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they allow researchers to investigate changes in one variable while manipulating one ortwo other variables under controlled conditions

Four Basic Levels of ScalesWhile scaling properties determine the amount of raw data that can be obtained from anyscale design, all questions and scale measurements can be logically and accurately classified

as one of four basic scale levels: nominal, ordinal, interval, or ratio. There are specificrelationships between the level of scale and which scaling properties are activated withinthe scale (see Exhibit 12.4).

Nominal ScalesA nominal scale is the most basic of the four levels of scale designs. In this level of scale,the questions require respondents to provide only some type of descriptor as the rawresponse. The response does not contain any level of intensity; therefore, it is impossible toestablish any form of rank order among the set of given responses. That is, nominal scalesprovide data that cannot be arranged in a greater than/less than or bigger than/smallerthan hierarchical pattern. Nominal scales allow the researcher only to categorize the rawresponses into mutually exclusive subsets that do not illustrate relative magnitudesbetween them.11 Some examples of nominal scale structures are given in Exhibit 12.5.

Ordinal ScalesThe structure of an ordinal scale activates both the assignment and order scaling properties.This level of scale allows respondents to express relative magnitude between the answers toa question. The researcher can rank-order the raw responses into a hierarchical pattern.12Assuch, it is easy to determine greater than/less than, higher than/lower than, moreoften/less often, more important/less important, or less agreement/more agreementtypes of relationships between the responses. It is important to remember that ordinal scaledesigns do not allow the researcher to determine the absolute difference in any of the ordinalrelationships. In reality, almost all state-of-mind data responses are collected using ordinalscales. Exhibit 12.6 provides several examples of ordinal scale structures.

Interval ScalesThe structure of a true interval scale activates not only the assignment and order scaling propertiesbut also the distance property. By activating the distance property, the researcher can

build scale responses that demonstrate absolute differences between each scale point. Here theresearcher and respondent alike can identify and understand what the exact difference isamong the possible raw responses. In reality, an interval scale is nothing more than a collapsed set ofratio-based responses. Normally, numerical descriptors are used to represent the set ofinterval ranges that make up the raw response categories. When the researcher employs thedistance property, it allows for more powerful statistical techniques to be used to analyze theraw data into meaningful data structures.13With interval scale structures, researchers can identifynot only some type of hierarchical order among the raw data but also the specific differencesbetween the data. In true interval scales, the distance between each scale point orresponse does not have to be equal. The research can employ disproportional scale descriptorswhen developing an interval scale. Interval scales are most appropriate when the researcherwants to collect state-of-behavior, state-of-intention, or certain types of state-of-being data.There are many situations where state-of-mind data are of interest to the researcher and

the desire to apply more powerful statistical analysis forces the researcher to, after the fact,transform ordinal scale data into what many consider interval scale data. The transformationis achieved by assuming that the original scale point descriptors activated the distancescaling property.14 However, this transformation is a researcher artifact that violates thefundamental mathematical principles that underpin the four scaling properties. We willdiscuss the assumed interval scale format later in this chapter. Exhibit 12.7 illustratessome examples of interval scale formats.

Ratio ScalesA ratio scale is the only level of scale that simultaneously activates all four scaling properties.

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A ratio scale tends to be the most sophisticated scale in the sense that it allows theresearcher not only to identify the absolute differences between each scale point (or rawresponse) but also to make absolute comparisons between the raw responses.15 Forinstance, in collecting data about how many cars are driven by households in Atlanta Normally, the ratioscale requests that respondents give (or write) a specific singularnumerical value as their response, regardless of whether or not a set of scale points is used.Exhibit 12.8 displays several examples of ratio scale structures.

Ordinally Interval Hybrid ScalesThere are many situations in marketing research where the information problem, definedconstruct, and data requirements call for the collection of certain types of state-of-mind,state-of-behavior, and state-of-intention data that are truly ordinal but have an assumeddistance scaling property artificially imposed on them so that the researcher can performsome type of advanced statistical analysis.16 For example, the researcher might be interestedin knowing not only an individuals attitudes or feelings toward a given product orservice, but also the combined overall attitudes or feelings of a group of individuals at onetime. To achieve this, the researcher must be able to add together many separate rawresponses and perform some type of basic mathematical procedure, like establishing themean response of the group. Thus, the researcher must temporarily violate the distanceand origin properties and artificially assume that they can exist in a scale that normally

cannot activate those two properties.17

When a researcher incorporates artificial distanceand/or origin properties into an ordinal scale design, we call this type of scale anordinallyinterval scale. The term refers to the taking of an ordinal scale and artificially transformingit into an interval scale.