Refinements on the CognitiveModel of Argument:Concreteness, Involvementand Group ScoresDALE HAMPLE

This study undertakes further testing of Hample's cognitive model of argument. Resultsof the concreteness manipulation are difficult to interpret. The model is more accuratefor highly involved than for less involved subjects. The model performs at the usuallevels of accuracy when the standard individual scores analyses are conducted. Whengroup data are used, however, the model accotints for as much as 95% of the variancein adherence to argument daims. These results suggest that the model's validity is greaterthan previously indicated.

AN ARGUMENT CAN BE legitimately understood to be several different sortsof things, and contemporary argumentation theorists are exploring a variety

of conceptual possibilities (see Brockriede, 1975; McKerrow, 1980; O'Keefe,1977, 1982; Wenzel, 1980). Argument can be considered to be a textual product,like an editorial or an oration —that is, the physicsil result of inventing, com-posing, and delivering a message. Or otie can view an argument as an inter-personal exchange, as something that emerges from the mutual managementof conversation. Argument can also be seen as a disciplined structure for inquiry;one thinks here of the rules of philosophical dialectic, for instance. Some oftbe main issues currently exercising the argumentation community relate tothese different perspectives: whether the views are mutually consistent, which(if any) is primary, whether they are adl perspectives on the same thing.

This paper is concerned with a view of airgument different from those justmentioned.' Here, argument is understood to be an intrapersonal event whichgoes on in the mind of the arguer. When a person moves from one belief toanother, invents public discourse or thoughtfully considers someone else's utter-ance, that person is considered to be arguing cognitively. This theoreticalposition obviously connects argument closely with thought and is perhaps argu-mentation's version of the current cognitive movement in the discipline at large.

One particular cognitive model of argument has been recently tested in aseries of experimental studies (Hample, 1977a, 1978, 1979, 1981a, 1981b). Thisinvestigation seeks to refine that model in two main ways. First, this experi-

The Western Joumal of Speech Communication, 49 (Fall 1985), 267-285

268 Western Joumal of Speech Communication

ment sissesses two possible limits to the general model. As will be shown momen-tarily, concrete messages and low subject involvement are two conditions underwhich the model ought not to work as well. These parameters are tested herefor the first time and so may suggest refinements to the basic model. Second,and perhaps more importantly, previous work has revealed an apparent reli-ability problem in the measurement procedures. This study explores a wayof byp£issing that difficulty and so offers a better estimate of the model's fitto the data. Results of the present study therefore allow a retrospective re-evaJuation of the whole series' results.

THE COGNITIVE MODEL OF ARGUMENT

The historical stimulus to Hample's development of the model was the workof McGuire (1960), especially as revised by Wyer (1974; Wyer & Goldberg,1970; see also McGuire, 1981). However, Hample's data pointed to the needto alter Wyer's model by introducing some weights into the equation, changesthat unfortunately severed Hjunple's model from most of Wyer's Bayesian justi-fications. Therefore, the model and its supporting evidence are described hereindependently of both Wyer and Bayes.

The model's most fundamental assumption is that human reasoning is essen-tially syllogistic. 2 Beliefs, which are considered to be the material of cognitivectrgument, combine in predictable ways. These ways are the same as those whichlogicians specify for propositions. The hypothetical syllogism (if p, then q; p;so q) is used as the preliminary description of the way people move from onebelief to another.

The model, however, is also influenced by Toulmin's (1958) description ofnatural-language argument. Since Toulmin hoped to supply an alternative tologic with his "layout of argument," this use of Toulmin may be unexpectedand perhaps deserves some explanation. As is well known, Toulmin says thatthe three main functions statements can play in an argument are data, warrant,and daim. Toulmin rather intends these as alternatives to minor premise, majorpremise, and conclusion. Haunple's (1977b) critical reading of Toulmin, how-ever, leads him to conclude that Toulmin's warrant is functionally equivalentto the "if p, then q" portion of a hypotheticEil syllogism. So Toulmin's "D; W;so C" i&ftmaionalty the same as "D; if D, then C; so C (also see Hample, 1983b).These comments outline the justification for joining Toulmin and logic together,restoring the traditional union of argument and logic (more detail on these pointsmay be found in Hample, 1977b and 1983b).

Given this functional/logical understanding of data, warrant, and claim, weare ready to see how intrapersonal argument works. The claim will be truewhen both data and warrant are true (i.e., when they intersect). Thus, if wemay assume that data and warrant are independent (an assumption tested inHample, 1981a),

[1] c = D n w.

Due to Toulmin, however, we notice that C, D, and W are more or less prob-able, not merely true or false. Given [1], [2] follows formally:

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[2] p(C) - p(D) p(W).

Thus, the claim's probability is a joint function ofthe probabilities ofthe dataand wzirrant.

In a hypothetical syllogism, the warrant says "if D is true/probable, thenC is true/probable." Thus W may be represented as C|D—that is, the claimgiven that the data are true. By substitution into [2], then, we obtain

[3] p(C) = p(D) p(C|D).

Equation [3] says that a person's belief in a claim is predicted by his/her beliefin the argument's data, times belief in the warrant. While equation [3] is notthe full model of argument, it is that model's kernel: it is the basic expressionof a cognitive syllogism.

But how can an experimenter discover what C, D, and C|D are? We are,after all, discussing people's thoughts, and these are not ordinarily availablefor inspection. The answer is that the experimenter assumes that people willbe thinking about the C, D, and C|D which are in (or implied by) the messageto which the jieople are exposed. One need not be an argumentation scholarto know that this assumption will often be grievously wrong and that non-message beliefs may weli dominate the conclusions people draw. This is why[3] is not the full model of argument and elaborate modifications of [3] havebeen required, both in Hample's model and in the work of McGuire and Wyeron which it draws.

