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Variations between the Extremes A Note on Dimensions







Degree of precision is a second consideration in operationalizing variables. What it boils down to is how fine you will make distinctions among the various possible attributes composing a given variable. Does it matter for your purposes whether a person is 17 or 18 years old, or could you conduct your inquiry by throwing them together in a group labeled 10 to 19 years old? Don't answer too quickly. If you wanted to study rates of voter registration and participation, you'd definitely want to know whether the people you studied were old enough to vote. In general, if you're going to measure age, you must look at the purpose and procedures of your study and decide whether fine or gross differences in age are important to you. In a survey, you'll need to make these decisions in order to design an appropriate questionnaire. In the case of in-depth interviews, these decisions will condition the extent to which you probe for details.

The same thing applies to other variables. If you measure political affiliation, will it matter to your inquiry whether a person is a conservative Democrat rather than a liberal Democrat, or will it be sufficient to know the party? In measuring religious affiliation, is it enough to know that a person is Protestant, or do you need to know the denomination? Do you simply need to know whether or not a person is married, or will it make a difference to know if he or she has never married or is separated, widowed, or divorced?

There is, of course, no general answer to such questions. The answers come out of the purpose of a given study, or why we are making a particular measurement. I can give you a useful guideline, though. Whenever you're not sure how much detail to pursue in a measurement, get too much rather than too little. When a subject in an in-depth interview volunteers that she is 37 years old, record " 37" in your notes, not " in her thirties." When you're analyzing the data, you can always combine precise attributes into more general categories, but you can never separate any variations you lumped together during observation and measurement.


We've already discussed dimensions as a characteristic of concepts. When researchers get down to the business of creating operational measures of variables, they often discover—or worse, never notice—that they're not exactly clear about which dimensions of a variable they're really interested in. Here's an example.

Let's suppose you're studying people's attitudes toward government, and you want to include an examination of how people feel about corruption. Here are just a few of the dimensions you might examine:

• Do people think there is corruption in government?

• How much corruption do they think there is?

• How certain are they in their judgment of how much corruption there is?

• How do they feel about corruption in government as a problem in society?

• What do they think causes it?

• Do they think it's inevitable?

• What do they feel should be done about it?

• What are they willing to do personally to eliminate corruption in government?

• How certain are they that they would be willing to do what they say they would do?

The list could go on and on—how people feel about corruption in government has many dimensions. It's essential to be clear about which ones are important in our inquiry; otherwise, you may measure how people feel about corruption when you really wanted to know how much they think there is, or vice versa.

Once you have determined how you're going to collect your data (for example, survey, field research) and have decided on the relevant range of variation, the degree of precision needed between the extremes of variation, and the specific dimensions of the variables that interest you, you may have another choice: a mathematical-logical one. That is, you may need to decide what level of measurement to use. To discuss this point, we need to take another look at attributes and their relationship to variables.


 


134. Chapter 5: Conceptualization, Operationalization, and Measurement


Defining Variables and Attributes

An attribute, you'll recall, is a characteristic or quality of something. Female is an example. So is old or student. Variables, on the other hand, are logical sets of attributes. Thus, gender is a variable composed of the attributes female and male.

The conceptualization and operationalization processes can be seen as the specification of variables and the attributes composing them. Thus, in the context of a study of unemployment, employment status is a variable having the attributes employed and unemployed; the list of attributes could also be expanded to include the other possibilities discussed earlier, such as homemaker.

Every variable must have two important qualities. First, the attributes composing it should be exhaustive. For the variable to have any utility in research, we must be able to classify every observation in terms of one of the attributes composing the variable. We'll run into trouble if we conceptualize the variable political party affiliation in terms of the attributes Republican and Democrat, because some of the people we set out to study will identify with the Green Party, the Reform Party, or some other organization, and some (often a large percentage) will tell us they have no party affiliation. We could make the list of attributes exhaustive by adding other and no affiliation. Whatever we do, we must be able to classify every observation.

At the same time, attributes composing a variable must be mutually exclusive. Every observation must be able to be classified in terms of one and only one attribute. For example, we need to define employed and unemployed in such a way that nobody can be both at the same time. That means being able to classify the person who is working at a job but is also looking for work. (We might run across a fully employed mud wrestler who is looking for the glamour and excitement of being a social re-

nominal measure A variable whose attributes have only the characteristics of exhaustiveness and mutual exdusiveness. In other words, a level of measurement describing a variable that has attributes that are merely different, as distinguished from ordinal, interval, or ratio measures. Gender would be an example of a nominal measure.


searcher.) In this case, we might define the attributes so that employed takes precedence over unemployed, and anyone working at a job is employed regardless of whether he or she is looking for something better.

Levels of Measurement

Attributes operationalized as mutually exclusive and exhaustive may be related in other ways as well. For example, the attributes composing variables may represent different levels of measurement. In this section, we'll examine four levels of measurement: nominal, ordinal, interval, and ratio.

Nominal Measures

Variables whose attributes have only the characteristics of exhaustiveness and mutual exdusiveness are nominal measures. Examples include gender, religious affiliation, political party affiliation, birthplace, college major, and hair color. Although the attributes composing each of these variables—as male and female compose the variable gender —are distinct from one another (and exhaust the possibilities of gender among people), they have no additional structures. Nominal measures merely offer names or labels for characteristics.

Imagine a group of people characterized in terms of one such variable and physically grouped by the applicable attributes. For example, say we've asked a large gathering of people to stand together in groups according to the states in which they were born: all those born in Vermont in one group, those born in California in another, and so forth. The variable is place of birth; the attributes are born in California, born in Vermont, and so on. All the people standing in a given group have at least one thing in common and differ from the people in all other groups in that same regard. Where the individual groups form, how close they are to one another, or how the groups are arranged in the room is irrelevant. All that matters is that all the members of a given group share the same state of birth and that each group has a different shared state of birth. All we can say about two people in terms of a nominal variable is that they are either the same or different.



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