(Measures) Operational Definition
The term operational definition refers to a precise statement of how a conceptual variable is turned into a measured variable. Research can only proceed once an adequate operational defi nition has been defi ned. In some cases the conceptual variable may be too vague to be operationalized, and in other cases the variable cannot be operationalized because the appropriate technology has not been developed. For instance, recent advances in brain imaging have allowed new operationalizations of some variables that could not have been measured even a few years ago. Table 4.1 lists some potential operational definitions of conceptual variables that have been used in behavioral research. As you read through this list, note that in contrast to the abstract conceptual variables (employee satisfaction, frustration, depression), the measured variables are very specifi c. This specifi city is important for two reasons. First, more specific definitions mean that there is less danger that the collected data will be misunderstood by others. Second, specific definitions will enable future researchers to replicate the research.Converging Operations
That there are many possible measures for a single conceptual variable might seem a scientific problem. But it is not. In fact, multiple possible measures represent a great advantage to researchers. For one thing, no single operational defi nition of a given conceptual variable can be considered the best. Different types of measures may be more appropriate in different research contexts. For instance, how close a person sits to another person might serve as a measure of liking in an observational research design, whereas heart rate might be more appropriate in a laboratory study. Furthermore, the ability to use different operationalizations of the same conceptual variable allows the researcher to hone in, or to “triangulate,” on the conceptual variable of interest. When the same conceptual variable is measured using different measures, we can get a fuller and better measure of it. Because this principle is so important, we will discuss it more fully in subsequent chapters. This is an example of the use of converging operations, as discussed in Chapter 1.
The researcher must choose which operational defi nition to use in trying to assess the conceptual variables of interest. In general, there is no guarantee that the chosen measured variable will prove to be an adequate measure of the conceptual variable. As we will see in Chapter 5, however, there are ways to assess the effectiveness of the measures once they have been collected.
Conceptual and Measured Variables
The relationship between conceptual and measured variables in a correlational research design is diagrammed in Figure 4.1. The conceptual variables are represented within circles at the top of the fi gure, and the measured variables are represented within squares at the bottom. The two vertical arrows, which lead from the conceptual variables to the measured variables, represent the operational defi nitions of the two variables. The arrows indicate the expectation that changes in the conceptual variables (job satisfaction and
job performance in this example) will cause changes in the corresponding measured variables. The measured variables are then used to draw inferences about the conceptual variables.
• if the research hypothesis (that the two conceptual variables are correlated) is correct, and
• if the measured variables are adequate—that is, if there is a relationship between both of the conceptual and measured variables (the two vertical arrows in the figure)—then
• a relationship between the two measured variables (the bottom arrow in the fi gure) will be observed (cf. Nunnally, 1978).
The ultimate goal of the research is to learn about the relationship between the conceptual variables. But, the ability to learn about this relationship is dependent on the operational definitions. If the measures do not really measure the conceptual variables, then they cannot be used to draw inferences about the relationship between the conceptual variables. Thus, the adequacy of a test of any research hypothesis is limited by the adequacy of the measurement of the conceptual variables.
Nominal and Quantitative Variables
Measured variables can be divided into two major types: nominal variables and quantitative variables. A nominal variable is used to name or identify a particular characteristic. For instance, sex is a nominal variable that identifi es whether a person is male or female, and religion is a nominal variable that identifies whether a person is Catholic, Buddhist, Jewish, or some other religion. Nominal variables are also frequently used in behavioral research to indicate the condition that a person has been assigned to in an experimental research design (for instance, whether she or he is in the “experimental condition” or the “control condition”).
Nominal variables indicate the fact that people who share a value on the variable (for instance, all men or all the people in the control condition of an experiment) are equivalent in some way, whereas those that do not share the value are different from each other. Numbers are generally used to indicate the values of a nominal variable, such as when we represent the experimental condition of an experiment with the number 1 and the control condition of the experiment with the number 2. However, the numbers used to represent the categories of a nominal variable are arbitrary, and thus we could change which numbers represent which categories, or even label the categories with letters or names instead of numbers, without losing any information.