The model must account for the unknown beliefs—those which are not evenhinted at by the message but which may nonetheless dominate someone's beliefin C. The solution to this problem is Wyer's essentied contribution to McGuire'swork. If we focus on the data (once we know the data and claim, after all,we can mechanicaJly construct C|D, the warraint), we can say that the unknownarguments will be those for which D is wrong or irrelevant. This situation isrepresented by not-D, or D. We may use this as the premise of a new syllogismwhich has the same conclusion as our first one: "if D is false, then C is true;D is false; so C is true." This formulation is intended to take into account cillthe reasons (except D's truth) which support the truth of C. Using not-D asour premise instead of D, we can construct [4], which is exactly parallel to [3]:

[4] p(C) = p(D) p(ClD).

Both equations [3] and [4] predict p(C), but they do so on different grounds.Equation [3] represents "if D is true, then C is true; D is true; so C is true,"while [4] works from the premise of D's falsehood. Together, [3] and [4] shouldtriangulate on the real value of p(C).

If we had any reason to supjwse that these two syllogisms had equal weightin the prediction of p(C), we could simply add together the right-hand membersof [3] and [4| and get the best prediction. In fact, this is Wyer's basic modeland is expressed in equation [5]:

[5] p(C) = p(D) p(C|D) + p(D) p(CjD).

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But we have no grounds for supposing this: what reason could we have fortheorizing that message beliefs (modeled by equation [3]) will always haveexactly the same importance as all other beliefs (modeled by equation [4])?In one situation, an auditor could be completely seduced by a speech and haveno non-message beliefs at all. Another listener, however, might have been stimu-lated (or bored) into a private reverie which indudes dozens of powerful counter-arguments. Therefore, equation [5] is theoreticeJly inadequate for modelingargument.

At this stage of its development, however, Hample's model is mute aboutwhat variables determine whether [3] or [4] will be more accurate (hence morepowerful) in predicting p(C). Therefore, the two basic components ofthe model(i.e., the right-hand members of [3] and [4]) are assigned separate weights,which are determined empiricailly by the normal least-squares procedures ofmultiple regression. This gives

[6] p(C) = /3, p(D) p(C|D) + fi, p(D) p(C|D),

which is the full cognitive model of argument studied here. Belief in C is heldto be the result of the message's argument and all salient non-message argu-ments as well.

Hample's experiments have uniformly shown [6] to be a better predictor ofp(C) than equations [3], [4], or [5]. All results to this point have been basedon predictions of individuals' beliefs. For such data, [6] explains between aquarter and a half of the variance in people's belief in C. On these grounds,[6] has been held to be a useful model of cognitive argument.^ One ofthe con-tributions of this study is to introduce new data-analytic procedures which willallow a comparison of results based on individuals' beliefs with those basedon predictions of groups' averaged beiiefs.

SUBSTANTIVE REFINEMENTS: CONCRETENESS AND INVOLVEMENT

The first goal of this project is to test the effects of two substjuitive variables,concreteness and involvement. As will be det£iiled momentarily, previous litera-ture gives good grounds for supposing that these two variables may wellattenuate the model's accuracy. A few general remarks may help to displaythe basic considerations which led to the choice of these two vztriables for inves-tigation.

What equation [6] represents is obviously a fairly disciplined process. Eachbelief is assessed and related to others; conclusions are less airrived at thanworked out. Even without assuming that pieople have any awareness that theyEire pnxessing subjective probabilities logically or that they give conscious atten-tion to the task of "working out" the conclusions to their cognitive arguments(see Hample, 1984), we can still say that the argumentation modeled by equa-tion [6] is hard and would seem to require an appreciable degree of logicallydisciplined effort. If people do not work very hard, the model ought not topredict as well as when they go to greater propositional (i.e., logical) effort.The following portions of this section explain why we might well expect people

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to mELke less propositional effort when dealing with concrete or uninterestingmaterial. Consequently, we expect that equation [6] will not generate normallyaccurate predictions under those two conditions, concrete messages and lowsubject involvement in the topic. The model should work better when subjectsare processing abstract and/or highly involving messages.

Concreteness

Consider concreteness first. Equation [6] is modeling a cognitive version ofpropositional logic. Such thinking is linear and must proceed in a specific order(premise to premise to conclusion). However, some work in cognitive science(Dominowski & Gadlin, 1968; Groninger & Groninger, 1982; Paivio, 1971)gives good reason to believe that only part of our thinking, that which we mightcall verbal thought, is propositional, linear, and sequential. Nonverbal, orimagic, thought is nonpropositional, nonlinear, and multidirectional. Theselatter patterns of cognitive processing are necessarily alogical. As will be dis-cussed immediately below, concreteness is held to be a key stimulus to imagicthought and should therefore decrease the amount of verbal cognitive effortrequired by the model. Consequently, equation [6] ought not to work as wellfor concrete stimuli as for more abstract materials.

Paivio's (1971) work is especially suggestive. He argues that humans havetwo different information processing systems. One is specialized for verbal mate-rial. It handles strings of words, is very sensitive to order, operates sequen-tially, and is therefore the mind's natural seat of logic. To contrast it with thesecond system, the first is called the abstract modality. It is the only systemin which abstract words can be processed. The other system, the concrete one,is for visual information such as pictures or images. This second cognitivemodedity is orgEinic and perspectival, allowing people to "view" things in anyorder and encouraging logic-free associations. Concrete words are coded inboth systems, says Paivio, because they are at once words and image stimuli.So they bridge the two modalities for information processing.

Given a free choice, however, people will tend to process concrete materialimagically rather than verbally. This is because processing in the imagic modeis easier and is therefore often the spontaneous reaction to concrete stimuli(Dominowski & Gadlin, 1968; Groninger & Groninger, 1982). Concrete mate-rials are so high in rated imagery that a distinction between the two vairi-ables — concreteness and imagery —is difficult to make empirically (Paivio,Yuille, & Madigan, 1968; Williams, 1979; but see Richardson, 1980). Thus,highly concrete stimuli encourage mental imagery: people tend to leave theverbal, logicsd field, in favor of less propositionally disciplined effort. Abstractwords, in contrast, allow only verbal processing. On the basis of the work ofPaivio and others, Hample (1982a) theorizes that persuaders can play on thetension between imagery and logic to make fallacies attractive. Certain fallaciesought to be more tempting when the message uses concrete words because theseshould lead the receiver out of logic's field during his/her intrapersonal argu-ing. We would expect the cognitive model of argument to apply much moreclosely when people are in the logical field than when they are out of it. There-

272 Western Joumal of Speech Communication

fore, the hypothesis here is that equation [6] works better for abstract thanfor concrete material.

