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Modeling the Effects of Processing Effortand Ability in Response to PersuasiveMessage ArgumentsChristopher J. Carpenter a & Franklin J. Boster ba Department of Communication , Western Illinois Universityb Department of Communication , Michigan State UniversityPublished online: 22 Aug 2013.

To cite this article: Christopher J. Carpenter & Franklin J. Boster (2013) Modeling the Effects ofProcessing Effort and Ability in Response to Persuasive Message Arguments, Communication Quarterly,61:4, 413-430

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Modeling the Effects of ProcessingEffort and Ability in Response toPersuasive Message ArgumentsChristopher J. Carpenter & Franklin J. Boster

This study proposed a path model of the effects of message comprehensibility on attitude

change with the measured cognitive mediators suggested by dual-process models of

persuasion. An experiment was conducted using a 2 (strong arguments vs. weak

arguments)� 2 (easy vs. difficult to understand arguments) fully crossed independent-

groups design with 120 participants. The data were consistent with the traditional

impact of message comprehensibility on difficulty of processing the message such that

the effect of argument strength was diminished when the arguments were difficult, rather

than easy, to understand. The path models were not consistent with the data, and the

model was revised to produce adequate fit.

Keywords: Causal Modeling; Cognitive Processing; Social Influence

Dual-process models of persuasion including the elaboration likelihood model (ELM;

Petty & Cacioppo, 1986) and the heuristic systematic model (HSM; Chaiken, 1980)

have become the dominant perspective in persuasion research. These models argue

that persuasion proceeds via one of two processes. One process is a low cognitive

elaboration process such that when people are exposed to a persuasive message, they

produce their attitude by relying on simple cues regarding the persuasiveness of the

message such as credibility (Petty, Cacioppo, & Goldman, 1981). This process occurs

when the audience is not able or willing to attempt to process the message carefully

and effort fully. The ELM refers to this process as peripheral processing (heuristic

processing in the HSM). The other process, called central processing (systematic

Christopher J. Carpenter (Ph.D., Michigan State University, 2010) is an assistant professor in the Department of

Communication at Western Illinois University. Franklin J. Boster (Ph.D., Michigan State University, 1978) is a

professor in the Department of Communication at Michigan State University. Correspondence: Christopher J.

Carpenter, Department of Communication, Western Illinois University, 318 Memorial Hall, 1 University Circle,

Macomb, IL 61455; E-mail: cj-carpenter2@wiu.edu

Communication Quarterly

Vol. 61, No. 4, September–October 2013, pp. 413–430

ISSN 0146-3373 print/1746-4102 online # 2013 Eastern Communication Association

DOI: 10.1080/01463373.2013.799509

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processing in the HSM) is a high effort process such that people exposed to a

persuasive message are able and motivated to carefully consider the quality of the

message arguments. Those processing centrally are more likely to reject weakly argued

positions and accept strongly argued positions than those processing peripherally.

When message arguments are strong, such increased message elaboration is associated

with increased persuasion and the opposite when message arguments are weak.

Central processing is predicted to produce stronger attitudes that are held with greater

certainty. The two predictors of whether the audience processes the message periph-

erally or centrally are how difficult it is to process the message information (ability)

and the extent to which they are motivated to carefully evaluate the message (effort).

There have been many studies that have replicated the pattern predicted by

dual-process theories (although some have not; see Johnson & Eagly, 1989), but

few have attempted to produce a causal model of these processes; and none, to the

authors’ knowledge, have attempted to use measurements of difficulty of processing

and processing effort identified as the key constructs of the ELM to test such a model.