In contrast to a nominal variable, which names or identifi es, a quantitative variable uses numbers to indicate the extent to which a person possesses a characteristic of interest. Quantitative variables indicate such things as how attractive a person is, how quickly she or he can complete a task, or how many siblings she or he has. For instance, on a rating of perceived attractiveness, the number 10 might indicate greater attractiveness than the number 5.
Measurement Scales
Specifying the relationship between the numbers on a quantitative measured variable and the values of the conceptual variable is known as scaling. In some cases in the natural sciences, the mapping between the measure and the conceptual variable is quite precise. As an example, we are all familiar with the use of the Fahrenheit scale to measure temperature. In the Fahrenheit scale, the relationship between the measured variable (degrees Fahrenheit) and the conceptual variable (temperature) is so precise that we can be certain that changes in the measured variable correspond exactly to changes in the conceptual variable.
In this case, we can be certain that the difference between any two points on the scale (the degrees) refers to equal changes in the conceptual variable across the entire scale. For instance, we can state that the difference in temperature between 10 and 20 degrees Fahrenheit is exactly the same as the difference in temperature between 70 and 80 degrees Fahrenheit. When equal distances between scores on a measure are known to correspond to equal changes in the conceptual variable (such as on the Fahrenheit scale), we call the measure an interval scale.
Now consider measures of length, such as feet and inches or the metric scale, which uses millimeters, centimeters, and meters. Such scales have all of the properties of an interval scale because equal changes between the points on the scale (centimeters for instance) correspond to equal changes in the conceptual variable (length). But, measures of length also have a true zero point that represents the complete absence of the conceptual variable—zero length. Interval scales that also have a true zero point are known as ratio scales (the Kelvin temperature scale, where zero degrees represents absolute zero, is another example of a ratio scale). In addition to being able to compare intervals, the presence of a zero point on a ratio scale also allows us to multiply and divide scale values. When measuring length, for instance, we can say that a person who is 6 feet tall is twice as tall as a child who is 3 feet tall.
In most behavioral science research, the scaling of the measured variable is not as straightforward as it is in the measurement of temperature or length. Measures in the behavioral sciences normally constitute only ordinal scales. In an ordinal scale, the numbers indicate whether there is more or less of the conceptual variable, but they do not indicate the exact interval between the individuals on the conceptual variable. For instance, if you rated the friendliness of fi ve of your friends from 1 (least friendly) to 9 (most friendly), the scores would constitute an ordinal scale. The scores tell us the ordering of the people (that you believe Malik, whom you rated as a 7, is friendlier than Guillermo, whom you rated as a 2), but the measure does not tell us how big the difference between Malik and Guillermo is. Similarly, a hotel that receives a four-star rating is probably not exactly twice as comfortable as a hotel that receives a two-star rating.
Selltiz, Jahoda, Deutsch, and Cook (1966) have suggested that using ordinal scales is a bit like using an elastic tape measure to measure length. Because the tape measure can be stretched, the difference between 1 centimeter and 2 centimeters may be greater or less than the difference between 7 centimeters and 8 centimeters. As a result, a change of 1 centimeter on the measured variable will not exactly correspond to a change of 1 unit of the conceptual variable (length), and the measure is not interval. However, although the stretching may change the length of the intervals, it does not change their order. Because 2 is always greater than 1 and 8 is always greater than 7, the relationship between actual length and measured length on the elastic tape measure is ordinal.
There is some disagreement of opinion about whether measured variables in the behavioral sciences can be considered ratio or interval scales or whether they should be considered only ordinal scales. In most cases, it is safest to assume that the scales are ordinal. For instance, we do not normally know whether the difference between people who score 8 versus 10 on a measure of self-esteem is exactly the same as that between two people who score 4 versus 6 on the same measure. And because there is no true zero point, we cannot say that a person with a self-esteem score of 10 has twice the esteem of a person with a score of 5. Although some measures can, in some cases, be considered interval or even ratio scales, most measured variables in the behavioral sciences are ordinal.