Unhappily for the symmetry of the hypothesis being developed here, however,some empirical research seems to point the other way, suggesting that con-crete materials enhance the accuracy of people's solutions to syllogisms. Wemust take a moment, therefore, to show why the results of such studies shouldbe set aside.

The most usual conclusion offered in the literature is that concreteness facili-tates reasoning (see Wason & Johnson-Laird, 1972). However, this work isnot quite to the point. The studies which support the idea of a concretenessincrement to accuracy ordinarily compare concrete with symbolic problems (e.g.,"All S is P") and so do not necessarily apply to the present study in which abstractand concrete messages are contrasted. Here, the abstract conditions use words,not algebra-like symbols. Furthermore, some recent research on categoricaland disjunctive syllogisms shows that higher concreteness can make theseproblems harder to solve, though the effect does not seem to extend to hypo-thetical or linear problems (Hample, 1982b, 1983a; but see Ray & Findley,1984, for results which do suggest that concrete materials impede hypotheticalreasoning). Thus, the empiriczd evidence on syllogisms is anything but simpleto interpret. Results of studies on symbolic problems do not support this study'shypothesis, but experiments dealing with abstract v. concrete messages do seemto justify it. A fmal difficulty with Etll these studies is that they required sub-jects to work explicitly on logic problems rather than simply to report theiropinions on a variety of beliefs which are only implicitly related logically. Afair conclusion to draw is that this body of work, though it certainly seemsas though it ought to be relevant, neither constrains nor encourages the presenthypothesis. How far logic can go in modeling concrete thinking remains anopen question.

This study is designed to explore this issue further by comparing the accuracyof equation [6] for both abstract and concrete messages. If the model producescomparably accurate predictions of p(C) for both types of stimulus material,then a reasonable course for future research will be to use the model for bothabstract and concrete arguments. If the equation proves to be superior forabstractly worded arguments, however, we may need to generate a differentmodel for concrete thinking and to restrict our generalizations about this orany other logic-based theory. Concreteness, then, may well be a limitation onthe present model.

Involvement

A second possible refinement is suggested by the work of Petty and Caciopix)(1981; Petty, Cacioppo, & Goldman, 1981). They distinguish between centraland peripheral routes to persuasion. The central route is typified by careful,conscious thinking about the message's arguments, while peripheral sources ofinfluence are extrinsic factors such as credibility or attractiveness of the per-suader. Petty and Cacioppo show that a person's topic involvement keys whichroute is taken: highly involved people are strongly influenced by arguments

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and are untouched by credibility while the opposite is true of people less inter-ested in the topic. What seems to be happening is that subjects taking the centralroute are making considerable cognitive efforts to handle the verbal stimuliwhile peripherally-routed people are not.

Subjects in the present study indicated their level of interest in the messagetopic. Highly interested students ought to be fairly well modeled by equation[6]. However, less interested subjects are predicted to lack the care and efffortwhich the cognitive model seems to assume and may therefore leave the logicalfield. If this turns out to be true, the subject's level of involvement will alsobe a constraint on the model.

RELIABILITY

Several experiments justify some concern about the reliability of the measure-ments used in testing the model. In these studies (Cohen, 1978; Hample, 1975,1981b), as in all the work on equation [6], the subjective probabilities (i.e.,beliefs that C, D, etc., are probable) are each measured by a single scale, asemantic differential bounded by LIKELY-UNLIKELY or some similar pairof adjectives. Using a lOO-point scale, Cohen (1978) reports test-retest reli-abilities of about .65 for the p(X) type of item and about .60 for the condi-tional statements (e.g., the probability that C is true, given that D is true).In two studies, one using a seven-point scale and one a five-point item, Hample(1975, 1981b) reports test-retest correlations (not corrected by the prophecyformula) of .50 and .65 for the p(X) items and .40 and .35 for the conditionalones.

Since these are test-retest measures, they assume that the measured con-struct is supposed to be stable throughout the interval between the measure-ments. But, in fact, McGuire (1960; see also Wyer, 1974) has shown that syllo-gistically related beliefs change spontaneously just by virtue of appearing onthe same questionnaire together (this is the robust 'Socratic Effect"). This naturaladjustment of beliefs does not really represent error of measurement, but itwill depress test-retest correlations. So the reliability estimates just mentionedshould be regarded only as lower limits for the scale's true reliability.

Still, we cannot be certain whether the figures are low because the scale isflawed or because the beliefs reedly changed. Thus, these results are troubling,not only because they challenge the quality of the model's supporting evidencebut also because low reliability (due to random error) would depress the mul-tiple correlations which express the fit between [6] and the data. If there areserious reliability problems, then, the model may actually be better than pre-vious research suggests.

One standard solution to this sort of reliability problem is to aggregate thedata; this is Wyer's usual practice, for instance. By making predictions aboutthe beliefs of groups of people (rather than individuals), the experimenter allowsrandom errors to cancel one another. Thus the data are purer and less con-taminated by error. All of Hample's studies to date have reported results basedonly on individual scores. This will also be done here. In addition, though,some artificial groups of data will be formed, and those groups' means will also

274 Western Journal of Speech Communication

be used to test [6]. If the model does considerably better for group than forindividual data, we will be on solid ground in supposing that individual scoresare "noisy" but that aggregation allows the random errors to neutralize oneanother, yielding purer, less volatile estimates of the model's components andoverall explanatory piower. On the other hand, if aggregating data does notproduce a more impressive fit between equation [6] and the raw data, we wouldbe obliged to conclude that the model in its current form will never do betterthan explaining about half the variance in adherence to claims.