Instead, most of the studies in this literature induce the key constructs, check the

effect of the inductions on the conceptual variables with measured induction checks,

and then ignore the induction checks in favor of testing the effects of the inductions

on attitudes. O’Keefe (2003) identified this practice as a common failing in com-

munication research and persuasion research in particular. He suggested that

researchers would produce more insightful tests of persuasion theories if instead of

abandoning their induction checks, they used them to test the causal models specified

by persuasion theories. One of the problems with failing to test models this way is

that researchers are less likely to find the proposed relationships when the intervening

variables are ignored because distal effects are necessarily weaker ones. O’Keefe

argued that one of the strengths of communication researchers is that they explore

exactly which attributes of a message affect attitudes via particular cognitive and

emotional states, rather than perceiving messages merely as means to induce those

states. If communication researchers wish to explore the effects of message features

more fully, they must test the degree to which their data are consistent with models

of the entire process they are studying using path modeling techniques, rather than

relying on simpler designs in which the effects of inductions on attitudes is tested

with no exploration of the intervening processes.

In the case of the dual-process models, measuring the amount of effort put forth by

the audience and the difficulty experienced in processing a given message reported

by the audience will provide a more rigorous test of the intervening variables proposed

by the ELM.Measuring these variables and systematically studying whichmessage vari-

ables affect them will help determine the validity of the dual-process predictions.

Specifically, variations in effort and difficulty are predicted to be associated with

changes in the likelihood that perceptions of argument quality will be affected by argu-

ment strength inductions. Once the audience forms these perceptions of argument

quality, their perceptions are predicted to be positively associated with their acceptance

of the message. Message inductions that are hypothesized to affect either effort or abil-

ity to process the message should be tested as exogenous variables in such a model.

414 C. J. Carpenter & F. J. Boster

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Such an approach to the ELM would have several advantages. First, Mongeau and

Stiff (1993) argued that ELM predictions essentially remain untested due to the lack

of research testing causal models of the theory. In addition, causal modeling may help

move persuasion research toward making point estimates of the effects of persuasion

variables, rather than the vague predictions of ‘‘more persuasive’’ or ‘‘less persuasive’’

that characterize most persuasion research. When the precise relationships among the

key variables can be estimated, the probable effects of messages can be better

predicted.

To test a causal model derived from dual-process reasoning, the effects of message

comprehensibility on the persuasion process will be modeled and the results of an

original study will be reported that was conducted to test the proposed model.

Message comprehensibility is an ideal variable to study because it has been indicated

as a key message variable in applied contexts such as advertising (Lowrey, 1998, 2008)

and health communication (Eagly & Warren, 1976). In addition, Hafer, Reynolds,

and Obertynski (1996) found that message comprehensibility had a strong impact

on difficulty of processing the message in a traditional dual-process study. This study

will serve as a replication of their work, as well as exploring the untested causal pro-

cesses implied in the dual-process reasoning that inspired that study. Processing

effort and difficulty of processing will be described and then followed by a description

of the proposed causal model.

Cognitive Effort

The ELM and the HSM both contend that the amount of effort people put into pro-

cessing a persuasive message varies on a continuum ranging from none to carefully

trying to examine the strength of the message arguments. This process was originally

described as part of information processing theory (McGuire, 1968). Several different

variables are theoretically associated with such cognitive effort. Previous research

indicates that personal involvement with the issue (Petty, Cacioppo, & Goldman,

1981) and need for cognition (Cacioppo, Petty, & Morris, 1983) are both predictors

of the audience putting forth a great deal of cognitive effort. Extensive cognitive effort

involves attempting to carefully examine the logic of the message and compare the

claims in the message to other previously learned information. The two models differ

in their descriptions of some of the motives for cognitive effort, but they share the

claim that it is a single continuum (Chaiken, Duckworth, & Darke, 1999; Petty,

Wheeler, & Bizer, 1999). Unfortunately, attempts to measure the amount of effort

the audience puts into message processing have been largely limited to one- or

two-item induction checks (e.g., Axsom, Yates, & Chaiken, 1987; Booth-Butterfield

& Gutowski, 1993; Cacioppo, Petty, Kao, & Rodriguez, 1986). In the marketing litera-

ture, some studies employed three- or four-item ad hoc measures that conflated

involvement and effort by combing items measuring both (e.g., Coulter, 2004;

Karson & Korgaonkar, 2001; Lord, Lee, & Sauer, 1995; Lowrey, 1998). In the ELM,

involvement and effort are conceptually distinct in that involvement in the issue is

what motivates the audience to put effort into evaluating the message. Beyond

Communication Quarterly 415

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reporting scale reliability, none of these studies tested the factor structure of these

scales or conducted other tests of validity.

