subjective message constructs: a theory of persuasion

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Subjective message constructs: A theory of persuasionDonald Dean Morley aa Assistant Professor of Communication , University of Colorado , Colorado SpringsPublished online: 02 Jun 2009.

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SUBJECTIVE MESSAGE CONSTRUCTS:A THEORY OF PERSUASION

DONALD DEAN MORLEY*

Subjective message construct theory maintains that the underlying constructs, orcognitive tests, that receivers apply to persuasive messages require that messageinformation be perceived as important, novel, and plausible for belief change to occur.In three different studies a total of six complete replications of the theory wereperformed. For each replication respondents indicated their subjective beliefs in theprobability of the claim, datum, claim given the datum, and claim if not the datum.These subjective probabilities were subsequently placed in a mathematical formula-tion that represented a multiplicative combination of importance, novelty, andplausibility. The results from six replications indicated that between 25 and 50%percent of the variance in belief change can be explained by subjective messageconstruct theory. Additionally, the results from validity checks indicated thatsubjective probabilities can be used to construct valid operational measures ofimportance and novelty.

EVER since Carl Hovland's heyday, social psychologists have dominated theconstruction of empirically grounded persuasion theories. Although these theo-

ries have increased our understanding of persuasion, their primary interest has beenthe cognitive or behavioral states that produce attitude, belief, and behavioral change.As such, social psychological theories have typically relegated the persuasive messageto the status of a stimulus that produces a cognitive state—e.g., imbalance,dissonance, incongruity, perspective shift—that leads to attitude or behavior change.Even the more recent "cognitive response" approach to persuasion (Greenwald,1968; Petty & Gacioppo, 1979; Petty, Ostrom & Brock, 1981) has focused onsupportive and antagonistic thoughts that are simply stimulated by the message, buttell us very little about the interpretive process. By contrast, most communicationresearchers are primarily interested in constructing theories that explain how peopleinterpret persuasive messages and the consequences of those interpretations.

Subjective message construct theory seeks to explain the persuasiveness ofmessages by investigating the subjectively generated cognitive operations performedon persuasive messages. Specifically, the theory argues that the subjective constructs,or "cognitive tests," that receivers generate and apply to persuasive messages requirethat message information be perceived as important, novel, and plausible beforebelief change occurs. Furthermore, the theory maintains that all three of thesejudgments are subjectively treated by receivers as necessary conditions for beliefchange. Although existing theories and research have implied that importance,novelty, and plausibility are factors determining the extent of persuasion, no theoryto date has provided either a conceptual or rigorous operational statement thatencompasses all three as necessary conditions for belief change (Anderson, 1971;Beach, Mitchell, Deaton & Prothero, 1978; Dustin & Baldwin, 1966; Feldman,1966; Fishbein, 1963; Florence, 1975; Hample, 1977; Kaplan, 1977; McCroskey,1969; Rokeach & Rothman, 1965; Vinokur & Burnstein, 1974,1978; Wyer, 1970a,b,c).

*Donald Dean Motley is Assistant Professor of Communication, University of Colorado at ColoradoSprings. This paper was accepted for publication by the previous editor, Gerald R. Miller.

COMMUNICATION MONOGRAPHS, Volume 54, June 1987

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184 COMMUNICATION MONOGRAPHS

THEORETICAL ORIENTATION

Message Structure

Subjective message construct theory attempts to explain the persuasiveness of bothsimple and complex messages. The theory assumes that messages can be analyzedinto their component parts and the resultant arguments classified as either simplefirst order, hierarchically structured, or laterally structured arguments.

A simple first order argument contains a claim and a single datum to support theclaim. Hierarchically structured arguments advance a claim and offer a datum tosupport the claim, but also provide additional reasons for believing the datum andreasons for why the datum justifies belief in the claim. Laterally structuredarguments advance a claim, but provide multiple data that directly support the claim.Figure 1 illustrates both the first order and hierarchical structures with a partialanalysis of Hample's (1977) "bicycle lanes" message. The first order argument at thetop of Figure 1 attempts to change a person's belief in the claim (p(C)) that "Bikelanes reduce cyclists' safety" by introducing the datum (p(D)) that "Bike lanes

u

uo

u

CLAIM p[C) Bike lanes reduce cyclists' safety

DATUM p{D) _Bike lanes increasetraffic congestion

uv

•8ooo4)

SUPPORT p{S)because lanes take upspace that would other-wise be available for cars

WARRANT p{D\S)belief that bike lanesincrease traffic congestiongiven that bike lanes takeup space that wouldotherwise be available for

WARRANT p{C\D)belief that bike lanesreduce cyclists' safetygiven that bike lanesincrease traffic congestion

cars

BACKING p{B)because drivers aremore irritable incongested traffic

WARRANT p{C\DkB)belief that bike lanesreduce cyclists' safetygiven that they increasetraffic congestion anddrivers are more irritablein congested traffic

FIGURE 1EXAMPLE OF FIRST AND SECOND ORDER MESSAGE STRUCTURE

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SUBJECTIVE MESSAGE CONSTRUCTS 185

increase traffic congestion." Specifically, belief change in the claim should be equalto the importance by novelty by plausibility of the datum.

Because the importance, novelty, and plausibility of a datum can only be assessedin the context of the claim, two additional elements need to be taken into account.The first is the warrant (p(C\D)), which can be either, as Toulmin (1964)indicates, an explicitly stated reason for the datum justifying belief in the claim, or a"subjective warrant" (Hample, 1977), which is the individual's belief that the datumjustifies changing his or her belief in the claim. In both Hample's (1977, 1978) andthis research the subjective warrant was simply the respondent's belief in the claimgiven the datum, or p{C\D)—e.g., assuming that bike lanes increase trafficcongestion, how likely is it that bike lanes reduce cyclists' safety? Finally, a completeanalysis of the importance and novelty_of the datum requires measuring belief inthe claim if not the datum, or p(C | D)—e.g., assuming that bike lanes do notincrease traffic congestion, how likely is it that bike lanes reduce cyclists' safety?

Although such a first order analysis is adequate for charting a simple argumentcomposed of a claim, datum, and warrant, it does not account for more complexmessages that contain a hierarchical or lateral structure. This need not be alimitation to the theory. Rather, as Cronkhite (1969) has demonstrated, therepresentation of a theory can, and should, account for messages with multiple dataand be recursively applied to hierarchical or second order information.

