Toward Conceptualizing and Measuring Cognitive Structures

ABSTRACT - Three characteristics of cognitive structure, considered to be the coded representations of information in memory, are identified and defined--dimensionality, abstraction, and articulation. Two measurement procedures, free elicitation and the repertory grid, were hypothesized to provide indicators of these three constructs. Reasonable levels of convergent and discriminant validity were obtained for the proposed construct measures in a study of consumers' cognitive structures regarding nutrition. Suggestions are offered for further research which can more firmly establish the construct validity of these characteristics of cognitive structure.


Rajesh Kanwar, Jerry C. Olson, and Laura S. Sims (1981) ,"Toward Conceptualizing and Measuring Cognitive Structures", in NA - Advances in Consumer Research Volume 08, eds. Kent B. Monroe, Ann Abor, MI : Association for Consumer Research, Pages: 122-127.

Advances in Consumer Research Volume 8, 1981      Pages 122-127


Rajesh Kanwar, The Pennsylvania State University

Jerry C. Olson, The Pennsylvania State University

Laura S. Sims, The Pennsylvania State University

[This research was supported by the Science and Education Administration of the U.S. Department of Agriculture under Grant No. 5901-0410--8-0151-0 from the Competitive Grants Office to the second and third authors. The data collection assistance of Elizabeth Leung and Roslyn Weinstock is much appreciated.]


Three characteristics of cognitive structure, considered to be the coded representations of information in memory, are identified and defined--dimensionality, abstraction, and articulation. Two measurement procedures, free elicitation and the repertory grid, were hypothesized to provide indicators of these three constructs. Reasonable levels of convergent and discriminant validity were obtained for the proposed construct measures in a study of consumers' cognitive structures regarding nutrition. Suggestions are offered for further research which can more firmly establish the construct validity of these characteristics of cognitive structure.


Although much has been written about consumers' cognitive structures for brands and product categories, little direct research has been undertaken. Related work has focused on attitudes, where the major interest concerns belief structures and their relationships to attitude (e.g., Lutz 1975, Olson and Dover 1978). Although some researchers have been concerned with consumers' stored information about brands or products (cf. Woodruff 1972), few have tried to measure memory structures directly. Research by Bettman (cf. 1979), Olson and Mudderisoglu (1979), and Russo and Johnson (1980) are among the few exceptions.

Clearly, however, cognitive structure issues are involved in many research topics (Olson 1978b), although this has not always been explicitly recognized. For instance, information acquisition (cf. Jacoby, Szybillo and Busato-Schach 1977) is essentially concerned with the formation of cognitive structures. Attempts to measure various effects of product familiarity (e.g., Park 1976, Raju and Reilly 1960) can be seen from a cognitive structure perspective (Marks and Olson 1981). Advertising effects research (e.g., Wright 1973) can be interpreted as examining the interaction between advertising and cognitive structure (Calder 1978, Mitchell 1980, Olson 1980, Olson and Dover 1978). Indeed, the increasing interest in the cognitive processes involved in consumers' processing of information, especially as they are influenced by organized structures of knowledge stored in memory, forces us to realize that much (if not all) of consumer research involves the concept of cognitive structures (Bettman 1979, Slash 1978b).

Thus, it seems likely that the concept of cognitive structure--as it refers to the encoded representations of information in memory--will because a central concept in models of consumer behavior. This importance reflects the trend in cognitive psychology where it is now widely recognized that one's acquired knowledge about specific domains has very powerful effects on a variety of cognitive processes and outcomes (see Lachman, Lachman and Butterfield 1979). Many cognitive theorists are actively considering the content and organization of the knowledge held in cognitive structure and its effects (e.g., Anderson 1976, Kintsch 1974, Anderson, Spiro and Montague 1977, Tulving and Donaldson 1972, Shank and Abelson 1977, among many others). A variety of concepts such as schemas (Norman end Bobrow 1975, Norman 1979), scripts (Shank and Abelson 1977), frames (Minsky 1975), and hierarchical semantic memory structures (Collins and Quillian 1969), to name only a few, have been proposed as models of or metaphors for cognitive structure.