Furthermore, the relationship between effort and perceptions or argument

strength or attitudes at different levels of argument strength was not reported in

any of these studies. Dual-process logic suggests that when the arguments are strong,

increases of processing effort will be associated with increasing acceptance of the

message recommendations and decreasing acceptance when the arguments are weak.

The relationship between induced variables that are hypothesized to motivate proces-

sing and attitudes have been tested many times and often demonstrate the predicted

pattern (Johnson & Eagly, 1989). The key variable of processing effort, however, has

never been directly related to changes in attitudes in response to strong and weak

arguments. Therefore, this relationship needs to be tested to effectively evaluate

dual-process predictions.

In an effort to improve on the one- or two-item ad hoc measures previously used

to measure processing effort, Reynolds (1997) produced the Reynolds Message

Elaboration Measure (RMEM). The measure is composed of 12 self-report items that

ask the audience to indicate the extent to which they were trying to carefully process

the message arguments. Reynolds reported excellent reliability and a valid factor

structure. In addition, Reynolds found evidence consistent with construct validity

as the RMEM was sensitive to instructions to read the message carefully or carelessly.

The scale also correlated with need for cognition and message involvement, both of

which have been theoretically and empirically associated with substantial cognitive

effort in dual-process research. The RMEM has not, however, been tested in a tra-

ditional dual-process study with an argument strength induction. RMEM scores

are hypothesized to be positively associated with perceptions of argument strength

and attitude change toward the message recommendations when the arguments

are strong and negatively associated when they are weak. The same relationships

are hypothesized for the impact of RMEM scores on attitudes. This study will be

the first to test key portions of dual-process reasoning using an independently

validated scale.

Processing Difficulty

The amount of effort the audience attempts to put into processing a persuasive

message carefully is only one predictor of central processing. The audience must also

have the ability to do so. In particular, some messages are more difficult to cogni-

tively process because they contain scientific jargon (Bradley & Meeds, 2004), have

their sentences in the wrong order (Eagly, 1974), or contain complex syntax (Lowrey,

1998). When the message is difficult to understand, it is harder for the audience to be

able to thoughtfully evaluate the strength of the arguments in support of the message

advocacy. For example, Hafer et al. (1996) manipulated the complexity of the struc-

ture and grammar of a persuasive message and found that when the message was easy

to comprehend, strong arguments were substantially more persuasive than weak.

When the message was difficult to understand, the effect of argument strength on

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attitudes was no longer statistically significant and in the wrong direction such that

weak arguments were slightly more persuasive than strong.

Although the impact of message comprehensibility inductions on attitudes has

been empirically demonstrated, the causal process mediating the relationship has

not been tested. Like processing effort, the difficulty of understanding the message

has been measured using ad hoc measures, but such measures have only been used

as an induction check (e.g., Chaiken & Eagly, 1976). If dual-process theories are cor-

rect, message comprehensibility inductions will affect the audience’s self-reported

ability to understand the message. In the causal model described later, this is illu-

strated by the prediction that perceptions of difficulty of processing are hypothesized

to be negatively associated with perceptions of argument strength when the argu-

ments are strong and positively when they are weak. The same relationships are

hypothesized for the effect of difficulty to understand the message on attitudes. No

study, to the authors’knowledge, has reported the correlation between perceptions

of processing difficulty and attitudes under varying levels of argument strength.

A Causal Model of Dual-Process Models

Despite Mongeau and Stiff’s (1993) call for ELM proponents to produce and test a

causal model of the ELM, very little empirical work has been conducted in this area.