In sum, this study has two objectives. The first is to evaluate two likely limitson the model — concreteness and low involvement. The second is to side-stepthe appEirent reliability problems suggested by previous work. These are thethree hypotheses suggested by the literature just reviewed:

Hj: Equation [6] will be more accurate with abstract than with concretemessages.

Hj: Equation [6] will be more accurate for highly involved subjects thanfor those less involved.

H3: Equation [6j will be more accurate for aggregated than individud data.

METHOD

Subjects

Subjects were 233 undergraduates enrolled in various communication classesat a medium-sized public university in the Midwest. One hundred and fifteenwere from the university-required public speaking course, while the remainderwere taking classes populated almost entirely by communication majors. Malesand females were almost equally represented. About three-quarters of the stu-dents were juniors or seniors. Oral instructions explained that participationwas voluntary, and a handful of students present in the classes chose not todo the study.

Variables and Conditions

The independent variables were concreteness, subject involvement, andgroup V. individual scores. The dependent variable was the quality of equa-tion [6]'s prediction of adherence to the claim, p(C). For lack of a better term,the measures necessary to obtain that prediction (i.e., p(C), p>(D), p(C|D), andso forth) were called ancillary variables.

Concreteness. Concreteness was manipulated by presenting two otherwise iden-tical versions of the same 400-word message which dealt with the nationaleconomy as it affects undergraduates. The concrete version was designed tobe more specific by using a clearly identified protagonist in the "newspaperstory." The abstract version lacked a central character and therefore read moregenerally. In the concrete message, the story was centered on a particular stu-dent, "Bob Heiland, 21, a political science major from Ohio State. A nativeof Dayton, he is tall, with striking orange hair—his friends still call him 'carrot-top.' He graduates soon." He was referenced by name several times in the

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message, and his father's experiences were also mentioned. In the abstract ver-sion, references to HeUand and his father were replaced by less specific phrases,such as "any student" and "one clothing shop operator," and the detailed descrip-tion of Heiland (quoted above) was omitted without replacement. On a priorigrounds, the "Heiland" version of the message was considered to be more con-crete than the "any student" story.

Pilot Study on Concreteness Manipulation. These two versions of the message weretested on a separate group of undergraduates from the same population as theexperimental subjects. They were asked to compare the messages' abstractnessand image-ability on single-item scales. Image-ability was tested because, asnoted above, it is highly correlated with concreteness/abstractness and directlyindexes the imagic processes which are theorized to explain hypothesis 1. Bothcomparisons support the effectiveness of the manipulation, although theabstractness test did not quite reach statistical significjince due to the low powerof the test (for abstractness, i = 1.35, £^= 20, />< .10; for image-ability, t = 3.60,df- 18, < .001). The power of these correlated t tests, assuming a mediumeffect size and a .05 significance level, is .33 (Cohen, 1969). The differencesare in the appropriate directions. The messages, then, seem to differ on con-creteness.

Involvement. Subjects'level of involvement was assumed to be naturally vari-able and was not manipulated or pilot-tested. Involvement was measured byhaving participants respond to a seven-point semantic differential scale, "Howimportant is the topic of the economy to you?" The scEile's endpoints were VERYUNIMPORTANT and VERY IMPORTANT. Experimental subjects filled out this (andall other scales) after reading the message. Based on subjects' responses to thisitem, they were determined to be high or low in involvement. This assign-ment was accomplished by a median split which resulted in 48% of the samplebeing labeled as highly interested in the topic and 52% labeled as having lowerinterest.

Individual and Group Scores. To evaluate hypothesis 3, analyses were carriedout on both individueil and group scores. Individual data, of course, weregathered directly from the subjects. These data were also aggregated into arti-ficial groups as part of the analysis procedure, forming the group scores. Theseaggregating procedures are detailed below in the Analysis subsection.

Ancillary Variables. Equation [6] specifies the remaining variables which arenecessary for obtaining a prediction of p(C). These variables are of two generaltypes, p(X) and p(Y|Z). The same type of semantic differential scale was usedto obtain all the measures. The scale, bounded by VERY UNLIKELY and VERYLIKELY, had seven points. The items used in the study included, for example:

Rate this statement: Next year's economy will be much better for most undergraduates.[p(C), abstract version]

Assume that Bob Heiland's experience indicates that inflation is under control. Withthat in mind, rate: next year's economy will be much better for Bob Heiland. [p(CjD),concrete version]

Assume that Bob Heiland's experience indicates that inflation is not under control.With that in mind, rate: next year's economy will be much better for Bob Heiland,[p(C|D), concrete version]

2^6 Westem Joumal of Speech Communication

In this way, measures were obtained for p(C), ^(D), p(D), p(C|D), and p(C|D)for items reflecting both the abstract and concrete versions of the message. Allsubjects filled out scales for both versions. Students who read the abstractmessage (and for whom, therefore, the test items were the first mention of "BobHeiland") were told that some scales refer to " 'Bob Heiland,' who is a typicalundergraduate student at Ohio State University."

Conditions and Booklet Format. Three versions of the experimental booklet wereprepared and distributed randomly within each class. The control version con-tained no message but included the same measures of ancillary variables asthe two experimental versions. These other two sets of materials presented theconcrete or abstract version of the message, as discussed above, and were fol-lowed by the ancillary variables' items.

Experimental subjects also responded to an item which measured involve-ment, as noted above, and to several seven-point semsintic differential scaleswhich assessed imageability (bounded by VERY HARD TO FORM AN IMAGE andVERY EASY TO FORM AN IMAGE), passage difficulty (VERY HARD TO UNDERSTANDand VERY EASY TO UNDERSTAND), concreteness (VERY ABSTRACT and VERY CON-CRETE), and credibility of the passage's unnamed author (VERY NON-CREDIBLEand VERY CREDIBLE). The imageability and concreteness items were includedto check the concreteness manipulation. Credibility was assessed to evaluatePetty and Cacioppo's (1981; Petty, Cacioppo, & Goldman, 1981) claim thatcredibility ought to affect uninvolved subjects but not involved ones. This isa corollary of their central v. peripheral routes theory, as mentioned earlier.The remaining items were included to determine if the concretenessmanipulation had any other unintended effects on the message's quality.