Extending the analysis to complex messages is illustrated in Figure 1. The "secondorder" information is labeled as such and the message is considered hierarchicalbecause the information is not directly related to the claim but is directly related tothe datum and warrant. Second order information that supports the datum is labeled"support statements" and information that supports the warrant is labeled "back-ings."

For example, although Hample's (1977) bicyle lanes message contained only onedatum that directly supported the claim, that datum was in turn supported by foursupport statements (p(S)), and the subjective warrant (p(C \D)) by four backings(p(B)). For the sake of visual simplicity only one of the support statements and oneof the backings are illustrated in Figure 1. Such additional "second order"information needs to be accounted for because of its potential to change belief in thedatum and warrant that could subsequently produce belief change in the claim. Inthis situation second order information must first be used to predict change in theprobability of the datum (Ap(D)) and subjective warrant (Ap(C\D)) beforeattempting to predict belief change in the overall message claim. This is not terriblydifficult, for any model that can predict belief change in the claim can also predictbelief change in the datum and warrant because the relationship of second orderinformation to the datum and warrant is exactly parallel to the relationship of firstorder information to the claim. Specifically, belief change in the datum and warrantcan be predicted by the importance by novelty by plausibility of their respectivesecondary support and backing statements. That is, change in the datum and warrantcan be predicted in the same way that change in the claim is predicted. For example,Figure 1 illustrates that belief change in the datum (Ap(D)) can be predicted fromthe second order support statement (p(S)) and corresponding second order warrant(p(D | S)), while belief change in the first order warrant can be predicted from thesecond order backing (p(B)) and corresponding second order warrant(p(C\D&.B).

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MATHEMATICAL STATEMENT OF THE THEORY

Although an infinite number of formulations can be constructed from thesesubjective probabilities, the logic of subjective message construct theory is based onthe mathematics of determining the joint probabilities of two independent andnonindependent events. Specifically, we know from probability theory that thelikelihood of both the datum and claim being true can be derived from the probabilityof the claim given the datum (p{C \ D)) multiplied by the probability of the datum,or p(C | D)p(D). However, if the claim and datum are independent (knowledge ofD tells us nothing about the probability of C), then their joint probability can bederived by multiplying the probability of the claim by the probability of the datum,or p(C)p(D). That is, p(C)p(D) is the probability of both the claim and datumbeing true by chance alone. Therefore, when p(C\ D)p{D) and p(C)p(D) areequal we would conclude that no more than a chance relationship existed betweenthe claim and datum. Hence, an index of the extent to which the claim is perceived asconditional on the datum (or associated beyond chance) can be derived fromp(C | D)p(D) — p(C)p(D). A clearer, and algebraically equivalent expression is:

Ap(C)«(p(C\D) -p(C))(p(D)) [1]

For example, suppose a person held a moderate belief in the datum that bike lanesincrease traffic congestion (.5), but little belief in the claim that they reduce cyclists'safety (.1). If that person held only a modest belief in the claim given the datum (.3)then the potential of the datum to influence belief in the claim would be quite low or(.3 — .1)(.5) — .1. Conversely, if belief in the claim given the datum weredramatically different from the original belief in the claim the potential for influencewould be much greater.

Importance, Novelty, and Plausibility

First order model. The conceptual meaning of (p(C\D) - p(C))(p(D)),however, is still somewhat unclear. Although the equation could yield an infinitenumber of derivations, Hample (1977, p. 110) claimed that the lack of a differencebetween the subjective warrant (p(C \ D)) and p(C) indicated that the datum waseither perceived as irrelevant to the claim or had "already been integrated into thecognitive structure regarding C." Ifp(C \ D) — p{C) represents the importance andnovelty of the datum, then p(C \ D) — p(C) can be split into separate mathematicalexpressions for importance and novelty.

The first construct, importance, refers to the perceived significance of a datum to aclaim's probability relative to the claim and other beliefs about the claim. That is, thesubjective importance of a given datum depends both on the relevance of the datum tothe claim and the other beliefs and data that person has about the claim. Such"relative" subjective importance can be determined by subtracting the probability ofthe claim, if not the datum, from the probability of the claim given the datum(p(C | D) — p(C | D)). That is, if the datum is perceived as unimportant or of littlerelevance to the claim, the datum's truth or falsity should have little impact on aperson's belief in the claim. This subjective difference has also been argued by Wyer(1974) and by Jaccard and King (1977) to represent relevance.

The second construct, novelty, refers to the extent to which information has notbeen previously integrated into determining a person's belief in a claim. If, for

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example, the datum has been key in forming a person's belief in the claim, thereintroduction of the datum should do little to change that person's belief in theclaim. Such subjective novelty can be represented by:

/ p(C)-p(C\D)_\

\p(C\D)-p(C\D)l

Specifically, the numerator of the novelty component is based on the notion that ifa person's belief in a claim is greatly diminished when assuming the datum's falsity,then the datum has been previously integrated into forming the subjective judgmentp(C). For example, given the familiar datum, "Cigarette smoking is correlated withcancer and heart disease," and the claim, "I should not smoke cigarettes," mostcigarette smokers would find a large discrepancy between their initial belief in theclaim (£(C)) and belief in the claim when assuming the falsity of the datum(jb(C | D)). That is, much of a smoker's belief that he or she should not smokecigarettes has already been based onjhe datum. The reason the numerator is dividedby importance (p_iC\ D) — p(C\ D)) is that in some situations the quantity 1 —(p(C) — p(C\D )) can be high not because the datum is novel but because thedatum is unimportant and therefore the datum's falsity has little impact on belief inthe claim.

The third construct, plausibility, can be represented and operationalized as aperson's direct subjective estimate of the probability of the datum (p(D)). Superfi-cially, this is both conceptually and operationally simple. In an actual persuasivemessage, however, Figure 1 illustrates that a given "datum" can have the dual rolesof supporting the overall claim as well as serving as a "subclaim" with supportingdata of its own.

If importance, novelty, and plausibility are the general cognitive tests contained inthe basic conditional model (p(C\D)p(D) - p(C)p(D)), and if all three arenecessary conditions for belief change, then these components should combinemultiplicatively and still be algebraically equivalent to the basic conditional model.Such an equivalency is provided by Equation 2:

-*cim(i - ̂ 1;!%%)^) PIThe second order application. Equation 2 represents a simple "first order" model

in that change in the probability of the claim is predicted from the primary datum(jb(Z))) and subjective warrant (p(C | £))). The first order model, however, does notaccount for those messages that contain multiple data and warrants, or messages inwhich the primary datum is supported by secondary support statements (p{S)) andthe warrant by backings (p(B)). Extending the model to more complex messageswas accomplished by functionally combining and or "chaining" predictions withmultiple regression (see Slovic & Lichtenstein, 1971, for a review of regressionapproaches to cognitive algebra). That is, Ap(D) and Ap(C \ D) can be predicted bythe importance by novelty by plausibility of their respective secondary support andbacking statements. If the message contained only one support statement then simpleregression could be used to predict Ap(D). If, however, the message containedmultiple support statements then the importance by novelty by plausibility of eachsupport statement would be used in a multiple regression equation to predict Ap(D).