A few studies employing causal modeling techniques have tested the valence of the

audience’s self-reported thoughts during message exposure as a mediator of message

features like argument quality (Stephenson, Benoit, & Tschida, 2001) and source

credibility (Manfredo & Bright, 1991), as well as mood inductions (Petty, Schumann,

Richman, & Strathman, 1993). None of these studies have reported a test of the

model as a whole with independent measures of processing effort and difficulty of

processing the message.

Figure 1 shows the proposed path model of cognitive effort and difficulty of pro-

cessing the message arguments. Both effort and ability are hypothesized to produce

additive effects on perceptions of argument strength. When the arguments are strong,

the effect of cognitive effort on perceptions of argument strength is predicted to be

positive and when the arguments are weak, the effect is predicted to be negative. The

opposite pattern is predicted for difficulty of processing. Perceptions of argument

strength are expected to be positively related to attitudes consistent with the message

recommendations. As message comprehensibility is expected to affect the audience’s

Figure 1 Proposed path model.

Communication Quarterly 417

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ability to process the message, a path is hypothesized from the argument comprehen-

sibility induction to difficulty of processing. This model does not represent the

entirety of dual-process predictions. The proposed model is designed to explore

message features that are specifically hypothesized to affect cognitive elaboration of

strong or weak arguments.

Attitude Certainty

In addition, the ELM proposes that increased levels of message processing produce

stronger attitudes such that when the audience processes the message more carefully,

they become more confident that their attitudes are correct (Petty & Cacioppo,

1986). The ELM argues that by processing message arguments centrally, the audience

is able to draw more connections between the message arguments and their overall

attitude. These connections give the audience a sense that they have carefully con-

sidered the message and have arrived at the best possible attitude. Therefore, proces-

sing effort is hypothesized to be positively related to attitude certainty whereas

difficulty of processing is hypothesized to be negatively related.

Method

Sample

Participants were 120 undergraduate students at a large Midwestern university. One

participant’s data were omitted because of an admission that she failed to read the

message. There were 56 (47.1%) male and 63 (52.9%) female participants. They were

recruited from undergraduate courses, and they were given course credit in exchange

for their participation. Their ages ranged from 18 to 47 (M¼ 20.43, SD¼ 2.95).

There were some cases in which a few participants did not respond to all of the items

in the measures. The variations in sample sizes reported for each test are due to this

variation in responding to all of the measures.

Design

This experiment employed a 2 (strong vs. weak arguments)� 2 (easy to understand

vs. difficult to understand arguments) fully crossed independent-groups design.1

Participants were randomly assigned to conditions with the constraint of equal cell

sizes.

To make certain that all of the participants were putting forth cognitive effort suf-

ficient to comprehend the lengthy message, the participants in all conditions were

told that the researchers were assisting the Education Department in developing tests

of college students’ aptitude. The instructions stated that at a later time experts would

view their responses, and that their names would be associated with their responses

should the experts needed to ask follow up questions. This method of inducing high

response involvement is similar to Chaiken’s (1980) method of inducing systematic

processing.

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The message was described as a newspaper editorial and concerned a fictitious

political candidate for the President of Ukraine named Khmelnyt.2 The topic mini-

mized variation in prior knowledge that might affect the participants’ difficulty of

processing the message (Bransford & Johnson, 1972). The message described several

problems with Ukraine and then offered explanations of how the candidate would

solve those problems. The message proposed that the audience should support this

fictitious individual’s candidacy for president.

The two levels of comprehension difficulty were induced by starting with a

moderately complex version of the arguments. Then, using recommendations from

Lowrey (2008), more complex language and structure were added to create the dif-

ficult to comprehend messages. The language and structure were simplified to create

easy to comprehend messages. For example, a sentence from one of the simple, weak

arguments read, ‘‘Khmelnyt wants stricter penalties for heroin users who use nee-

dles.’’ The parallel difficult to comprehend sentence was, ‘‘A proposition has been

constructed by Khmelnyt for the implementation of more draconian penalties for

heroin abusers who inject the drug intravenously using needles.’’ The two levels were

balanced carefully by examining differences in the Flesch Reading Ease score (Flesch,

1948). The easy to understand conditions were both scored above 50 and the difficult

to understand conditions were both scored below 20. These scores are consistent with

easy and difficult to understand messages.

