Define and distinguish operationalization. Give an example.

Question

What will be an ideal response?

Answer 1

After we have defined our concepts in the abstract—that is, after conceptualizing—and after we have specified the specific variables we want to measure, we must develop our measurement procedures. The goal is to devise operations that actually measure the concepts we intend to measure—in other words, to achieve measurement validity.

Exhibit 4.2 represents the operationalization process in three studies. The first researcher defines her concept (binge drinking) and chooses one variable (frequency of heavy episodic drinking) to represent it. This variable is then measured with responses to a single question or indicator: “How often within the past two weeks did you consume five or more drinks containing alcohol in a row?” The second researcher defines his concept, poverty, as having two aspects or dimensions, subjective poverty and absolute poverty. Subjective poverty is measured with responses to a survey question: “Do you consider yourself to be poor?” Absolute poverty is measured by comparing family income to the poverty threshold. The third researcher decides that her concept, social class, can be indicated with three measured variables: income, education, and occupational prestige.

An Operation is a procedure for identifying or indicating the value of cases on a variable

Operationalization is the process of specifying the operations that will indicate the value of a variable for each case

An Indicator is the question or other operation used to indicate the value of cases on a variable

Exhibit 4.2 Concepts, Variables, and Indicators



Good conceptualization and operationalization can prevent confusion later in the research process. For example, a researcher may find that substance abusers who join a self-help group are less likely to drink again than those who receive hospital-based substance abuse treatment. But what is it about these treatment alternatives that is associated with successful abstinence? Level of peer support? Beliefs about the causes of alcoholism? Financial investment in the treatment? If the researcher had considered such aspects of the concept of substance abuse treatment before collecting her data, she might have been able to measure different elements of treatment and then identify which, if any, were associated with differences in abstinence rates. Because she did not measure these variables, she will not contribute as much as she might have to our understanding of substance abuse treatment.

Social researchers have many options for operationalizing their concepts. Measures can be based on activities as diverse as asking people questions, reading judicial opinions, observing social interactions, coding words in books, checking census data, enumerating the contents of trash receptacles, or drawing urine and blood samples. We focus here on the operations of using published data, asking questions, observing behavior, and using unobtrusive means of measuring people’s behavior and attitudes.

Using Available Data

Government reports are rich and readily accessible sources of criminal justice data, as are datasets available from nonprofit advocacy groups, university researchers, and some private businesses. For example, law enforcement and health statistics provide several community-level indicators of substance abuse (Gruenewald, Treno, Taff, & Klitzner, 1997). Statistics on arrests for the sale and possession of drugs, drunk driving arrests, and liquor law violations (such as sales to minors) can usually be obtained on an annual basis, and often quarterly, from local police departments or state crime information centers.

Still, indicators such as these cannot be compared across communities or over time without reviewing carefully how they were constructed in each community (Gruenewald et al., 1997). We also cannot assume that available data are accurate, even when they appear to measure the concept in which we are interested in a way that is consistent across communities.

Government statistics that are generated imposes through a central agency such as the U.S. Census Bureau are usually of high quality, but caution is still warranted when using official data. Data accuracy is more of an issue for data collected by local levels of government. For example, the Uniform Crime Reports (UCR) program administered by the Federal Bureau of Investigation (FBI) standard classification criteria, with explicit guidelines and regular training at the local level, but data are still inconsistent for many crimes. Different jurisdictions vary in their definition of terms such as “more than necessary force” and even in the classification of offenses as aggravated or simple assaults (Mosher, Miethe, & Phillips, 2002). The new National Incident-Based Reporting System (NIBRS), mentioned in Chapter 1, corrects some of the problems with the UCR, but it requires much more training and documentation and has not yet been adopted by all jurisdictions (Mosher et al., 2002