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Equation 3 represents a restatement of Equation 2 for predicting Ap(D):

where:

Ap(D) •= change in the probability of the datump(S) = the probability of the support statement

p(D I S) = the probability of the datum given the support statementp(D I S) = the probability of the datum if not the support statement

b = the unstandardized regression weight, anda - the intercept

Similarly, Equation 4 represents a second order model for predicting change in thesubjective warrant:

Ap(C \D)-a + Y.i-i

p(C\D)-p(C\D&Bi)

where:

Ap(C ID) = change in the probability of the subjective warrantp(B) •= the probability of the backing

p(C I D&B) — the probability of the claim given the datum and backingp{C\ D&.B) = the probability of the claim given the datum and if not the

backingb = the unstandardized regression weight, anda = the intercept

If, however, we are not interested in the exact contributions of importance andnovelty, Equations 3 and 4 can be reduced to:

Ap(D) =a + Yl1-1

Ap(C \D) = a + Yli-l

Once Ap(D) and Ap(C \ D) have been predicted, the effects of second orderinformation can be included in the prediction of Ap(C) by simply adding thepredicted Ap(D) and Ap{C \ D) scores to their respective pretest probabilities andusing these new values in Equation 2. Because Equation 2 would now reflect bothfirst and second order information, this procedure will be referred to as the "full"model prediction of Ap(C).

STUDIES I, II, and III: METHOD

Samples

Respondents were university undergraduates enrolled in required speech andcomposition classes (Study I, N - 52; Study II, N = 168; Study III, N = 105). The

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studies were described as focusing on student opinion and student reaction to severaltypes of communication. Students were told, both orally and in writing, that theirparticipation was totally voluntary. Only two students (Study II) declined toparticipate.

Procedures

All three studies were conducted in a single session, pretest-message-posttestformat. Participants in Study I reported their beliefs on seven-point likely/unlikelyscales while Studies II and III used 11-interval 0% to 100% scales. For each topic,students used to test the simple first order model were given a "partial pretest" inwhich they indicated on the pretest and posttest their subjective beliefs that: (1) themajor claim (p(C)) of each message was true, e.g., "Campus police at the universityshould be permitted to carry handguns while on duty"; (2) the datum (p(D)) thatdirectly supported the claim was true, e.g., "The number of violent crimes committedon the university campus has been increasing"; (3) the subjective warrant or claimgiven the datum (p(C | D)) was true, e.g., "Assume it is TRUE that the number ofviolent crimes committed on campus has been increasing. With that in mind, rate:Campus police at the university should be permitted tojcarry handguns while onduty"; and (4) the claim was true if not the datum (p(C \ D)), e.g., "Assume that thenumber of violent crimes committed on campus has NOT been increasing. With thatin mind, rate: The campus police at the university should be permitted to carryhandguns while on duty." In sum, the first order test attempted to predict beliefchange in the claim based on the importance, novelty, and plausibility of the majordatum alone and ignored support and backing information. _

In addition to the "first order" measures of p(C),p(D),p(C\D), and p(C\ D),participants who were used to test the second order and full models indicated theirbeliefs in: (1) support statements (p(S)) for the datum, e.g., "Many students areafraid to walk alone at night on campus"; (2) the datum given the support statement(p(D | S)), e.g., "Assume it is TRUE that many students are afraid to walk alone atnight on campus. With that in mind, rate: The number of violent crimes committedon campus has been increasing"; (3) backing for the warrant (p(B)), e.g., "Anunarmed security officer will be more reluctant to investigate a potentially violentsituation than an armed campus security officer"; and (4) the subjective warrantgiven the backing (p{C\D&.B)), e.g., "Assume it is TRUE that the number ofviolent crimes committed on campus has been increasing AND that unarmedsecurity officers are more reluctant than armed security officers to investigate apotentially violent situation. With that in mind, rate: Campus police at the universityshould be permitted to carry handguns while on duty."

For both the pretest and posttest in all three studies the probability of the claimswas obtained first, followed by the probability of the data, claims given the data, andclaims if not the data. Beliefs in the second order backings and support statementswere obtained last. The belief measures for all three studies were repeatedly shiftedfrom one replication topic to another in order to minimize any socratic effect.

Replications

Across all substantive studies a total of nine replications were performed. Table 1provides a summary of the testing and treatments administered to the six groups ofrespondents used in the nine replications. In Study I, three simple first order

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TABLE 1

TESTING AND MESSAGES ADMINISTERED TO SIX GROUPS OF RESPONDENTS IN STUDIES I, II, AND III*

Study IBike LanesEmploymentInflationTenant RightsStudy IIApartmentGunsDegreeStudy IIIDormPsychological Adjust-

ment

FullPretest

Gl

G2G3G4

G5.G6

G5.G6

PartialPretest

GlGlGl

G3.G4G2.G4G2.G3

Posttest

GlGlGlGl

G2,G3,G4G2.G3.G4G2.G3.G4

G5.G6

G5.G6

Message

GlGlGlGl

G2.G4G2.G3G3.G4

G6

G5

*The Gs designate groups of respondents (Group 1 n - 52, Group 2 n — 57, Group 3 n - 55, Group 4 n - 56,Group 5 n - 55, Group 6 n - 50).

replications and one full model replication were conducted using Hample's (1977,1978) messages on employment opportunities, inflation, tenant rights, and bicyclelanes. Although all four messages were approximately 500 words long and containedsecondary support for the datum and backing for the warrant, the concern forrespondent fatigue precluded obtaining these subjective probabilities for all replica-tions. Second order measures of belief in "backing" and "support" statements wereobtained only for the bicycle lanes replication. No controls for testing effects wereprovided in that all respondents read all four experimental messages.