Argument strength was induced by including sound logic with strong support in

the strong arguments and including logical errors with weak support in the weak

arguments. The strong arguments contained well-established evidence from qualified

sources, whereas the weak arguments contained weakly supportive evidence from

dubious sources. For example, a strong argument claimed that the candidate would

fight government corruption by creating an anticorruption bureau modeled after

successful programs in other eastern European countries. A prominent political

scientist was said to support this policy. The weak argument was that the candidate

wants to create a committee to make a report on some solutions to the corruptionproblem. Many problems have been solved by trying to make solutions so thiscommittee is sure to succeed. Some of the richest people in Ukraine like this answerto the bribery and corruption problem.

This supporting argument is illogical and the source supporting it is obviously

self-interested.3

Procedure

Participants logged onto an online data collection Web site that assigned them ran-

domly to one of the four conditions. They first viewed an informed consent form.

They were then presented with the cover story. Next, they were presented with the

persuasive message they had been assigned. The message was followed by the attitude

scale, a measure of attitude certainty, a measure of perceived difficulty of reading the

message, perceived argument strength (LaFrance & Boster, 2001), and the RMEM

(Reynolds, 1997). Finally, they were thanked and debriefed.

Communication Quarterly 419

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Instrumentation

The five-factor measurement model was tested using confirmatory factor analysis

(CFA) that tested for both internal consistency and parallelism (Hunter & Gerbing,

1982). The average error was unacceptably high (root mean square error

[RMSE]¼ 0.13. The correlation between the relationships predicted by the measure-

ment model and those obtained was also weaker than good model fit would demand,

r(559)¼ .91. Examination of the error matrix and the factor loadings showed that the

reverse-coded items in RMEM formed a separate factor from the positively coded

items. Subsequent CFA found that the reverse coded items did form a single factor

and that the items framed positively formed a separate factor. The six-factor

measurement model was a better fit with the data than the five-factor model

(RMSE¼ 0.10). The correlation between the relationships predicted by the measure-

ment model and those obtained was also larger for this model, r(559)¼ .94. A cor-

relation matrix of the inductions and measured variables can be found in Table 1.

The first set of questions measured attitudes toward the proposed candidate by

asking the participants to rate their attitude toward voting for the proposed candidate

on five 7-point semantic differential items anchored at wise to foolish, beneficial to

harmful, desirable to undesirable, good to bad, and advantageous to disadvantageous.

Higher scores indicated more favorable attitudes. Scores on these items were averaged

(mean) for each participant. The distribution of attitude scores was skewed some-

what negatively (M¼ 4.54, SD¼ 1.40) and formed a reliable scale (a¼ .94). The dif-

ference between the mean attitude score and the midpoint of the scale was statistically

significant, t(115)¼ 4.15, p< .001.

Attitude certainty was measured using five Likert items with 7-point response

scales. Two of the items were adapted from Bizer et al.’s (2006) attitude certainty

scale and three new ones were written to increase scale reliability.4 Higher scores indi-

cated more certainty. For each participant, the mean scores of these items were

employed as an attitude certainty index. The distribution of scores on this index

Table 1 Correlation Matrix of Induced and Measured Variables

Variable 1 2 3 4 5 6 7 8

1. Difficulty induction �0.01 0.01 �0.18 0.36 0.05 �0.14 0.05

2. Argument strength induction �0.01 0.43 0.08 0.00 0.41 �0.05 0.07

3. Attitudes 0.01 0.42 0.24 �0.11 0.86 0.08 0.09

4. Attitude certainty �0.17 0.08 0.22 �0.33 0.33 0.69 �0.18

5. Perceived difficulty 0.34 0.00 �0.10 �0.30 �0.15 �0.34 0.33

6. Perceived argument strength 0.05 0.40 0.81 0.30 �0.14 0.08 0.10

7. Evaluative processing �0.13 �0.05 0.07 0.61 �0.30 0.07 �0.39

8. Cognitive effort 0.04 0.06 0.08 �0.15 0.28 0.09 �0.32

Note. Correlations below the diagonal are raw correlations, and those above the diagonal have been corrected for

measurement error.

