical question to ask at this point is “How do I come up with such a theory?” Unfortunately, there is neither an easy answer nor a single answer. Instead, what we can offer you is a set of strategies. “Strategies?” you may ask. Imagine that you were given the following assignment: “Go out and get struck by lightning.” There is no cut-and-dried formula that will show you how to get struck by lightning, but certainly there are actions that you can take that will make it more likely.