Analysis

The main analytic objectives were to determine how well equation [6] worksunder various conditions —concreteness or abstractness, high or low involve-ment—£ind to compare results for individual scores with those for group scores.The concreteness and involvement analyses were carried out on individualscores, consistent with the procedures of previous studies in this series.

The individual analyses used a standard multiple regression. Subjects'responses to all the ancillary variables specified in the right-hand m e m ^ r ofequation [6] — p(D), p(C |D), and so forth—were gathered, and the two productsweje formed for each subject, giving two predictor variables: p(D) p(C|D) andp(D) p(C|D). These two predictors were weighted according to the least-squarescriterion and used to generate a prediction of p(C) for each subject. The pre-dicted p(C)'s were compared to the obtained p(C)'s, yielding a multiple cor-relation coefficient, R, which indicates the quality of the prediction.

Group scores analysis made use of the weights calculated for the individualscores analyses but was ultimately an ordinary zero-order Pearson correlation.Responses from different individuals were aggregated to generate means forvarious subgroups. The aggregations represented artificial groupsof data whichwere formed as follows: All subjects who read the concrete version of the messageand who responded "1" to the concrete p»(C) item were combined into one group.

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All subjects who read the concrete message and answered "2" to the item wereput into a second group, and so on. In this way, seven groups (one for eachpoint on the semantic differential) were formed for the concrete message,concrete item condition. This was done separately for the concrete and abstractitems measuring p>(C), yielding two sets of seven groups for the concretemessage. Exactly the same procedure was followed to generate two sets of sevengroups of subjects who read the abstract message and two sets of seven groupsfor the control subjects.

For each artifici^ group, mean scores for the pertinent p(C), p(D), p(D),p(C|D)^and p(C|D) items were calculated. Next, means for the p(D) p{C|D)and p(D) p(C|D) terms (from equation [6]) were calculated. These lattermeans were then mtiltiplied by the appropriate B weights (not the beta weights,which are wrongly calibrated for this sort of procedure) obtained from theindividual scores least-squares analysis. This generated predictions of p(C) foreach of the six sets of groups. The predicted p(C)'s were then correlated tothe mean obtained p(C)'s for each group in the usual way {N = 1 for each r).Thus, correlations were obtained for the concrete message group respondingto the concrete p(C), for the concrete group responding to the abstract p(C),and so forth.

Summary

The experiment, therefore, used a post-test only design, with reuidom assign-mient of subjects to three conditions: control, concrete, and abstract. Subjects'scores on the involvement item restilted in half the experimental subjects beingplaced in the high-involvement condition and half in low-involvement. Theindividual v. group scores conditions were created artificially, in the dataanalysis. Typical subjects took about 15 minutes to finish. Students weredebriefed after handing in their booklets.

RESULTS

Preliminaries

Persuasion. The first check was to see if the messages were actually persua-sive. The control and experimental subjects' responses to both the abstract andconcrete ("Bob Heiland") versions of p(C) were compared. An ANOVA was usedto compare the three groups' adherence to the two p(C) measures. The messageswere effective in stimtalating beliefjchange: for the abstract version of p(C),F= 19.151, £^=2, 223, / i<.001, X^,^ = t,M, Zab««c, = 4.29, and Z,=«c«.=4.51; for the concrete measure of p(C), F= 26.824, df= 2, 223, < .001, Za»<roi= 2.90, .STabMract = 4.31, ajid Jfconcrete = 4.74. Thus, the remsiining results applyto persuasive dynamics.

Equivalence of Message Versions. Excepting the concreteness manipulation, thetwo message versions ought to have similar characteristics. The two experi-mental messages were comparable on several grounds. First, they were equediypersuasive: the two experimental groups did not differ significantly in their

278 Western Joumal of Speech Communication

responses to the two versions of the p(C) item. For the abstract measure ofp(C), F=.6O8, df= 1, 156,p>A0, X^,,^, = 4.29 and vfconcr.« = 4.51. For theconcrete measure of p(C), F= 2.841, ^ = 1, 156, /><.1O, Z^,r,c. = 4.30 andA'concme = 4.73. Secondly, the two experimental groups also made comparableratings on several other variables_^ For message importance, F= .958, df= 1,136, />>.3O, Xh«raci = 4.92 and Xconcr«c = 4.65. On credibility of the author,i^=.006, # = 1 , 136, p>.90, Zab«r.« = 2.31 and Zc<,nc«« = 2.29. Lastly, forpassage difficulty, F= 1.711, df= 1, 136, /)<.2O, Z.b,trac. = 4.19 and Xconcr.« =3.81. These results offer assurance that the two messages were equivalent inrespects other than the manipulation and that the analysis has no specialproblems with uncontrolled variables.

Hypothesis 1: Concreteness

Manipulation Check. Results bearing on concreteness are less encouraging.Though the evidence reported in the Method section of this report indicatesthat the abstract and concrete versions of the message differed on concretenessfor the pilot sample, the two experimented groups did not differ significantlyoji their ratings ofjhe messages' imageability (F= .003, df= 1, 136, p> .95,X^mc, = 3.26 and Zconcm== 3.28) or abstractness {F= .262, # = 1, 136, p>M,absiraci = 2.11 and concrete = 2.26). On the evidence of these self-reports, then,

the manipulation failed.Hypothesis Evaluation. However, the predicted difference in fact appears for

the "concrete" and "abstract" conditions. Results are reported here, and thequestion of whether, or how, to interpret them is taken up in the Discussionsection.

Notice the contrast between the abstract and concrete message conditionsin the "Individual Scores" columns of Table 1. The model explains nearly halfthe individual variance in the abstract condition but only about a quarter witha more concrete message. This is a fairly substantial difference which wouldstrongly support hypothesis 1 if the manipulation had been successful.

Hypothesis 2: Involvement

The next result of special interest is the comparison of students expressinghigh and low interest in the message topic. As indicated in the Method section,subjects were classified according to their responses to the importance item anddivided into two groups of roughly equal size.