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approximated the normal distribution (M¼ 4.15, SD¼ 1.28) and was reliable

(a¼ .89).

Perceived difficulty of comprehending the arguments was assessed with five 7-point

semantic differential items. The items were anchored at easy to hard, simple to diffi-

cult, effortless to demanding, clear to confusing, and understandable to incomprehen-

sible. The distribution of this measure was symmetrical, albeit somewhat leptokurtic

(M¼ 3.74, SD¼ 1.38), and was reliable (a¼ .91). The difference between the mean

score and the scale midpoint was statistically significant, t(114)¼ 1.98, p¼ .05.

Participants next completed a perceived argument strength measure with seven,

7-point semantic differential items (LaFrance & Boster, 2001). The scale was slightly

leptokurtic (M¼ 4.51, SD¼ 1.28), and scores ranged from one to seven. The scale

was also reliable (a¼ .94). The difference between the mean score and the midpoint

of the scale was statistically significant, t(111)¼ 4.16, p< .001.

Participants also completed the RMEM (Reynolds, 1997). The scale was composed

of 12 items with 7-point response scales. Examination of the items placed in each fac-

tor suggests what each factor measures. The reverse coded items that make up one

factor are all concerned primarily with the degree of cognitive effort put forth by

the audience. Items such as ‘‘not really exerting your mind,’’ ‘‘taking it easy,’’ and

‘‘unconcerned with the ideas’’ (p. 270) all refer to how much cognitive work the par-

ticipant reported performing. This scale will be labeled the cognitive effort scale. On

the other hand, the positively worded items were concerned mostly with specific eva-

luative cognitions such as ‘‘reflecting on the implications of the arguments,’’

‘‘attempting to analyze the issues in the message,’’ and ‘‘searching your mind in

response to the ideas’’ (p. 270). This scale will be labeled the evaluative processing

scale. The distribution of the scores in the cognitive effort scale approximated closely

the normal distribution (M¼ 4.01, SD¼ 1.02) and formed a reliable scale (a¼ .79).

Scores on the evaluative processing scale approximated closely the normal curve

(M¼ 4.10, SD¼ 1.14) and the scale was reliable (a¼ .87). Both scales were con-

structed so that higher scores indicated more cognitive elaboration. The scales were

moderately correlated with each other, r¼ .34, r0 ¼ .41, p¼ .001.

Results

First, the effects of the inductions on themeasured variables were examined to establish

that the inductions were successfully induced and determine if the Hafer et al. (1996)

findings were replicated. Then the fit of the hypothesized causal model was tested for

each argument strength condition. Finally the hypothesized relationship between both

processing effort and difficulty of processing with attitude certainty will be examined.

Effects of the Inductions on the Dependent Variables

A two-way analysis of variance (ANOVA) was performed to assess the effectiveness of

the message difficulty induction. There was a substantial and statistically significant

main effect for the difficulty of understanding induction on the difficulty of

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understanding measure, F(1, 111)¼ 14.17, p< .001 (r¼ .34, r0 ¼ .36), such that the

difficult to understand arguments were rated as harder to understand (M¼ 4.20,

SD¼ 1.34) than the easy to understand arguments (M¼ 3.28, SD¼ 1.27). The argu-

ment strength main effect and the message difficulty� argument strength interaction

were both insubstantial and not statistically significant. These findings were consist-

ent with an effective difficulty of comprehension induction.

A two-way ANOVA was performed to assess the effectiveness of the argument

strength induction. The means and standard deviations can be found in Table 2.