Interestingly, the published literature contains many conceptualizations of cognitive structure, but relatively few attempts at operationalization. Many researchers are theorizing about the form end effects of cognitive structures, but few have tried to measure them directly (see Puff 1979). As one example, the cognitive structure concept of memory schemata or schemas has become popular in social psychology (Taylor and Crocker 1980). Yet to our knowledge, no one has attempted to measure the content and organizational structure of a schema directly, although the research of Markus (1977) comes close. Typically, researchers have been content to predict certain behaviors (e.g., reaction time to questions) that should occur if a certain type of schema is indeed present, and then create an experimental setting to demonstrate that effect (cf. Teaser 1978). The schema itself--i.e., content and internal organization--is usually not measured directly, or even relatively directly.

Moreover, there has been little effort directed toward developing hypothetical constructs which describe broad, generalizable characteristics of cognitive structures. Except for Scott's (1963, 1969, 1974) work dealing directly with cognitive structure, most researchers have theorized at a very abstract level about the ways in which knowledge representations may be coded and/or organized in memory. Few have been concerned with developing constructs at an intermediate level of abstraction that define the specific characteristics of cognitive structure that should influence other cognitive processes, and ultimately, overt behavior. This intermediate level is a necessary compromise in order to move away from the highly abstract theoretical level to a more operational level, yet avoid the idiosyncratic level of measuring the unique contents of each person's schemas.

The present research can best be understood against this background of developing interest in cognitive structures, We have three objectives for this paper: (a) to present our initial conceptualizations of three major characteristics of cognitive structure, (b) to briefly describe the empirical indicators we have developed to measure each characteristic, and (c) to present results from an initial study intended to establish the convergent and divergent validity of these indicators. The long-term goals for our research program, the first construct validity study of which is detailed here, are to develop a clearly conceptualized model of cognitive structure and a set of reliable and valid indicators of the major components of that model. Once reliable and valid measures are identified, we hope to learn more about how differences in cognitive structures influence a variety of interesting information processing behaviors such as product evaluations, purchase decision-making, re-spouses to advertising, and information search, to name but a few.

Basic Assumptions

Before presenting specific details, it is first necessary to describe several basic assumptions that provide the theoretical foundations for this research. Essentially, these are metatheoretical ideas, so abstract that they can not be easily subjected to empirical verification. Still, it is valuable to identify the conceptual framework which led to this work so that the reader can trace the logic underlying our approach.

Perhaps the most basic assumption is that people, through various encoding processes, cognitively represent information from the external world in symbolic form. We term these coded representations "knowledge," after Russo (1978). Our use of the term is broad; knowledge can refer to semantic, belief-like representations as well as affective and emotional, perhaps even visual, representations. Another basic assumption is that these coded representations are stored in memory in an organized or structured manner. A useful metaphor for conceptualizing such a cognitive structure is a network of interrelated, associated concepts. The links between concepts might be based on the meaningfulness of the association or the semantic relatedness between the concepts (see Collins and Loftus 1975). A third important idea is that a concept or group of concepts can be activated or "retrieved" from memory merely by attention to external or internally generated cues (Collins and Loftus 1975). In summary, then, information is presumed to be encoded and scored in organized networks of representations that we can call structures of knowledge, or cognitive structures (cf. Hayes-Ruth 1977). Such structures presumably can be activated from memory, either in part or perhaps as a "unit," and used in various cognitive processes (e.g., Olson 1978a).


What are the major characteristics of the network of knowledge that constitutes a cognitive structure? The cognitive psychology literature provides little help in answering this question, Instead, we turned to the cognitive style literature (see Goldstein and Blackman 1979 for review), and especially to the work of William Scott (1963, 1969, 1974), a social psychologist. During the early 1960's there was substantial interest in "cognitive structure" concepts such as cognitive complexity. Much of this work was "stylistically" oriented in that it attempted to describe and measure regular patterns or styles of behavior. For the most part, little attention was paid to the cognitive representations in memory that, with the hindsight provided by our information processing perspective, we now see as responsible for the observed behavior.