The high Emd low imjxjrtance columns of Table 1, which use individualscores, show the model to be consistently better for the more involved students.This suggests that the model's primary relevance is to what Petty and Cacioppo(1981) call the central route to persuasion.

The converse result—that source credibility ought to be more effective forthe less interested subjects—did not appear. Credibility did not correlate sig-nificantly with daims for all subjects in either the high or low importance classi-fications. Possibly this outcome occurred because credibility was not manipu-lated in this design. However, the absence of the predicted credibility result.

Fall 1985 279

TABLE 1Individual and group tests of equation [6], and high v. low importance predictions

Claim

IndividualScores

GroupScores

HighImportance

LowImportance

df

ConcreteAbstract

ConcreteAbstract

ConcreteAbstract

ConcreteAbstract

.575*"

.615'**

.534*"

. 4 4 4 " '

.684**'

.689'* '

.345'

.549'* '

2, 2132, 213

2, 692, 69

2, 762, 76

2, 622, 62

.957

.989

All Data32

' * ' 33

Concrete Message.838*" 11.960"* 11

Abstract Message.954**' 12. 9 6 9 ' " 12

No.826.674

.641

.780

.669

.689

.765

.800

Message Controls10

* 10

* « *

** ****

• **

N.A.N.A.

2, 732, 73

2, 252, 25

2, 322, 32

2

2

.482'**

.486*'*

.534**

.435'

.618*'*

.667*"

N.A.N.A.

2, 752, 75

2, 312, 31

2, 312, 31

2

2

'These are the mean sample sizes for the groups which generated the correlated values. Theactual N for each Pearson correlation is 7. See the text for an explanation of this procedure.

'Control subjects were not asked to rate the importance of the message topics.

while it fails to replicate Petty, Cacioppo, and Goldmem (1981), does not impairthe finding that equation [6] was considerably more accurate for highly involvedsubjects. This result is clearly evident in Table 1, suggesting that topic involve-ment is an important variable mediating the model's accuracy. Hypothesis 2is supported.

Hypothesis 3; Individual and Group Scores

Results bearing on the third hypothesis may also be found in Table i . Thefirst columns report the individual subjects-based multiple correlations for thestudy's vjirious conditions. These multiple i?'s are in the usual range, basedon Hsunpie's earlier studies. Within the experimental conditions, equation [6]generally explains about a quarter to a half of the variance in individuals' adher-

j to claims."The next columns of Table 1 report the correlations for the aggregated data.

Restilts show that this procedure for filtering out noise due to random measure-ment error made a dramatic difference. Explained group variance ranges from98 % to 45 %. In the experimental conditions alone, this range is 94 % to 70 %.This constitutes extremely strong support for the cognitive model of argumentunder study.

28o Western Joumal of Speech Communication

The results for r generally parallel those for R, except that the control groupno longer shows Etny superiority for the abstract items. The control r's, how-ever, au"e the least stable of all those in the table; 4 of the 14 artificied controlgroups had 3 or fewer subjects. Therefore, readers ought not to place muchemphasis on the group data for the control conditions.

In sum, the use of group scores is judged to have been successful in provid-ing a more sensitive test of equation [6], and the apparent reliability problemin the measuring techniques seems to have been niillified. Hypothesis 3 isstrongly supported.

DISCUSSION

This study's results both confirm the general usefulness of equation [6] andsuggest some limitations of it. The model, while adequate for subjects who wererelatively uninterested in the topic, performs much better for interested people.Equation [6] was also more accurate for what had been intended as the con-crete message, though interpretation of this result is difficult.

Probably the most important result of this study is that the group data cor-relations (the zero-order r's in Table 1) gave exceptionally clear evidence forthe model's validity. The multiple i?'s (for individual scores) reported in Table 1are of about the usual size for tests of equation [6]. Being in the general rangeof .40 to .70, however, they leave quite a lot of variance to be explained. Thisadditional variance might have been accounted for by extraneous variables(source characteristics or message features, for instance) or by measurementerror. The size of the zero-order correlations for group data in Table 1, how-ever, strongly supports the notion that the if's are depressed only by unreli-ability and that equation [6] is a virtuzilly complete description of cognitiveargument. These r's range from about .80 to about .95 for experimental groups.These are unusually high for substantive predictions in the social sciences.

We ought to note carefully, however, that the multiple R's have a differentpredictive base than the r's. The multiple R's summarize the model's accuracywith individuEd subjects, while the r's reflect group responses. One implicationof this is that the model is considerably more accurate for groups than for indi-viduals, given the current measurement procedures. Another, more specula-tive implication is that in the long run we might be able to achieve similarlevels of accuracy for a given individual if we have enough data on Enougharguments. This follows from the conclusion that the measurement error istruly rjuidom. If it is, then it ought to be random within people just as it iswithin groups. Enough tests of enough arguments, therefore, ought to allowthe mean measuring errors to approach zero and to generate multiple correla-tions in the range of .90 and above. Thus, we could describe the degree towhich a person reasoned according to the model's specifications. However, thisdoes not mean that the model will ever be much better for an individual argu-ment measured once. Improvement in those predictions must await develop-ment of a much more relieible way of measuring subjective probabilities.

The first two hypotheses were derived from some theoretical notions regard-

Fall 1985 a8i

ing the amount and kind of cognitive effort implied by equation [6]. The generalidea is that the model should work best for subjects who are working hard toprocess their beliefs in a verbal, propositional manner.

Reasonably clear evidence for this thesis follows from the comparison ofinvolved versus uninvolved subjects. Petty and Cacioppo (1981) theorize thatpeople attend more closely and critically to persuasive arguments when thetopic is interesting; thus, more cognitive arguing and counterarguing take place(see Greenwald, 1968), Less ego-involved audience members are not as likelyto go to the cognitive effort of working through a complex argument 2ind henceare more vulnerable to peripheral cues that a claim is attractive or unattrac-tive. Consequently, equation [6], which models logical reasoning stimulatedby the message itself, is more accurate for people whose level of interest hasput them on the central route to persuasion.