In Study II three new messages on the topics of price fixing by apartment owners,the decreasing value of a university degree, and arming campus police were used. Allthree messages advanced a single major claim supported by a single major datum andwarrant. In terms of the second order structure of the messages, the apartmentmessage contained a single support statement for the datum, while the degree andguns messages contained two support statements for their respective data. Thewarrants in the apartment and guns messages were each supported by three backingswhile the warrant in the degree message was supported by two backings. All threemessages were approximately 350 words long. Participants read two of the messagesand were administered the full pretest on one topic, the partial pretest on a secondtopic, and served as controls for the third topic.

In Study III all participants were given the full pretest and posttest on two newtopics. Approximately half of the students read a message advocating mandatorypsychological testing for all entering students, while the other half received a messageadvocating that one additional student should be placed in every dorm room. Bothmessages were approximately 300 words long. Students reading the experimentalmessage for one topic were used as controls for the second topic.

Data Analysis

Pretest to posttest belief change in the overall message claim was the dependentvariable for all first order and full replications. The predictor variable for the simplefirst order tests in Studies I and II was calculated by placing first order subjective

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probabilities in Equation 2. With the exception of the inflation replication the abilityof the model to predict belief change in the claim was initially examined withPearson correlations. The laterally structured inflation message contained two majorsupporting data, which required the calculation of an importance by novelty byplausibility score for each datum. These scores were subsequently used as predictorvariables in a stepwise regression. Two alternative analyses were performed,however, because all messages contained support and backing that could havechanged participants' beliefs in the datum and warrant. First, partial correlation wasused to remove the effect of Ap(D) and Ap(C \ D) from the relationship betweenAp(C) and the model. Second, posttestp(D) andp(C \ D) were used in Equation 2to calculate and subsequently predict Ap(C).

The dependent variables for the second order tests of the model were Ap(D) andAp(C\D). The prediction of Ap(D) and Ap(C\ D) was calculated in the samemanner as the prediction of Ap(C) in the first order replications except that thereduced versions of Equations 3 and 4 were used in order to limit the number ofsecond order measures in the pretest.

The full model replications (bicycle, apartment, degree, guns, psychologicaltesting, dorm) of Equation 2 were calculated in the same way as the first orderreplications except that the unstandardized regression coefficients and interceptsfrom the second order tests were used to predict participants' Ap(D) and Ap(C \ D)scores. These predicted scores were added to pretest p(D) andp(C | D) and used inEquation 2. The ability of the full model to predict belief change in the claim wasexamined with Pearson correlation and stepwise hierarchical regression. For thehierarchical regression the main effects of importance, novelty, and plausibility wereentered first, followed by the two-way interactions, and lastly the unstandardizedtriple interaction. The test of interest was if the triple interaction contributedsignificantly to predicting belief change in the claim. Such a hierarchical procedurewas necessary because the multiplicative combination of importance, novelty, andplausibility constitutes hypothesizing that an unstandardized triple interaction canpredict belief change. Although a triple interaction was the a priori hypothesis, bothBirnbaum (1973) and Allison (1977) point out that the exclusion of" lower-orderterms in an interactive model might not provide a good test of such a model. Becausethe first order and second order replications were of secondary concern thehierarchical analysis was only applied to the full model replications.

STUDIES I, II, AND III: RESULTS

Study I

The results of Study I generally indicated that the simple first order model was aweak predictor of belief change in the claim when Ap(D) and Ap(C\D) were notcontrolled (Table 2). The inadequacy of the first order model in the bicycle and jobsreplications appears to have resulted from failure to control for change in thewarrant's and datum's probability. Specifically, for the tenants' rights message,where a moderate correlation was found, the mean change in the probability of thedatum and warrant was only .275 and .079, respectively, as compared to .367 and.148 for the jobs message and .446 and .151 for the bicycle lanes message. WhenAp(C\D) and Ap(D) were controlled for by using posttest datum and warrantscores, the correlations between Ap(C) and the conditional model increased dramati-

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TABLE 2

CORRELATIONS BETWEEN THE FIRST ORDER MODEL AND Ap(C)

ReplicationBicycle LanesTenant RightsJobsApartment/FullApartment/PartialGuns/FullGuns/PartialDegree/FullDegree/PartialDorm RoomPsychological Testing

Argument 1Argument 2

Change in p(C) UsingPretest Datum and

Warrant

.282*

.543***

.180

.314"

.174

.485"*

.412"*

.286*

.512***

.412*"

.095

.380"

Change in p(C) UsingPosttest Datum and

Warrant

.903**

.848**

.950"

.862"

.744"

.861"

.947**

.841**

.764**•

.692***

.828***N.A.

Change in p(C) withAp(D)&ndAp{C\D)

Partialed Out

.665**

.794"

.621 **

.743"

.600**

.737"

.752**

.658**

.639"

.484"

.350**N.A.

•p < .05**p < .01•••/> < .001

cally (Table 2). Similarly, when Ap(D) and Ap(C\ D) were controlled for withpartial correlations, the correlations were moderately high. The inflation message,which utilized two data and warrants but did not control for Ap(D) and Ap(C \ D),was also successful in predicting Ap(C) (Multiple R - .575; F(2, 49) = 12.08,p < .001).

For the bicycle lanes replication. the second order model was successful inpredicting change in the probability of the warrant and datum (Table 3). When thefull model was calculated the correlation between belief change in the bicycle lanesclaim and the importance by novelty by plausibility formulation was .633 (p < .001).Furthermore, the hierarchical regression analysis (Table 4) indicated that even whenall main effects and two-way interactions were entered first, the importance by

TABLE 3

STEPWISE REGRESSION FOR SECOND ORDER BICYCLE LANES REPLICATION

Predictor Variable

Prediction o/Ap (D)Support 1Support 3Support 2Support 4Prediction ofAp (C | D)Backing 4Backing 1Backing 3

R

.455

.566

.582

.586

.888

.912

.922

R2

.207

.320

.339

.343

.789

.832

.850

R2

Change

.207

.113

.019

.004

.789

.043

.018

r

.455

.450

.322

.266

.888

.824

.733

b

.521

.551-.201-.091

.580

.332

.356

Beta

.455

.507- .224-.075

.519

.305

.187

F to Enter

14.49*"7.91"1.33.28

247.22"*13.47"*5.64*

*p < .05••/> < .01•••/> < .001

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TABLE 4

HIERARCHICAL REGRESSION FOR FULL BICYCLE LANES PREDICTION OF A/>(C)

Predictor Variable

Importance (I)Novelty (N)Plausibility (P)I x NI x PN x PI x N x P

R

.252

.465

.483

.607

.621

.626

.684

R2

.064

.216

.234

.368

.385

.392

.474

R2 Change

.064

.153

.017

.134

.017

.007

.082

r

.252

.228

.145

.544

.279

.232

.633

6

1.41.24

- .10-1.50-1.78- .282.95

Beta

1.124.61

- .07-1.23-1.33-4.70

2.02

F to Enter

3.259.16"1.049.57**1.24.48

6.57**

*p < .05"p < .01*••/> < .001

novelty by plausibility interaction contributed significantly to the prediction ofAp(C).