We have modified selected concepts from this earlier work in adapting them to our cognitive structure perspective. Actually, this process was less procrustean than it may appear. Scott's work in particular was fairly easy to translate into a memory structure orientation. Essentially, we selected major behavior patterns that previous researchers felt to be important, and we attempted to define the characteristics of a cognitive structure that could or should produce such behavior. Then, we developed conceptual definitions for each major characteristic of cognitive structure. In our scheme, then, the observed behaviors which previously had been the major focus of interest are now created as outcomes of the cognitive structure constructs, and therefore, as potential indicators of these characteristics. We recognize, of course, that the constructs and measures of this initial nomological framework are likely to undergo substantial changes during the course of the next few years' research. They are offered here as a potential stimulus to that research effort, not as the final product.


The notion of cognitive complexity provides the clearest example of our approach. Goldstein and Blackman (1979, p. 115) state that "The cognitively complex individual is one who differentiates among concepts and is able to make discriminations on each of these conceptual dimensions." Note that this definition is strictly operational. It specifies two behaviors to be observed, based on which one can label an individual as more or less cognitively complex. Following Scott's (1969) lead, we conceptualized two aspects of cognitive structure that might be expected to produce these behaviors. One is dimensionality, defined as the number of salient, activatable (see, Olson, Kanwar and Muderrisoglu 1979) concepts stored in memory. Here we are concerned with the number of activatable concepts associated with a particular content domain.


The second cognitive structure characteristic is articulation, defined as the number of category representations or levels for each salient dimension in memory. People who possess larger numbers of activatable concepts for a domain have a cognitive structure that is higher in dimensional-icy. Presumably, they are capable of using more concepts to perform evaluation or choice tasks regarding that domain, although whether or not they do so may be influenced by other factors. Likewise, people who are higher in articulation should, in general, be able to make finer distinctions between stimuli in terms of that dimension than those whose cognitive structure dimensions are less articulated. Thus, people high in both dimensionality and articulation should evidence "cognitively complex behavior" in certain tasks, because of these characteristics of their cognitive structures.


Abstraction is the third construct of interest in this paper. [We have proposed two additional characteristics of cognitive structure--interrelatedness and centrality--but these are not discussed here.] As a characteristic of cognitive structure, abstraction concerns the degree of abstractness of the salient, activatable dimensions stored in the structure. A cognitive structure's abstractness is necessarily a relative concept, based on the mix of more and less abstract and concrete representations in the structure. This concept seems highly related to ideas such as hierarchical groupings of concepts (Collins and Quillian 1969), chunking (Simon 1974), unitization (Hayes-Roth 1977), and categorization (Rosch 1975). Abstraction can also be considered as a recoding process in which a new code is assigned to represent several other usually less abstract or more concrete codes. The abstraction process is functional since recoding reduces the number of salient concepts in a structure. Because larger amounts of knowledge are now represented by a smaller number of more abstract dimensions, using more abstract cognitive structures should require less of a person's limited cognitive capacity compared to using less abstract, more concrete structures.

These three characteristics--dimensionality, articulation, and abstraction--are proposed as critical features of cognitive structure. These (and perhaps other) features of someone's stored knowledge are expected to affect how that person processes information relevant to that domain. Of course, it is relatively easy to propose constructs. Compelling evidence for the validity of these constructs requires independent measures of the characteristics, in addition to accurate predictions of their presumed effects. Measures of the three characteristics are discussed below.

Measurement Approaches

The three cognitive structure constructs are defined conceptually in terms of the coded representations organized in network-like memory structures. Therefore, measurer of constructs must tap into memory, at least relatively directly. Most of the procedures used in past research to measure cognitive complexity are inappropriate for this purpose. For instance, personality-like measures such as the scale used by Kasulius and Zaltman (1977) provide no indication of the content or organization of cognitive structure. Instead, we developed two procedures to indicate the proposed characteristics of cognitive structure more directly--free elicitations and a modified repertory grid task.

Free Elicitation.  Free elicitation is the "newer" procedure, having been used relatively little in cognitive research. The conceptual basis for free elicitation is activation theory (Collins and Loftus 1975). The basic idea is that exposure to a cue activates the cognitive representation of that stimulus. Then, that activation spreads to other related concepts via the associations or linkages between representations. Thus, an entire structure (if one exists) might be activated in a relatively short time. Once a structure is activated and available for processing, the subject should be able to verbally report some, perhaps much of the structure content. By this logic, then, the elicited responses may be considered to reflect the characteristics of the cognitive structure.

