Chapter 2 - Theories, Hypotheses, and Evidence - Textbook Summary PDF

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Chapter 2: Theories, Hypotheses, and Evidence Important to have good theories that can help us understand—not misunderstand—how the world works. Important to interpret evidence correctly. Introduction to Theories, Hypotheses, and Evidence Social scientists look for convincing answers to important questions about why things happen. Theories Theories are general explanations of empirical phenomena, or explanations about how the world operates. Aims to explain more than just one or two cases or examples and is backed by a considerable number of supporting facts as empirical evidence. There are two different types of theory; normative theory and empirical (or positive) theory. Normative theory deals with questions of values and moral beliefs. Empirical theory deals with empirical questions. Mostly focused on empirical theory as a general explanation of why things happen. Hypotheses Hypotheses are specific proposed explanations for why an outcome occurs. When answering research questions, hypotheses are formulated or generated to try to explain a set of facts upon further research. Hypotheses can be generated from existing theories in a deductive fashion: starting with general ideas and then testing whether they work on specific examples. It is an effort to test an observable implication of the starting theory. We can learn a great deal from so-called deviant cases, or “outliers,” that do not do as we might expect. We do not normally aim to create a hypothesis from empirical data in an inductive way—moving from specific

observations to general claims. Approach the hypothesis with an open mind, it may be supported or rejected by the research and could be wrong. Rarely, if ever, can we fully confirm or disprove a hypothesis with limited research. Theories help guide us in formulating hypotheses, and confirming hypotheses may either support or undermine theories. A specific hypothesis is generated for each research question and is put on the line to be tested in each case. After testing hypotheses for a specific study, scholars will typically offer a thesis, a claim to argue based on evidence that comes from testing a hypothesis. How Theories Emerge and Are Used Theories emerge and are used all the time to explain the world around us. In testing a hypothesis, a scholar will examine whether the evidence is consistent with such an explanation. Even if they find that the evidence is consistent, a single study doesn’t prove anything. Counterarguments will emerge. The scientific endeavour can be further pursued by testing related hypotheses to see how the theory changes as a result. Narrow in on good explanations by finding increasing evidence that certain hypotheses are consistent with the evidence while others are inconsistent. Theories have facts and evidence supporting them but that doesn’t mean that they have proof or are valid or correct in all circumstances. Often, a wrong theory can stay for a long time before it is replaced by a stronger one. Theories may ultimately fail and be rejected, but ideally theories only “die” when replaced by new ones that better explain existing evidence. Theories in political science explain tendencies and help us understand many cases, but there are almost always exceptions to the rules. Cause-and-effect relationships are general patterns, not absolute laws. As a result, building theory is an intensive process over an extended period of formulating and testing hypotheses, gathering and examining evidence, and understanding and synthesizing debates. Theories are imperfect but can be improved over time.

Types of Evidence The dominant form of evidence will be qualitative, meaning it comes from detailed accounts of historical or contemporary events. Social scientists use quantitative data such as statistics and figures to complement qualitative data as they aim to make inferences, or conclusions based on evidence, about cause and effect. Quantitative data differ from qualitative data in their presentation, but both types are used to generate and test hypotheses. In comparative politics, you will use historical accounts and data more often than you will make predictions about the future. This is because we have real evidence only for things that have happened, and not for what might happen. Hypothesis Testing The core of comparative politics is testing hypotheses about cause and effect between two or more variables. Cause-and-effect arguments are based on examining different variables and how those variables relate to one another and may depend on one another. Correlation Correlation measures the association between two variables. When two variables correlate, they are related to one another. If two variables have a positive correlation, they tend to increase together: one increases as the other increases. A negative correlation is the opposite, and means that as one variable tends to increase, the other tends to decrease. Causation Causation exists when one variable causes another. As the word because implies, answering why involves explaining causes. Without causal arguments and theories, correlations

are just patterns in search of an explanation. When we have causation, we usually have correlation, but the opposite is not true. We cannot assume that all correlations between two variables means that X leads to Y. The first of those was the causal argument that X leads to Y. However, there are some other possibilities that could also happen. 1) Definitional problems and falsifiability problem (X=Y): This involves the arguments being “too correct.” Two variables that are the same by definition. Defining two variables that are so nearly the same that the causal argument is meaningless, or tautological. This relates to the problem of falsifiability, which is the idea that for an explanation to be meaningful, it must be contestable. To argue that something is true means something only if there is a chance it could at least possibly be incorrect and could be proved wrong. 2) Reverse causality problem (X ← Y): Two variables are correlated, but the causal argument linking them may be the opposite of what is anticipated. Instead of X leading to Y, perhaps Y leads to X. 3) Endogeneity problem (X ↔ Y): The endogeneity problem is about circularity; it happens when two variables exhibit mutual or reciprocal effects. “The chicken and the egg” problem. Whether X caused Y to happen or Y caused X to happen. 4) Intervening variable problem (X → Z and Z → Y): X leads to Y, but indirectly. The effect of X on Y is mediated through another variable, Z. If we can specify the argument and its steps, we do not have an intervening variable problem. The potential problem arises when we miss an intervening variable and this leads us to a wrong interpretation. 5) Omitted variable problem (Z → X and Z → Y): We frequently miss or omit variables that should be in our analysis. X and Y are attributable to a third factor (confounding or “lurking” variable), because though it is there in the background we might not see it.

6) Spurious correlation problem (X →? ← Y): Correlate with one another even in the absence of any causal relationship. Simply no meaningful causal relationship exists. Critiques: Using Theories and Evidence Evidence can be used to enhance a theory or an argument by providing a helpful critique of the conventional wisdom. Empirical critiques have a prominent place in comparative politics, as do the theoretical critiques they enable. Empirical Critiques: Using Deviant Cases Deviant cases—those that do not fit a theory or are exceptions or outliers—are very important in advancing social science theory. These cases help us test out why a theory doesn’t work, and understand what improvements need to be made to our knowledge. They allow us to make an empirical critique of a theory because they do not support it. Theoretical Critiques: Improving Theories and Hypotheses Theoretical critiques are new ideas that improve upon the logic or reasoning of existing theories. Theory and empirical evidence constantly interact, and where deviant cases help provide an empirical critique, these can help us improve our theories. Critiques help us craft better arguments and theories. They can improve our understanding of scope of conditions, or the conditions under which an argument works. Critiques based on empirical evidence can help improve our concepts and lead to clearer understanding of what exactly we are studying. By identifying weaknesses in arguments and offering alternative explanations, critiques give us better understandings of why things happen. The Challenges of Measurement: Biases, Errors, and Validity

Beyond determining how to gather evidence and which pieces to use, we must pay attention to measures and indicators (elements or features suggesting underlying factors). Bias is a preference for one idea or perspective over another, especially a preference that may result in unbalanced use of evidence or in analytical error. It is possible to simply make measurement errors, which are either an episodic error, such as improperly recording data, or a systematic error, meaning that a measurement doesn’t fully reflect what it’s designed to measure. Measurement bias is when a measure is biased if it doesn’t produce comparable results for all observations. Perhaps the most serious form of bias for beginning researchers is seeking to confirm one’s favoured hypothesis. This can include a tendency to believe things are a certain way that we want to see them. We must ask research questions and test hypotheses fairly, by ensuring the answer is not predetermined. Even when researchers are careful not to bias their measures, we must consider the problem of measurement validity—that is, whether a given measure effectively captures or represents what is being researched. Should strive for valid measurements to the greatest extent possible. Explicitly state our reservations about our measures when we present our work. Be mindful of how our measured variables relate to our concepts and questions....