The results of Study I indicated that belief change in the claim can be predictedfrom the full model, while change in the probability of the warrant and datum can besuccessfully predicted by the second order model. The initial study, however, wassomewhat limited in that only the bicycle lanes message was analyzed with the fullmodel. Additionally, the extent of any pretesting effects could not be determined dueto the lack of a control group. Three additional replications, therefore, were providedin Study II.

Study II

All experimental groups, across all three replications, significantly increased theirbelief in the claims. The control groups for the apartment and degree replications didnot change significantly while the control group for the guns replication did changesignificantly. The significant change in the guns control group, however, does notappear to have resulted from a socratic effect becausep(C | D) decreased significant-ly, F(\, 55) = 6.06, p < .05, while the probability of the claim if not the datumincreased significantly, F(\, 55) = 5.12,/) < .05.

No evidence was found to indicate that the extensive pretesting used to test the fullmodel affected belief change in that Ap(C) did not differ significantly between thefull and partial pretest for any of the replications (Apartment, Degree, and Guns Fsall <1). Similarly, change in the probability of the datum, claim given the datum,claim if not the datum, novelty, and importance did not appear to be affected by theextensive pretesting in that the partial and full pretests conditions differed signifi-cantly on only p(C] D) for the degree replication, F{\, 109) = 5.26,/? < .03. Since atotal of 18 univariate Fs were run, the one significant difference can easily beattributed to chance.

In terms of the predictive ability of the subjective message constructs, thecorrelations between Ap(C) and the first order model for the apartment replicationwere low for the full pretest condition and not significant for the partial pretestcondition (Table 2). The first order model, however, was a substantially betterpredictor of Ap(C) for the guns and the degree replications. As in Study I, thecorrelations between the first order model and belief change in the claim increaseddramatically when the effects of Ap(D) and Ap(C \ D) were removed with partial

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correlation. Furthermore, the correlations between Ap(C) and the model were evenhigher across replications when posttest p(D) and jt»(C | Z>) were used to constructthe model.

The second order model (Table 5) was highly successful in predicting Ap(D) forboth the apartment and degree replications. The two support statements in the gunsmessage, however, were unsuccessful in predicting Ap(D). Change in the warrantwas successfully predicted by the second order backing models for the apartment,guns, and degree messages (Table 5). Belief change in the claim predicted by the fullmodel correlated satisfactorily with observed change in the probability of the claim

TABLE 5

STEPWISE'REGRESSION FOR STUDY II SECOND ORDER PREDICTIONS

Predictor Variable

Prediction o/Ap (D)Support 1Prediction ofAp(C\D)Backing 3Backing 2Backing 1

Prediction ofAp(D)Support 1Support 2Prediction o/Ap(C\D)Backing 1Backing 2Backing 3

Prediction o]'Ap(D)Support 2Support 1Prediction o/Ap(C\D)Backing 2Backing 1

R

.974

.705

.716

.722

.093

.099

.654

.690

.693

.488

.531

.658

.665

R2 R2 Change r

Apartment Replication

.948

.498

.512

.521

.009

.010

.427

.476

.480

]

.238

.282

.433

.442

.948

.498

.015

.009

Guns Replication

.009

.001

.427

.048

.004

Degree Replication

.238

.044

.433

.010

.974

.705

.616

.418

.093

.058

.654

.631

.580

.488

.367

.658

.615

b

1.607

.834

.302-.235

.164

.052

.714

.465

.180

.659

.543

.722

.292

Beta

.974

.591

.258-.132

.083

.035

.369

.281

.107

.410

.223

.495

.190

F to Enter

996.48"*

55.10"*1.661.00

.48

.05

41.88***4.71*

.39 ,

17.57***3.25

41.13*".95

•p < .05"/> < .01*"p < .001

for the apartment (r = .654, p < .001), guns (r = .720, p < .001), and degree (r =.588, p < .001) replications. In all three replications, however, the hierarchicalanalyses failed to detect a significant contribution for the importance by novelty byplausibility interaction (Table 6).

Interpreting the hierarchical analyses as indicating that the plausibility of thedatum does not influence the persuasiveness of an argument would be prematurebecause for these three messages the plausibility of the data was not perceived as thestasis of the argument. Specifically, as the plausibility of the datum approaches 1.0,its contribution to the equation necessarily decreases. In all three messages the datawere ultimately perceived as highly plausible (Apartment M •= .760; Guns M =

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SUBJECTIVE MESSAGE CONSTRUCTS

TABLE 6

HIERARCHICAL REGRESSION FOR STUDY II PREDICTIONS OF Ap(C)

195

Predictor Variable

Importance (I)Plausibility (P)Novelty (N)I x NI x PN x PI x N x P

Novelty (N)Importance (I)Plausibility (P)I x NN x PI xPI x N x P

Importance (I)Novelty (N)Plausibility (P)I x NN x PI x PI xN x P

R

.448

.470

.487

.649

.685

.688

.688

.350

.472

.475

.644

.644

.644

.648

.438

.517

.520

.749

.761

.765

.772

R2

.201

.221

.238

.421

.469

.473

.474

.122

.223

.226

.415

.415

.415

.420

.192

.267

.270

.561

.579

.584

.596

R2 Change r

Apartment Replication

.201

.021

.016

.184

.048

.004

.001

.448

.221

.090

.642

.516

.135

.654

Degree Replictaion

.122

.100

.003

.189

.000

.000

.005

.350

.229

.161

.576

.354

.221

.588

Guns Replication

.192

.076

.003

.291

.018

.006

.011

.438

.189-.153

.733

.072

.346

.656

b

-.666-.215-.059

.704

.798

.067-.174

.074-.024

.411

.403-.096-.447

.696

-1.73- .45- .36.315.41

2.04-2.71

Beta

-.796-.195-.519

.761

.951

.462-.187

.187-.021

.246

.336-.187-.369

.465

-1.69- .72- .262.55.52

1.60-1.57

F to Enter

13.81*"1.431.13

16.48*"4.61*

.35

.07

7.53**6.83".21

16.46*".02.01.42

12.55***5.39*.21

33.19*"2.03

.641.30

*p < .05" p < .01"*/> < .001

.747; Degree M = .735). The result of such highly plausible data was thatimportance by novelty correlated with importance by novelty by plausibility at .939,.919, and .913 for the apartment, guns, and degree replications respectively. Suchhigh multicollinearity between interaction terms makes determination of the "cor-rect" interactive model nearly impossible. Study III was conducted to providereplications where the plausibility of the data was a salient concern for respondents.

