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Home arrow Environment arrow Research Methods in Anthropology: Qualitative and Quantitative Approaches


Whenever you define a variable operationally, you do so at some level of measurement. Most social scientists recognize the following four levels of measurement, in ascending order: nominal, ordinal, interval, and ratio. The general principle in research is: Always use the highest level of measurement that you can. (This principle will be clear by the time you get through the next couple of pages.)

Nominal Variables

A variable is something that can take more than one value. The values of a nominal variable comprise a list of names (from the Latin nomen for name). You can list religions, occupations, and ethnic groups; and you can list fruits, emotions, body parts, things to do on the weekend, baseball teams, rock stars . . . the list of things you can list is endless.

All of the following questions produce nominal data: In what country were you born? Are you healthy? On the whole, do you think the economy is in good shape? Is Bangladesh a poor country? Is Switzerland a rich country? What is your sex?

For sex, you can assign the numeral 1 to men and 2 to women, but gender will still be a qualitative, nominal variable. The number 2 happens to be twice as big as the number 1,

BOX 2.3


Operational definitions permit replication of research and the accumulation of knowledge. The Attitudes Toward Women Scale (AWS), for example, was developed by Janet Spence and Robert Helmreich in 1972 (Spence and Helmreich 1972, 1978) and has been used in about 400 studies since then, including some 70 dissertations. Many of those studies involved American college students and, as you'd guess, attitudes toward women have became more liberal/feminist over the years.

By 1990, men's average score on the AWS was about the same as women's average score in 1975 (Twenge 1997). In other words, men's attitudes changed but lagged those of women by 15 years. (These data, remember, reflect the attitudes of college students—the quarter of the population whom we expect to be at the vanguard of social change.)

Some of the items on the AWS seem pretty old-fashioned today. For example, in one item, people are asked how much they agree or disagree with the idea that ''women should worry less about their rights and more about becoming good wives and mothers.'' You probably wouldn't use that item if you were building an attitudes-toward-women scale today, but keeping the original, 1972 AWS intact over all this time lets us track attitudes toward women over time. (For an assessment of the AWS, see Loo and Thorpe 1998, 2005.)

but this fact is meaningless with nominal variables. You can’t add up all the 1s and 2s and calculate the ‘‘average sex’’ any more than you can add up all the telephone numbers in the Chicago phone book and get the average phone number.

Assigning numbers to things does make it easier to do certain kinds of statistical analysis on qualitative data—more on this in chapters 19 (on coding text) and 21 (on regression).

The following survey item is an operationalization of the nominal variable called “religious affiliation”:

  • 26a. Do you identify with any religion? (check one)
  • ? Yes
  • ? No

If you checked ‘‘yes,’’ then please answer question 26b.

  • 26b. What is your religion (check one):
    • ? Protestant
    • ? Catholic
    • ? Jewish
    • ? Moslem
    • ? Other religion

This operationalization of the variable “religious affiliation” has two important characteristics: It is exhaustive and mutually exclusive. The famous ‘‘other’’ category in nominal variables makes the list exhaustive—that is, all possible categories have been named in the list—and the instruction to ‘‘check one’’ makes the list mutually exclusive. (More on this in chapter 9 when we discuss questionnaire design.)

‘‘Mutually exclusive’’ means that things can’t belong to more than one category of a nominal variable at a time. We assume, for example, that people who say they are Catholic generally don’t say they are Moslem. I say ‘‘generally’’ because life is complicated and variables that seem mutually exclusive may not be. Some citizens of Lebanon have one Catholic and one Moslem parent and may think of themselves as both Moslem and Catholic.

Most people think of themselves as either male or female, but not everyone does. The prevalence of transsexuals in human populations is not known precisely, but worldwide, it is likely to be between one in ten thousand and one in a hundred thousand for male- to-female transsexuals (biological males whose gender identity is female) and between one in a hundred thousand and one in four hundred thousand for female-to-male transsexuals (Cohen-Kettenis and Gooren 1999).

Most people think of themselves as a member of one so-called race or another, but more and more people think of themselves as belonging to two or more races. In 2000, the U.S. Census offered people the opportunity to check off more than one so-called race from six choices: White, Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian and other Pacific islander, and Other. Nearly seven million people (2.4% of the 281 million in the United States in 2000) checked more than one of the six options (Grieco and Cassidy 2001).

And when it comes to ethnicity, the requirement for mutual exclusivity is just hopeless. There are Chicano African Americans, Chinese Cuban Americans, Filipino Cherokees, and so on. This just reflects the complexity of real life, but it does make analyzing data more complicated because each combination of attributes has to be treated as a separate category of the variable ‘‘ethnicity’’ or collapsed into one of the larger categories. More about this in chapters 20 and 21 when we get to data analysis.

Occupation is a nominal variable, but lots of people have more than one occupation. People can be peasant farmers and makers of fireworks displays for festivals; they can be herbalists and jewelers; or they can be pediatric oncology nurses and antique car salespeople at the same time. A list of occupations is a measuring instrument at the nominal level: You hold each person up against the list and see which occupation(s) he or she has (have).

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