There was a substantial and statistically significant main effect for the argument

strength induction on the perceived argument strength measure, F(1, 108)¼ 28.08,

p< .001 (r¼ .40, r0 ¼ .41), such that the strong arguments were rated as stronger

(M¼ 5.03, SD¼ 0.98) than the weak arguments (M¼ 4.00, SD¼ 1.27). The message

difficulty� argument strength interaction was modest in size but statistically signifi-

cant, F(1, 108)¼ 4.50, p¼ .04 (r¼ .18, r0 ¼ .19). In the difficult to read condition, the

strong arguments were rated as stronger than the weak, t(53)¼ 2.00, p¼ .05 (r¼ .26,

r0 ¼ .27). In the easy to read condition, the strong arguments were rated as substan-

tially stronger than weak arguments, t(55)¼ 4.35, p< .001 (r¼ .49, r0 ¼ .51). These

findings were consistent with an effective argument strength induction, although

the small interaction suggests that the induction was stronger in the easy to under-

stand condition than the difficult to understand condition. It is unsurprising that

the interaction occurred as perceived argument strength is hypothesized to precede

attitudes in the proposed model. The effects of the inductions on perceived argument

strength were expected to parallel those predicted for attitudes based on Hafer et al.’s

(1996) findings.

A two-way ANOVA was employed to estimate the effects of argument strength

and argument difficulty on attitudes. The means and standard deviations can be

found in Table 3. It was predicted that the Hafer et al. (1996) results would be repli-

cated such that the strong arguments would be more persuasive than the weak but

that this relationship would be stronger when the arguments were easier to read.

There was a substantial and statistically significant main effect for argument strength,

F(1, 112)¼ 26.59, p< .001 (r¼ .42, r0 ¼ .43), such that strong arguments (M¼ 5.14,

SD¼ 1.24) were more persuasive than the weak arguments (M¼ 3.96, SD¼ 1.30).

Table 2 The Effects of the Inductions on Mean Perceived Argument

Strength Scores

Difficulty

Argument strength

Easy Difficult

M SD M SD

Strong 5.18 (1.02) 4.86 (0.91)

Weak 3.69 (1.52) 4.31 (1.11)

422 C. J. Carpenter & F. J. Boster

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This main effect was qualified by a substantial and statistically significant interaction

between argument strength and difficulty, F(1, 112)¼ 12.39, p¼ .001 (r¼ .29,

r0 ¼ .30). Examination of the means demonstrated that the argument strength effect

was stronger in the easy to understand condition, t(57)¼ 6.42, p< .001 (r¼ .65,

r0 ¼ .67), than in the difficult to understand condition, t(55)¼ 1.11, p¼ .27 (r¼ .15).

A two-way ANOVA was employed to estimate the effects of message difficulty and

argument strength on attitude certainty. There was a small effect of the difficulty

induction that approached conventionally employed levels of statistical significance,

F(1, 113)¼ 3.37, p¼ .07 (r¼ .17, r0 ¼ .18), such that attitude certainty was slightly

higher when the arguments were easier to read (M¼ 4.37, SD¼ 1.30) than when they

were difficult to read (M¼ 3.93, SD¼ 1.23). The argument strength main effect and

the message difficulty� argument strength interaction were both insubstantial and

not statistically significant. There were no substantial or statistically significant effects

of the inductions on the elaboration measures.

Test of the Proposal Causal Models

The proposed model will be tested separately for the strong argument condition and

the weak argument condition as some of the relationships in the model were

hypothesized to differ across those conditions. As the RMEM scale has been subdi-

vided into two different measures, the model was initially modified such that cogni-

tive effort and evaluative processing were both exogenous, independent predictors of

perceived argument strength. These measures take the place of reported processing

effort in the original model. The proposed model was first tested for the participants

in the weak argument condition.5 The model exhibited poor fit (RMSE¼ 0.14). Sev-

eral changes were made to the original model based on examination of the error

matrix. The revised model is shown in Figure 2 with standardized path coefficients.

First, evaluative processing was not a substantial predictor of perceived argument

strength and it was removed from the model. Examination of the error matrix sug-

gested several other modifications. The difficulty induction had a direct effect on atti-

tudes. In addition, perceived difficulty had a direct effect on cognitive effort, as well

as its original path to perceived argument strength. The data were adequately consist-

ent with the revised model, v2(4, N¼ 46)¼ 0.46, p¼ .98 (RMSE¼ 0.08).