Study III

Participants in both the psychological testing and dorm room experimentalconditions significantly increased their beliefs in the claims and data while partici-pants in the control groups did not. The data for all three arguments were ultimatelyjudged by respondents to be of moderate to low plausibility (Psychological TestingArgument 1 M = .438; Argument 2 M - .493; Dorm M = .148).

The correlational tests indicated that the simple first order model was completelyunsuccessful in predicting belief change for Argument 1 in the psychological testingreplication, but was a moderately successful predictor for Argument 2 and the dormroom replication (Table 2). When the effects of change in the probability of thedatum and warrant were removed with partial correlation, the correlation betweenthe model and belief change increased substantially for the psychological testing and

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dorm room messages. Furthermore, when the posttest datum and warrant were used,the correlations between belief change and the model increased dramatically for bothreplications.

In terms of the second order model, a single support statement for the psy-chological testing replication was a moderately successful predictor of change in theprobability of the datum in Argument 1, while two backings successfully predictedchange in the probability of the warrant (Table 7). The second order model for the

TABLE 7

STEPWISE REGRESSION FOR STUDY III SECOND ORDER PREDICTIONS

Predictor Variable

Prediction ojhp(D)Support 1Prediction ofbp(C\D)Backing 2Backing 1

Prediction ojtip (D)Support 1Prediction o/Ap (C\ D)Backing 1

R

.452

.709

.722

.152

.335

R2 R2 Change r

Psychological Testing Replication

.204

.503

.521

.204

.503

.018

.452

.709

.582

Dorm Room Replication

.023

.112

.023

.112

.152

.335

6

1.15

.952

.484

.170

.706

Beta

.452

.585

.182

.152

.335

F to Enter

13.60"*

54.61"*1.95

1.13

6.06*

*p < .05**p < .01•••/> < .001

dorm room replication, however, was not a successful predictor of change in theprobability of the datum and only moderately successful in predicting change in theprobability of the warrant. The full model, however, was still a reasonable predictorof belief change in the claim for the dorm room replication (r — .503, p < .001).Similarly, in the psychological testing replication the full model (Argument 1) was areasonable predictor of belief change (r = .464, p < .001) and Argument 2 was astatistically significant, but weaker predictor of belief change (r = .380, p < .01).When both arguments were combined in a stepwise regression, Argument 2improved the prediction of belief change by a nonsignificant 4.5 percent, F(l, 52) •=3.19,/><.05.

For the hierarchical analysis, importance by novelty by plausibility contributedsignificantly to the regression equation for Argument 2 of the psychological testingand the dorm room replications (Table 8). The triple interaction, however, did notcontribute significantly for Argument 1 in the psychological testing replication.

VALIDITY CHECKS

The face validity of p(C\D) — p{C\D) as a measure of importance is notentirely obvious and the face validity of the novelty component is even less so.Although the tests of the components within the framework of the theory can suggestconstruct validity, the validity checks sought to provide evidence for the criterion-related validity of the importance and novelty components. The criterion established

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SUBJECTIVE MESSAGE CONSTRUCTS

TABLE 8

HIERARCHICAL REGRESSION FOR STUDY III PREDICTIONS OF Ap(C)

197

Predictor Variable

Importance (I)Novelty (N)Plausibility (P)I x PI x NN x PI x N x P

Plausibility (P)Importance (I)Novelty (N)I x NN x PI x PI x N x P

Plausibility (P)Novelty (N)Importance (I)I x NI x PN x PI x N x P

R

.452

.464

.464

.527

.571

.572

.579

.452

.473

.505

.532

.534

.534

.589

.291

.307

.312

.555

.586

.624

.673

R2 R2 Change r 6

Psychological Testing Replication: Argument One

.204

.215

.216

.278

.326

.327

.335

.204

.011

.000

.062

.048

.001

.008

.452

.161-.038

.457

.459

.117

.464

- .62- .02- .401.79.61.09

- .78

Psychological Testing Replication: Argument Two

.204

.224

.255

.284

.286

.286

.346

.085

.095

.098

.308

.343

.389

.452

.204

.020

.031

.029

.002

.000

.061

.452

.118

.178

.294

.058

.262

.380

Dorm Room Replication

.085

.010

.003

.210

.035

.046

.064

.291-.148-.024

.306

.202-.128

.503

.30- .20

.08- .23- .17- .06

.91

.36

.37- .21

.02-1.65-1.20

4.61

Beta

- .74- .15- .341.11.73.27

- .43

.39- .25

.28-.21- .32- .07

.66

.166.44

-.31.02

- .50-6.00

1.34

F to Enter

13.57***.76.03

4.30*3.51

.05

.57

13.61***1.312.102.02

.14

.004.38*

4.45*.50.16

13.67***2.363.224.88*

*/> < .05•*p < .01*•*/> < .001

for testing the validity of the importance measure was simply if p(C \D) — p(C\D)could discriminate between more and less important information, while the noveltymeasure attempted to discriminate between information that participants either hador had not been exposed to. As with any criterion-related validity test the legitimacyof the test ultimately depends on the face validity of the criterion. For this reason theprocedures and messages are described in detail.

Method

Importance. The importance validity check was conducted after the posttest forStudy II (N = 107), while the novelty validity check was conducted following StudyIII (N = 105). The messages used in the validity checks were segments of a fictitioustrial transcript directed at the claim that a person named Larson was responsible foran automobile accident. In one of the messages a police officer testified that Larsonwas speeding prior to the accident, whereas the second message contained testimonyfrom a bartender that Larson had been drinking prior to the accident. One version ofthe speeding message provided testimony (datum) that Larson was exceeding thespeed limit by five miles per hour, while a second version used the datum that Larsonwas exceeding the speed limit by 30 miles per hour. In an analogous manner, oneversion of the drinking message claimed that Larson had drunk one beer in the hour

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prior to the accident, while the second version claimed that Larson had drunk sixbeers in the hour prior to the accident.