The present research concerned peoples' nutrition knowledge structures. For each subject, we used four different probe cues to initiate the elicitations. In fact, we used a multiple elicitation procedure in which each elicited concept was itself subsequently used as a probe to stimulate additional elicitations. In the present study we used three or four "layers" of probes which produced a large number of idiosyncratic responses for each subject. [We consider free elicitation to be subtly different from the free recall paradigm commonly used in cognitive and to some extent in consumer psychology (e.g., Johnson and Russo 1978). In free recall, the focus tends to be on a particular learning situation and on the specific episodic memory traces about that event. Thus instructions tend to be like, "Tell me which words (or ads) you remember seeing yesterday." In free elicitation, the researcher usually seeks probe cues that will trigger or activate a particular cognitive structure in semantic memory. Although the content of that structure probably was acquired over many learning occasions, these specific occasions are of little or no interest. It is interesting to note that the free elicitation procedure, as derived from activation theory and associationist network theory, is essentially identical to the method advocated by Fishbein (1967) for identifying salient beliefs.] Based on Epstein's (1979) arguments, we combined all the elicitations for the four probes to produce indices of dimension-alloy and abstraction. The total number of unique (multiple mentions of a concept were counted only once) nutritional concepts that are elicited were considered an indicator of the dimensionality of nutrition knowledge. And, the number of those nutrition responses that were relatively abstract and concrete were considered indicators of the abstractness and concreteness of a person's nutrition cognitive structure.

Repertory Grid.  The repertory grid is a more structured elicitation procedure than the fee elicitation technique. The repertory grid was originally developed by Kelley (1955) to identify the constructs people use to structure their perceptions of the social world. In adapting the repertory grid techniques to our purposes, we modified the original procedures somewhat.

Basically, our repertory grid methodology begins by presenting subjects with several sets of fond concepts three at a time. We use types of food as stimuli since we are interested in nutrition knowledge structures. For each triad, subjects are to state all the "ways" in which any two of the foods are similar and different from the third. When no new concepts can be elicited, another triad is presented, and so on. We have found that after 10 to 15 triads most subjects produce few new concepts. The next step is to select the specific concepts of interest--here, nutrition concepts. For each unique nutrition concept elicited, subjects are asked to sort 20 or 30 types of food (each food printed on a card) into "piles," so that the foods in each pile are similar with respect to that particular dimension. Subjects are free to use as many or as few groupings as they wish. After the free sort, subjects are asked to name each group (pile) and describe why those foods were clustered together on that nutrition dimension.

The data produced by the modified repertory grid procedure provides possible indicators of all three knowledge structure characteristics. The number of unique nutrition concepts elicited may indicate the dimensionality of the nutrition knowledge structure. The elicited nutrition concepts can be coded in terms of their abstractness/concreteness. And the average number of categories or levels produced in the sorting tasks may indicate the degree of articulation of the nutrition cognitive structure.

Knowledge Test.  The two methods described above were developed to provide relatively direct indicants of stored cognitive structures. It was considered valuable to compare these measures with a more traditional method of measuring knowledge. A common method of measuring consumers' knowledge regarding a domain is to have them complete a paper-and-pencil test consisting of several specific, usually factual questions about the topic (cf. Edell and Mitchell 1978). This is the dominant technique used by nutrition researchers to measure people's nutrition knowledge (see Cosper and Wakefield 1975, Fusillo and Beloian 1977, Grotkowski and Sims 1978).

For the present study, we constructed a 23-item scale containing a variety of factual questions about nutrition. These items were selected from a larger set of questions commonly used in nutrition research and additional items constructed by the authors. First, items identified as ambiguous in small-scale pretests were removed or rewritten. Then the revised scale was given to a group of 250 respondents, 150 college students and 100 adult participants in a continuing education course. Each of the final 23 items had positive item-total correlations (average r = .23). A factor analysis produced no clear factors beyond a general factor, and Cronbach's coefficient alpha, an indicator of internal consistency, was .68 for the entire test. Scores on this knowledge "test" were related to the measures of dimensionality, abstractness, and articulation.