Finally, let us consider the results for "concreteness," The main interpretiveproblem here is that experimental subjects' self-reports do not give any evi-dence that the induction of concreteness/abstractness worked. If the hypothe-sis had not been "supported," we would simply conclude that the variable wasnot manipulated and therefore (expectably) had no effect. But results did"support" the hypothesis. Two substantive explanations are possible: (1) somevariabie(s) other than concreteness were inadvertently mEuiipulated and sufficeto explain the results; or (2) concreteness was manipulated, even though theexperimental subjects gave no evidence of realizing it, and concreteness explainsthe results.

The first explanation cannot be rejected. No researcher can ever prove thatsome unknown variable is not really in control of all his/her results. As a con-sequence of the concreteness manipulation, the two messages differed slightlyin length, in the number of proper nouns used, in the degree of personifica-tion, and perhaps in other respects as well. However, the two versions of themessage were checked on several variables—persuasive effect, passage diffi-culty, topic importance, and source credibility—and no significant differenceswere found. Naturally, it is a large step from these four variables to the con-clusion that no important systematic differences existed, but the only availableevidence points away from the unknown variables explanation.

Nor can the second explanation be rejected. In spite of the experimentalsubjects' self-reports, concreteness may possibly have affected their responses.This sort of problem — of subjects not reporting evidence of a difference whichthe experimenter is able to demonstrate with independent data—is fairlycommon (Nisbett & Wilson, 1977). When we ask direct questions about cog-nitive processes or about cognitive contents that were not consciously noticed,we often get "inconvenient" results. If people are reporting about memorieswhich were not acquired with careful conscious attention in the first place, theyoften guess or give other kinds of invalid responses. There is no assumption,either in this paper or in the literature which gives rise to it, that people areaware of the effects of concreteness on their thought processes. Nor is it assumedthat subjects will consciously notice the concreteness of the materials they read.*With no awareness to begin with, subjects would not have any memory of con-

282 Western Journal of Speech Communication

creteness or abstractness to report on the questionnaire (Ericsson & Simon,1980). People C2in be unaware ofthinp which influence them (Nisbett & Wilson,1977), and that may be what happened here.

Though one cannot be certain which explanation to prefer, the study's resultsslightly incline one to prefer the second possibility: that, unbeknownst to thesubjects, they were in fact influenced by the manipulation. Of course, we cannothelp wondering if that manipulation actually dealt with concreteness or pwrhapswith message length, personification, or some other concomitant variable. Thismatter is sufficiently unclear that the most prudent course is probably to defera decision about the hypothesis until a more forceful manipulation is tried.

The main limitation to this study is probably that its design and power re-quirements led to the decision to use only one basic message in two versions(Jackson & Jacobs, 1983). However, the results' basic consonance with thoseof earlier studies —which, combined, have now used a reasonable variety ofmessages and topics—suggest that this shortcoming need not impair our con-fidence in its main outcomes.

The present study, then, has replicated previous research to the extentpossible and has refined earlier results in several ways. First, the cognitive modelof argument appears to have a much closer fit to the data than indicated inprevious reports. Second, the model works best under those conditions in whichwe may expect people to be thinking with high levels of interest. The extremelyequivocal findings regairding concreteness do not impair this general conclu-sion. Overall, this report magnifies our previous estimate of the validity of equa-tion [6] and improves our judgment of its applicability by having refined itthrough examination of its relevance to conditions which either encourage orhinder its accuracy.

ENDNOTES

1. I do not offer this view as a competitor to the others and do not challenge theirlegitimacy. I have tried to relate this perspective to the others in several papers, mostrecently Hample (1983b, 1985). The present report is not an appropriate place to reviewall the arguments designed to justify the cognitive view since this has been done before.Hesitant readers should consult the two papers just mentioned or perhaps Hample (1980).

2. I realize that this is an unpopular assumption and am aware of the literature describ-ing the large variety of reasoning errors which people commonly make. Such work isreviewed in Evans (1982), Wason and Johnson-Laird (1972), and Nisbett and Ross(1980). However, I interpret all this evidence as exceptions to logicality. Even if theexceptions are consistent, they are exceptions nonetheless. The artificialities and diffi-culties of reasoning experiments are well known, and we ought not rely exclusively onthese for our theoretical judgments; see, for instance, Henle (1962) and Ceraso andProvitera (1971). To say that reasoning is fundamentally logical is not to say that itis always jtnd perfectly logical. I believe that our best basic model of human thoughtis logic. ! have made these arguments in a little more detail in Hample (1983a). Thepresent paper reports data which help evaluate the degree to which the logicality assump-tion holds.

3. Readers who resist the idea of argument-as-cognition may prefer to consider that[6] is merely a model of receivers' reactions to persuasive messages. Typically, [6] doesnot work nearly as well for control groups (who hear no message) as for experimental

Fall 1985 283

groups (who do hear one). Thus, the model seems to require a message stimulus forvery high levels of accuracy.

4, Notice an important difference between experimental subjects, who did not reportawareness of the induction, and pilot study subjects, who did. The pilot subjects hadboth versions of the message in front of them, while the experimental subjects had onlyone. The pilot subjects, therefore, had a much greater chance to notice the differencein concreteness. Therefore, the argument being made in the body of this paper appliesmuch more forcibly to the experimental than to the pilot subjects,

REFERENCES

Brockriede, W. (1975). Where is argument? Journal of the American Forensic Association,11, 179-82.

Ceraso, J., & Provitera, A, (1971), Sources of error in syllogistic reasoning. CognitivePsychology, 2, 400-10,

Cohen, J, (1969), Statistical power analysis for the behavioral sciences. New York; AcademicPress.

Cohen, J. L, (1978), Test-retest reliabilities of subjective probability judgments, youma/of Personality and Social Psychology, 36, 643-46,

Dominowski, R. L., & Gadlin, H, (1968), Imagery and paired-associate learning. Cana-dian Joumal of Psychology, 22, 336-48.

