# Chapter Summary

#### Chapter Objectives

4.1: Identify the types of variables involved in an explanation for a phenomenon.
4.2: Explain the characteristics of good hypotheses.
4.3: Discuss the role of defining concepts in research studies.
4.4: Discuss the validity and reliability of measures and ways to demonstrate them.
4.5: Discuss measurement precision and levels of measurement.
4.6: Distinguish between categorical and quantitative measures.

• After choosing a research question, the initial steps of an empirical research project include the following:
• Proposing a suitable explanation for the phenomena under study.
• Formulating testable hypotheses.
• Defining the concepts identified in the hypotheses.
• Devising measurement strategies.
• Variables are used to specify how two or more variables are related in an effort to explain the phenomena of interest.
• An independent variable is thought to influence, affect, or cause variation in another variable.
• A dependent variable is thought to depend upon or be caused by variation in an independent variable.
• A variable is a concept with variation, while a constant is a concept without variation.
• In general, more than one independent variable is needed to adequately explain political phenomena.
• A variable that occurs prior to all other variables is referred to as an antecedent variable, while a variable that occurs closer in time to the dependent variable is called an intervening variable.
• An arrow diagram can be used to present and keep track of variables and complicated explanations.
• When we assert that variation in independent variable X causes variation in dependent variable Y, we are making three assertions:
• that X and Y covary,
• that the change in X precedes the change in Y, and
• that covariation between X and Y is not spurious or a coincidence.
• A hypothesis is an explicit statement about the relationship between phenomena that formalizes the researcher’s informed guess. Data analysis is used to test the hypothesis as it may be correct or incorrect.
• There are six characteristics of a good hypothesis. A good hypothesis should:
• be an empirical statement that formalizes educated guesses about phenomena that exist in the political world, not a statement about what the researcher wants to be true;
• explain general phenomena rather than one particular occurrence of the phenomena;
• be plausible--there should be a logical reason for thinking that the hypothesis might be confirmed by the data;
• be specific by stating the direction of the relationship between two phenomena, be it a positive or negative relationship;
• be consistent with the data by using terms that are consistent with the manner of testing; and
• be testable through feasible to obtain data that will indicate if the hypothesis is defensible.
• Hypotheses also specify a unit of analysis, or the level of political actor to which it applies (individuals, groups, states, organizations, etc.).
• Most research uses hypotheses with one unit of analysis.
• While a cross-level analysis specifying more than one unit of analysis is sometimes useful for making ecological inferences about individuals from aggregate data, in general, researchers should not mix units of analysis within a hypothesis.
• Definitions of concepts should be clear, accurate, precise, and informative, so that others may fully understand the concept as it was tested and evaluate the measurement strategy for the concept.
• Many of the concepts used in political science are fairly abstract and require careful and extensive thought to make definitions clear.
• The process of measurement is important. It provides the bridge between the proposed explanations and the empirical world they are supposed to explain.
• By specifying the operational definition of a concept, its precise meaning becomes clear and the researcher explicitly states the terms by which the concept will be measured.
• The quality of measurements is judged in regard to both their accuracy and their precision.
• Measurements may be inaccurate because they are unreliable or invalid.
• Reliability is the extent to which an experiment, test, or any measuring procedure yields the same results on repeated trials. The chapter discusses three tests of reliability: the test–retest, alternative form, and split-halves methods.
• Each test requires the researcher to compare the results of two or more tests for consistency in the answers--a more reliable measure will have more consistency.
• A valid measure is one that measures what it is supposed to measure--in other words, the degree of correspondence between the measure and the concept it is thought to measure. The chapter discusses six tests of validity, but validity is not as easy to demonstrate as reliability: face validity, content validity, construct validity, convergent construct validity, and discriminant construct validity.
• Each test confirms validity through comparison with the operational definition of a concept or with other measures of the concept of interest or related concepts. Measures that capture the full definition of the concept or produce results consistent with other measures are considered valid.
• The reliability and validity of the measures used by political scientists are seldom demonstrated to everyone’s satisfaction--most measures are partially accurate.
• The precision of a measure is captured by its level of measurement--the type of information a measure contains and the type of comparison or analysis that can be used with the measure across observations.
• There are four levels of measurement ranging from the nominal level that contains the least amount of information and lends itself to the fewest analytical tools to the ratio level that contains the most information and lends itself to the most analytical tools:
• The nominal level describes variables that indicate only a difference between categories.
• At the ordinal level, categories may be ranked in order in addition to indicating a difference between categories.
• The interval level includes all of the information of the preceding levels and adds meaningful intervals between values of the variable.
• The ratio level adds a meaningful zero to the interval level. Ratio-level variables hold the full properties of mathematics and can be used with most analytical tools.
• The level of measurement indicates the data analysis techniques that can be used and the conclusions that can be drawn about the relationships tween variables, so identifying it is very important.
• Variables with only two categories are referred to as dichotomous. They are considered to be special cases as they can be used as nominal, ordinal, or even ratio-level variables.
• Researches try to devise as high a level of measurement for their concepts as possible.
• It is easy to transform ratio-level information into ordinal-level information but transforming ordinal to ratio is not possible.
• Researchers also frequently begin an analysis with ordinal and nominal measures with quite a few categories but subsequently collapse or combine the data to create fewer categories.
• Extremely precise measures also may create problems.