The data collection procedures used in this research required substantial experimenter effort (as much as 2-1/2 hours per subject). Therefore, final sample size was restricted to 18 women who had the primary responsibility for food shopping and meal preparations in their households. Because the research purpose was to establish the convergent and divergent validities of the possible indicators of cognitive structure and their interrelationships, any person with cognitive knowledge about nutrition (nearly everyone, probably) was an appropriate subject. Therefore, because sample representativeness was not a major concern, subjects were graduate student wives and secretaries at Penn State University, selected on a convenience basis. All voluntarily agreed to participate by signing an informed consent form and were paid for their participation at the rate of $4.00 per hour.


Two stages of data collection are relevant for this paper. At the first stage, 29 subjects went through the free elicitation procedures described earlier. Each subject was run individually. Four initial probe cues were used for each subject. Fifteen subjects received as probes the phrases, "good health, balanced diet, nutritious food, and vitamins," while the other 14 subjects responded to "having good health, obtaining a balanced diet, eating nutritious food, and getting enough vitamins." For present purposes this manipulation is ignored. As described earlier, elicited responses to the four original probe cues were used in turn as probes and so on again, "down" to four levels. Interviewers recorded subjects' responses as they were elicited on special scoring sheets. This multiple probe procedure was intended to maximize the chances of activating parts of each subjects' nutrition knowledge structure(s). It resulted in a substantial number of elicited concepts per subject (M = 47.1). Following the elicitation, subjects completed the knowledge questionnaire, plus several other questions, On average, this session required from 1 to 1-1/2 hours.

The repertory grid was conducted at a second session about two months later. Due to some attrition and one refusal, only 18 of the original 29 subjects participated in the second session. The repertory grid procedures were followed as described above. An average of 34.1 concepts were produced for each subject. This session required about one hour.


Our major interest is in the convergent validity of the elicitation and rep grid procedures for measuring dimensionality and abstraction, and in the relationships (a) between these two constructs and articulation and (b) between all three concepts and the scores on the questionnaire knowledge test. To briefly review, dimensionality was measured by the number of unique nutrition concepts produced by the elicitation (M = 6.6) and repertory grid (M = 5.7) procedures. Possible indices of abstraction were generated by classifying the unique nutrition responses from the elicit-argon and grid procedures as relatively abstract (M's = 1.9 and 1.3) or concrete (M's = 4.6 and 3.4). Two independent judges were reasonably consistent in their categorizations of the concepts as nutritional or not (r = .85) and as abstract/concrete (r = .87). Unfortunately, we had only a single index of articulation, the average number of category levels per nutrition concept (M = 4.4), as evidenced by the rep grid free sorting task.

Table 1 presents the relevant correlations between these constructs, in a multimethod-multitrait matrix format. First, note that concreteness, the number of concrete nutrition concepts, is highly correlated with dimensionality, the total number of nutrition concepts elicited (r = .96 for the free elicitation method and r = .95 for the rep grid procedure). Apparently, subjects who produced more unique nutrition concepts also produced many concrete concepts. Therefore, "concreteness" may not be useful for indicating the abstractness of a cognitive structure. Certainly, the number of abstract concepts has more face validity. It is possible that if finer distinctions between levels of abstraction/concreteness could have been made, the apparent redundancy would be eliminated or reduced. However, criteria for more finely discriminating between levels of abstraction are not clear to us. Because concreteness is essentially totally redundant with the dimensionality measure, in the present data at least, we will ignore most of the correlations with the concrete measure, although they are shown for completeness.



Next, consider the convergent and discriminant indices. The rep grid and free elicitation methods seem to converge fairly well on the concepts of dimensionality and abstraction (r's = .56 and .65, respectively, p< .05). The low off-diagonal values of .02 and .29 provide evidence of discriminant validity. Note also the dimensionality and abstract constract constructs are apparently positively related, as one might expect (r's = .49 and .28 for free elicitation and rep grid, respectively). That is, as the dimensionality of cognitive structure increases, there is a tendency for it to become more abstract. Because only a single measure of articulation has been developed thus far, estimates of convergent and divergent validity are not possible. However, the low, negative relationships between articulation and the dimensionality and abstraction measures are quite consistent for both measurement methods. Although the small magnitude of these correlations do not allow confident theoretical interpretations, it seems logical to suppose that as the dimensions of a structure become more abstract, they also become less articulated. In sum, both the magnitude of these relationships and their general consistency, especially given the long time period between measures, provide support for the construct validity of the three characteristics of cognitive structure and the validity of the measures developed as indicators of those concepts.