Ericsson, K. A., & Simon, H. A, (1980). Verbal reports as data. Psychological Review,87, 215-51.

Evans, J. St. B, T. (1982). The psychology of deductive reasoning. Boston: Routledge & KeganPaul.

Greenwald, A, G, (1968), Cognitive learning, cognitive response to persuasion, andattitude change. In A. G, Greenwald, T, C, Brock, & T, M, Ostrom (Eds.), Psycho-logical foundations of attitiides. New York: Academic Press.

Groninger, L. D,, & Groninger, L, K. (1982). Functions of images in the encoding-retrieval process. youmai of Experimental Psychology: Learning, Memory, and Cognition, 8,353-58,

Hample, D, (1975), An empirical study of value argument. Unpublished doctoral dis-sertation. University of Illinois, Urbana.

(1977a). Testing a model of value argument and evidence. Communication Mono-graphs, 44, 106-20.

- (1977b). The Toulmin modei and the syllogism. Joumal of the American ForensicAssociation, 14, 1-9.

- (1978). Predicting immediate belief change and adherence to argument claims.Communication Monographs, 45, 219-28.

• (1979), Predicting belief and belief change using a cognitive theory of argu-ment and evidence. Communication Monographs, 46, 142-4-6.

- (1980). A cognitive view of argument. Joumal of the American Forensic Association,16, 151-58.

-(1981a). Models of arguments using multiple bits of evidence. Paper presentedto I,C.A., Minneapolis, (ERIC Document Reproduction Service No. ED 206 028),

-(1981b). The cognitive context of argument. Western Joumal of Speech Communi-cation, 45, 148-58,

- (1982a), Dual coding, reasoning and fallacies. Jourao/ of the American Forensic Asso-ciation, 19, 59-78.

- (1982b). The effects of imagery, negativity and ambiguity on syllogism diffi-culty. Paper presented to I,C,A., Boston (ERIC Document Reproduction ServiceNo. ED 226 380).

284 Western Journal of Speech Communication

(1983a). The effects of concreteness and negation on the difficulty of hypothetical,disjunctive and linear syllogisms. Paper presented to I.C.A., Dallas (EJRIC DocumentReproduction Service No. ED 223 410).

- (1983b). The functions of argument. In D. Zarefsky, M. O. Sillars, & J. Rhodes(Eds.) , Argument in transition: Proceedings ofthe third summer conference on argumentation.Annandale, Va.: Speech Communication Association.

(1984). Argumentation and the unconscious. Paper presented to S.C.A.,Chicago.

• (1985). A third perspective on argument. Philosophy and Rhetoric, 18, 1-22.Henle, M. (1962). On the relation between logic and thinking. Psychological Review, 69,

366-78.Jackson, S., & Jacobs, S. (1983). Generalizing about messages: Suggestions for design

and analysis of experiments. Human Communication Research, 9, 169-81.McGuire, W. J. (1960). A syllogistic analysis of cognitive relationships. In C. I. Hov-

land & M. J. Rosenberg (Eds.), Attitude organization and change. New Haven: Yale Uni-versity Press.

McGuire, W. J . (1981). The probabilistic model of cognitive structure and attitudechange. In R. E. Petty, T. M. Ostrom, & T. C. Brock (Eds.), Cognitive responses inpersuasion. Hillsdale, N.J.: Erlbaum.

McKerrow, R. E. (1980). Argument communities: A quest for distinctions. In J. Rhodes& S. Newell (Eds.), Proceedings ofthe summer conference on argumentation. Annandale, Va.:Speech Communication Association.

Nisbett, R . , & Ross, L. (1980). Human inference: Strategies and shortcomings of social judgment.Engiewood Cliffs, N.J.: Prentice-Hall.

Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reportson mental processes. Psychological Review, 84, 231-59.

O'Keefe, D. J . (1977). Two concepts of argument. Journal of the American Forensic Asso-dation, 13, 121-28.

OTCeefe, D. J. (1982). The concepts of argument and arguing. In J. R. Cox & C. A.Willard (Eds.), Adixmces in argumentation theory and research. Caibondale: Southern IDinoisUniversity Press.

Paivio, A. (1971). Imagery and verbal processes. New York: Holt, Rinehart & Winston.Paivio, A., Yuille, J. C , & Madigan, S. A. (1968). Concreteness, imagery and mean-

ingfulness values for 925 nouns . Joumal of Experimental Psychology Monograph Supple-ment, 76, no. 1, part 2, 1-25.

Petty, R . E., & Cacioppo, J . T. (1981). Attitudes and persuasion: Classic and contemporaryapproaches. Dubuque, Iowa: Brown.

Petty, R. E., Cacioppo, J. T , & Goldman, R. (1981). Personal involvement as a deter-minant of argument-based persuasion. Joumal of Personality and Social Psychology, 41,847-55.

Ray, J. L., & Findley, P. (1984). The effects of open and restricted context sentenceson decoding and reasoning with conditional propositions. Communication Monographs,51, 243-52.

Richardson, J. T. E. (1980). Mental imagery and stimulus concreteness._/ounM/o/^Afmta/Imagery, 4, 87-97.

Toulmin, S. E. (1958). The uses of argument. Cambridge: Cambridge University Press.Wason, P. C , & Johnson-Laird, P. N . (1972). Psychology of reasoning: Structure and content.

Cambridge, Mass.: Harvard University Press.Wenzel, J. W. (1980). Perspectives on argument. In J . Rhodes & S. Newell (Eds.),

Proceedings ofthe summer conference on argumentation. Annandale, Va.: Speech Communi-cation Association.

Fall 1985 285

Williams, R. L. (1979). Imagery and linguistic factors affecting the solution of linearSYViop.sms. foumal of Psycholinguistic Research, 8, 123-40.

Wyer, R . S. (1974). Cognitive organization and change: An information processing approach.Potomac, Md.: Erlbaum.

Wyer, R. S., & Goldberg, L. (1970). A probabilistic analysis ofthe relationships amongbeliefs and attitudes. Psychological Review, 77, 100-20.