For the importance validity check, these messages were used as manipulations in a2 by 2 by 2 analysis of variance with two levels of importance for the drinking datum(one or six beers), two levels of importance for the speeding datum (five or 30 milesper hour over the speed limit), and a repeated measure of the importance_of eachdatum. Following exposure to the messages, measures ofp(C | JD) andp(C \ D) wereobtained for both the drinking and speeding data and subsequently used to calculatetheir importance. Mp(C \ D) — p(C \ D) measures importance, the six beers datumshould have been perceived as more important on the drinking measure than the onebeer datum. Similarly, for the importance of the datum two speeding measure, 30miles per hour over the speed limit should have been perceived as more importantthan five miles per hour over the speed limit.

Novelty. The validity of the novelty component was tested in a 2 by 2 betweensubjects design (message pretreatments by novelty assumptions). For the firstmessage pretreatment, 52 students read the six beers message while the remaining 53students read the 30 miles per hour speeding message. Following exposure to one ofthe messages all participants evaluated the probability that Larson was responsiblefor the accident (p(C)). Fifty-one participants were then asked to evaluate the claimassuming it was true that Larson had been drinking (p(C | D)),and the probabilityof the claim assuming that Larson had not been drinking (p(C \ D)). The remaining54 participants evaluated the claim assuming Larson had been speeding and againassuming that he had not been speeding. That is, participants in both conditionsresponded to the same likelihood of responsibility scales but were asked to makedifferent assumptions while doing so. From these measures:

/ p(C)-p(C\D)\\p(C\D)-p(C\D)l

was used to calculate the novelty of each datum that served as the dependent variable.If the mathematical conceptualization of novelty is valid, the novelty scores for thedatum that participants were not exposed to should have been significantly higherthan the novelty scores for the datum to which they were exposed.

Results

Importance. Because a three way interaction was found between the drinkingdatum, speeding datum, and importance measures, F(\, 103) — 6.57, p < .012, theresults of the importance validity check are discussed in terms of simple interactionand simple effects analysis. The simple interaction analysis focused on the speedingdatum by drinking datum relationships separately for the importance of speedingand importance of drinking measures. For the importance of speeding measure, thespeeding datum by drinking datum interaction was not significant, F(l, 103) <1,p > .05. Simple effects analysis on the speeding measure indicated that exceeding thespeed limit by 30 miles per hour was_perceived as significantly more important (asoperationalized byp(C \ D) — p{C \ D)) than exceeding the speed limit by five milesper hour for both the one beer context, F(l, 51) = 21.08,/? < .001, and six beercontext, F ( l , 52) = 7.84, p < .007.

In contrast to the parallelism found on the importance of speeding measure, simple

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interaction analysis on the importance of drinking measure indicated a significantdrinking datum by speeding datum interaction, F(\, 103) — 8.61, p < .004.Specifically, having drunk six beers and exceeded the speed limit by 30 miles perhour tended to be perceived as more important on the drinking measure thandrinking six beers and exceeding the speed limit by five miles per hour, F(l, 52) =3.45, p < .07. Nevertheless, the simple effects analysis indicated that drinking sixbeers was rated as significantly more important than drinking one beer in both thefive miles per hour context, F(l, 50) = 8.63, jt> < .01, and the 30 miles per hourcontext, F(\, 53) = 27.45, p < .001.

Novelty. For the novelty validity check the predicted interaction between messagepretreatment and novelty conditions was obtained, F( l , 101) = 61.95, p < .001.Simple effects analysis revealed that respondents who were exposed to the drinkingmessage rated the subsequent speeding information as significantly more novel thanrespondents exposed to the speeding message, F(l, 52) = 14.75, p < .001.Conversely, respondents exposed to the speeding message rated the drinkinginformation as significantly more novel than respondents exposed to the drinkingmessage, F{\, 52) = 55.30, p < .001. Additionally, for respondents exposed to thedrinking message, the novelty scores were significantly higher for respondents whorated the novelty of the speeding information than for respondents who rated thenovelty of the drinking information, F(\, 50) = 35.98, p < .001. Finally, forrespondents exposed to the speeding message, the drinking information wasperceived as significantly more novel than the speeding information, F(l, 51) —27.43,/? <.001.

Validity Discussion

The results for the importance validity check indicate that as a measure ofcontextual importance, p(C \D) — p(C\ D) has, at least, criterion-related validity.In both the context of moderate and excessive speeding, drinking six beers wasperceived as more important than drinking one beer. Similarly, in both the one beerand six beers contexts, exceeding the speed limit by 30 miles per hour was rated assignificantly more important than exceeding the speed limit by five miles per hour.The three way interaction did not change these results, but simply illustrated thatdrinking six beers was rated as more important in the context of excessive speedingthan in the context of speeding by five miles per hour. This finding makes sensebecause the excessive speeding information implies that the drinking influencedLarson's ability to drive. That is, the excessive speeding information providedbacking for the argument that if Larson had drunk six beers he was responsible forthe accident.

In terms of the novelty validity check, the results unequivocally indicate that thenovelty portion of Equation 1 is a valid measure of subjective novelty. Respondentswho were previously exposed to the drinking information rated the speedinginformation as significantly more novel than the drinking information. Conversely,when respondents had been previously exposed to the speeding information, theyfound the drinking information to be significantly more novel.

DISCUSSION

Social psychological theories of persuasion have sought to explain persuasion bypositing the existence of various cognitive states. Subjective message construct theory

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is a departure from the social psychological tradition in that persuasion is explainedby cognitive operations that receivers perform on persuasive messages. Specifically,subjective message construct theory identifies importance, novelty, and plausibility asthe underlying constructs that receivers apply to persuasive messages before beliefchange occurs. Although previous researchers have investigated the persuasive effectsof importance, novelty, and plausibility, none of these researchers has formulated arigorous theoretical statement incorporating all three constructs. Furthermore, theresearch reported here has gone beyond traditional persuasion research by extendingthe theory to messages with complex structures.

In terms of the persuasive effects of importance, novelty, and plausibility, theresults indicate that a multiplicative combination of these subjective constructspredicted belief change in the overall message claim. The model, however, was onlyconsistent across replications when all message information was taken into account.When only the first order datum and warrant that directly supported the claim wereused, the theory ranged from being a weak to moderate predictor of belief change inthe claim. When second order information was used to first predict belief change inthe datum and warrant the theory was successful across replications in explaining 25to 50% of belief change in the claim.