What about the traditional paper-and-pencil knowledge test? As can be seen in Table 1, knowledge test scores are only weakly related to the other measures of cognitive structure. The test scores seem to relate more strongly with the rep grid measures, although this is only a weak tendency. At least all the relationships are positive, as would be expected if each measure is tapping nutrition knowledge structures. Clearly, however, the knowledge test is not measuring the same thing as the free elicitation and repertory grid methods.


In our opinion, sufficient evidence of convergent and discriminant validity has been demonstrated to warrant additional work on these hypothesized cognitive structure constructs. The magnitude of the obtained relationships and their consistency are reasonable, although certainly not outstanding. The effect sizes obtained can be seen as a bit more encouraging when one considers that the subject sample was probably more homogeneous with respect to nutrition knowledge than the general population. Given the probable restriction of range in our indicators produced by this homogeneity, the obtained coefficients can be seen as even more respectable. In conclusion, then, the repertory grid and free elicitation procedures appear to be capable of producing valid indicants of two potentially important characteristics of cognitive structure--dimensionality and abstraction. Also, the other hypothesized attribute of cognitive structure, articulation, evidenced consistent relationships with the measures of both dimensionality and abstraction.

We are encouraged by these results and are conducting further investigations concerning the construct validity of these characteristics of cognitive structure. What should be the next steps be in such a research program? Obviously, a replication of this study should be conducted. To increase power, such a replication should use more subjects, if possible. Moreover, it would be valuable to obtain subjects who possess wide ranges of nutrition knowledge. Increasing the variance for measures of nutrition cognitive structures should enhance the predicted relationships, compared to the present results, if the basic model is essentially valid.

A slightly different approach to construct validation could provide a somewhat stronger "test" of the validity of these measures. One could select subjects who on some a priori basis comprise two (relatively) extreme groups in terms of their knowledge about nutrition. For instance, one might use senior undergraduate nutrition majors and seniors who had never taken nutrition courses. Or, one could examine persons known to be highly concerned about their eating habits (health food advocates, e.g.) versus less concerned people (ordinary citizens). Presumably, such groups would differ dramatically in terms of their cognitive structures regarding nutrition. If so, valid measures of the major characteristics of knowledge structures should reflect these differences.

Eventually, future research must predict the effects that differing cognitive structures have on other cognitive variables and processes and must test those hypotheses empirically. For example, if someone has a complex cognitive structure for nutrition (high dimensionality, relatively abstract, well articulated), we might predict certain types of cognitive responses to a series of food ads containing nutrition information. Presumably, such a person's responses should differ from those produced by a different consumer with a shallow, simple cognitive structure for nutrition. Minimally, the former consumer should produce more nutritional cognitive responses. Probably, these responses would be more abstract or deeper in a semantic sense (Olson 1980). In contrast, the effects of differing cognitive structures on persuasibility (e.g., attitude and intention change) are less clear (see Marks and Olson 1981). To make such predictions, one must carefully examine the idiosyncratic content of stored knowledge and determine, among other things, the degree of discrepancy between the individual's pre-exposure beliefs and those presented in the message. Finally, differences in cognitive structures should be reflected by variation in decision-making processes. Here we might examine the amount of time taken, the number of attributes considered, and the optimality of the selected alternative, among other factors.

In conclusion, we hope to have stimulated at least some readers to undertake research that deals more directly with peoples' cognitive structures than typically has been done. Issues regarding cognitive structure are intrinsically interesting, and they are important. Developing clearer ideas regarding the content and organization of cognitive structures seems likely to enhance our understanding of the other cognitive processes that are influenced by cognitive structure, and of cognitively-mediated behavior, too.


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Rajesh Kanwar, The Pennsylvania State University
Jerry C. Olson, The Pennsylvania State University
Laura S. Sims, The Pennsylvania State University


NA - Advances in Consumer Research Volume 08 | 1981

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