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Measurement Levels
Making decisions about how concepts and variables are measured is vital to social scientific inquiry. Measurement also bears great importance to communication researchers. This entry discusses the tenets of social scientific measurement, the four measurement levels (nominal, ordinal, interval, and ratio), and what statistics are commonly associated with each level of measurement.
Measurement
In the physical sciences, measurement has been established as the process of assigning numbers to objects or observable events according to some formalized set of rules. This becomes troubling for communication researchers as they commonly seek to measure concepts and phenomena that are more abstract than objects and are not as easily observed as events. Many prominent communication phenomena fall into this troubling category, including media effects, nonverbal communication, media literacy, and health and risk messaging, among many others.
To measure such abstract and largely unobservable phenomena using only empirical observations remains the true challenge of social scientific measurement. For instance, a communication researcher might be interested in studying how health messages about skin cancer risks might influence people’s intention to wear sunscreen. While studying this topic, the researcher will confront a host of measurement challenges, not the least of which will be deciding how to measure abstract concepts like message attention and understanding, self-efficacy, and behavioral intention using empirical observations that represent the abstract concepts.
This process of measurement in the social sciences involves empirical and theoretical considerations. Empirically, the communication researcher will observe physical responses and assign numbers to those responses, formally known as indicators. Indicators can take many physical forms including observed behaviors and responses (e.g., people choosing to wear sunscreen after being exposed to a risk message about skin cancer), or indicators can be observed responses from study participants on survey questionnaire items or responses to interview questions. The communication researcher will then confront the challenge of linking these empirical observations to the more abstract concept they represent. The process of measurement focuses on this crucial junction between the empirically observable physical world and the latent unobservable concepts communication researchers commonly study. When empirically observed indicators strongly reflect the abstract concept under investigation, valuable inferences and knowledge claims can be made; however, poor assumptions and incorrect inferences can be made when indicators are more weakly tied to abstract concepts.
In communication research, measurement is noted to be a process of assigning values to concepts. In this process, the researcher denotes what values to assign or classify to each quality or aspect of a concept. It is the duty of the researcher to identify each quality and appropriately define its parameters for the assignment of values or classifications. To assist with the assignment and classification of values to concepts, four scales of measurement, also known as measurement levels, were defined by Stanley Stevens in 1946. Each level of measurement has distinct characteristics that enable researchers to compare similar variables using a variety of statistical tests detailed later in this entry. What follows is a discussion of the four levels of measurement.
The Four Levels of Measurement
Nominal
The first level of measurement, nominal, is derived from the Latin term nominalis, meaning “pertaining to names.” As you may surmise, researchers use nominal measures when they need to assign names or categories to describe the various qualities of a variable. Unlike the other three levels of measurement, nominal variables are discrete and qualitative in nature. They do not provide any ordering of the labeled categories and they also do not define any distance between the assigned values—nominal categories serve only as names or labels. As such, nominal measures have no mathematical meaning. Researchers who choose to assign numerical values to nominal measures do so arbitrarily; the resulting categories can be listed in any order without affecting the relationship between the categories. For instance, if a researcher were to be interested in what color eyes people said they had, the categories created for “eye color” might include blue, brown, green, and so on. If the researcher were to assign numeric values to these labels, they may represent the blue group = 1, the brown group = 2, and the green group = 3. For such a nominal variable, these numbers only serve to indicate that the groups are different from one another. This does not suggest that there are more green-eyed people than blue-eyed people, nor does it signify that the green-eyed group has more of any given quality in any way. Nominal measures must also be exhaustive, meaning that if an individual doesn’t fit into the provided categories, then a new category must be created. This might happen if a person were to report that they have hazel colored eyes, and thus a new category would emerge in the classification. Another nominal variable, “cities in California,” might assign San Francisco = 1, San Diego = 2, and Chico = 3. Each level within a nominal variable is discrete, meaning that they each are mutually exclusive from one another. A city cannot be both San Francisco and San Diego; they are exclusive from one another. Nor can the city be any number in between the assigned discrete values—There is no meaning in stating that a city might be 1.43 San Franciscos. Each value in a nominal measure is whole, discrete, and mutually exclusive from all other possible nominal values.
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