In terms of the second order application, the multiplicative combination of theimportance, novelty, and plausibility of the support statements ranged from explain-ing an exciting 95% of the change in the datum's plausibility for the apartmentreplication to only one percent for the guns replication. This between replicationvariability was due in large part to the ambiguity of the concepts judged in the secondorder model. Although "increasing violent crime" appears to be a relatively concreteconcept, the message probably changed the subjective object of judgment (Asch,1940). Specifically, students participating in the study might have initially assessedthe datum "increasing violent crime" from a short-term perspective (i.e., this yearversus last year) while the message shifted the time perspective by arguing that:

It used to be that the job of a campus security officer was pretty much confined to such things asoccasionally confiscating alcoholic beverages, calming overly boisterous students, or making sure thatstudent practical jokes did not go too far. Today, however, times have changed.

The weak predictive ability of the second order model for the dorm roomreplication, however, occurred because the message provoked counterargumentationby the students. Specifically, when issue involvement is high and the arguments areweak (as in the dorm room message) people will successfully argue against thepersuasive message (Petty & Cacioppo, 1979).

The most rigorous examination of the theory was the hierarchical regressionanalyses of the full model. For the bicycle lanes, dorm room, and Argument 2 in thepsychological testing replications, the multiplicative combination of importance,novelty, and plausibility contributed significantly to the prediction of belief changeeven when all main effects and two way interactions were entered first. The failure ofthe triple interaction to emerge for the apartment, guns, and degree replications wasbecause respondents perceived one of the main effects or lower order interactions asthe "cognitive stasis" of the argument. Specifically, the extremely high plausibility ofthe datum in the Study II replications indicated that plausibility was simply not atissue. When the plausibility of the datum was at issue in the psychological testingand dorm replications two significant triple interactions did emerge. The failure to

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find a triple interaction for Argument 1 in the psychological testing replication wasprobably because the probability of the first datum changed a great deal and thesecond order model was not an outstanding predictor of that change. Therefore, anypredictive advantage to the multiplicative inclusion of the datum's plausibility wasprobably counteracted by the errors introduced in estimating the posttest datum.

Despite these difficulties, subjective message construct theory is useful forincreasing our understanding of persuasion. Beyond the evidence that importance,novelty, and plausibility are necessary conditions for belief change, the conceptualtheory and corresponding operational definitions provide for finer conceptualdelineations. These delineations are useful in that they provide the basis for treatingpersuasion as more than simple belief, attitudinal, or behavioral change. Forexample, the "limited effects" view of media influence in political campaigns grewout of the inability of early research to demonstrate a direct link between the mediaand voters' attitudes and intentions (Berelson, Lazarsfeld & McPhee, 1954;Lazarsfeld, Berelson & Gaudet, 1948). More recently, however, research on the"agenda setting" function of the media suggests that the media play a major role indetermining which issues/data will be perceived as important by voters (see Becker,McCombs & McLeod, 1975, for a review). By agenda setting the media accom-plishes one important step in the persuasive process.

Additionally, subjective message construct theory provides for crucial experimentswith more traditional cognitive state theories. For example, social judgment theorymaintains that when the position advocated in a message falls within a receiver'slatitude of rejection, the position will be contrasted and only minimal attitude changewill occur (Hovland, Harvey & Sherif, 1975). The most questionable aspect of socialjudgment theory is that, except for the position of the message's overall claim,variables in the message are treated as irrelevant in determining the amount ofpersuasion. Stated differently, once the overall message position has been assimilatedor contrasted, persuasion has presumably been determined regardless of the qualityof supporting arguments. Conversely, subjective message construct theory predictsthat, regardless of contrast effects, persuasion will occur to the extent that the dataare perceived as important, novel, and plausible.

Although subjective message construct theory suggests some interesting researchpossibilities for expanding and/or replacing other theories, so far it reveals very littleabout what variables (beyond the obvious) determine the extent to which data andwarrants are perceived as important, novel, and plausible. For example, messagevariables such as language intensity might increase the perceived novelty of thedatum by recasting an old datum in a new linguistic light, or possibly intenselanguage would lead to the datum's being perceived as more important but lessplausible. In a more general sense, by providing empirically measurable conceptuali-zations of importance, novelty, and plausibility, the theory facilitates the study oflanguage effects.

Lastly, subjective message construct theory treats the persuasive message as alaterally and hierarchically structured body of arguments. "Lateral" structure refersto multiple data that directly support the overall claim, while "hierarchical"structure refers to the recursive second order, or even third order arguments thatmake coherent the data, warrants, support statements, and backings. The mostimportant and obvious aspect of this conceptualization is the plural "arguments" as abasis for thinking about persuasive message research. Except for simplistic messages,

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persuasive messages are collections of arguments. Potentially useful research,therefore, could investigate receivers' responses to messages in which the variable(s)of interest were "saturated" (present in all arguments), or "contrasted" (somearguments are high on the variable of interest while others are low). For example,the research reported here found that novelty facilitated persuasion; however,Harrell, Bowers, and Bacal (1973, p. 203) have hypothesized that "the rhetoricianmight control his message so as to supply some (but not too many) surprises andpuzzles for his receivers." Possibly a message saturated with highly novel argumentswould be difficult for receivers to integrate, or even comprehend.

Additionally, a multi-argument perspective would facilitate a process-orientedapproach to studying persuasion. In terms of ecological validity, such an approach isnecessary because astute rhetors structure their messages in order to maximizeacceptance. For example, message discrepancy research has typically investigatedonly single arguments such as the number of hours of sleep a person should have(Bochner & Insko, 1966), or the number of years before a cancer cure is discovered(Hovland & Pritzker, 1957). In general, the research has found a curvilinearrelationship between message discrepancy and persuasion (Aronson, Turner &Carlsmith, 1963; Bochner & Insko, 1966; Whittaker, 1963). However, sources whoare highly credible can advocate more discrepant positions than less credible sourcesbefore additional discrepancy results in diminished persuasion (Aronson et al., 1963;Bochner & Insko, 1966). Since message content can affect credibility (Brooks, 1970;Brooks & Scheidel, 1968), sources who initially advance less discrepant data/arguments might bolster their credibility and thereby increase the plausibility andpersuasiveness of a subsequent highly discrepant datum. On the other hand,Helson's (1964) adaptation level theory implies that receivers would adjust to the lowlevel of discrepancy, and therefore perceive the highly discrepant argument as moreimplausible and discrepant than they might have otherwise.

Although investigating persuasive messages as a structural process is difficult, thepredictive ability of the full model application of subjective message construct theoryindicates that such an approach is both possible and informative. In terms of theresearch reported here the major contribution of the theory has been to provide aclearly defined set of intervening constructs that help explain how individuals processpersuasive messages.

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subjective message constructs: a theory of persuasion

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