Table 3 The Effects of the Inductions on Mean Attitude Scores

Difficulty

Argument strength

Easy Difficult

M SD M SD

Strong 5.52 (1.06) 4.74 (1.30)

Weak 3.56 (1.27) 4.37 (1.22)

Communication Quarterly 423

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The data also fit the model poorly for the strong argument condition

(RMSE¼ 0.23). Again, several changes were made to the proposed model to produce

adequate fit based on examination of the error matrix. The revised model is shown in

Figure 3 with standardized path coefficients. First, the path representing the effect of

cognitive effort on perceived argument strength was insubstantial and the variable

was, therefore, removed from the model. In the weak argument model evaluative

processing had been removed instead. Again, examination of the error matrix sug-

gested that the difficulty induction had a direct effect on attitudes and a path was

added to the model reflecting that direct effect. Similar to the model for processing

weak arguments, a direct path was added from perceived difficulty to the evaluative

processing scale (as opposed to the cognitive effort scale in the weak argument

model). The data were adequately consistent with the revised model, v2(4,N¼ 47)¼ 0.37, p¼ .99 (RMSE¼ 0.07).6 In both models adding a path from the dif-

ficulty induction to attitudes improved model fit and in both models adding a path

from perceived difficulty of processing to the aspect of cognitive processing that had

a substantial effect on perceived argument strength improved model fit. The simi-

larity between the revisions to both models suggests that the revisions were not

simply capitalizing on chance.

Attitude Certainty

It was predicted that elaboration would be related substantially and positively to atti-

tude certainty. This relationship was not predicted to be moderated by argument

strength. Attitude certainty was related substantially to evaluative processing

(r¼ .61, r0 ¼ .69, p< .001), but not to cognitive effort (r¼ .15, r0 ¼ .18, p¼ .11).

Figure 2 Obtained path model for responses to weak arguments with standardized path coefficients.

Figure 3 Obtained path model for responses to strong arguments with standardized path coefficients.

424 C. J. Carpenter & F. J. Boster

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The difference between these two correlations is statistically significant, z¼ 4.01,

p< .001. The evaluative processing measure’s substantial correlation with attitude

certainty was consistent with the hypothesis but the cognitive effort measure’s weak

correlation was not.

Finally, it was predicted that perceived difficulty of processing would be related

substantially and negatively to attitude certainty. The data were consistent with that

prediction (r¼�.22, r0 ¼�.24, p¼ .03). The more difficult it was for the audience to

process the message, the less certain they were that their attitudes were correct.

Discussion

This study attempted to determine if the predictions of dual-process models of

persuasion (Chaiken, 1980; Petty & Cacioppo, 1986) could be modeled and tested using

continuous measures of the key constructs. This study went beyond replicating the inter-

action of message comprehension difficulty and argument strength found in Hafer et al.

(1996). The standardized coefficients in the path models shown in Figures 2 and 3

indicate that when arguments were strong, perceptions of difficulty of processing were

associated with less persuasion whereas perceptions of difficulty were associated with

increased persuasion when the arguments were weak. Results for the amount of

self-reported cognitive effort paralleled those of perceptions of difficulty such that

increased effort was associated with less persuasion when the arguments were weak

and more when they were strong. The data were not consistent with the proposed path

model, but the revised path model generated new insights into persuasion.

The Revised Path Model

The proposed model was based on previous dual-process theorizing and research. A

model was proposed to test the mediated processes suggested by Hafer et al.’s (1996)

interpretation of the ELM. Just as Hafer et al. found that strong arguments were more

persuasive when the arguments were easy to understand than when they were difficult

to understand, perceived difficulty of comprehension was predicted to be negatively

associated with perceived argument strength when the arguments were strong. The

opposite pattern was predicted for weak arguments such that increases in difficulty

of comprehension were predicted to be positively associated with perceptions of

argument quality. Perceived difficulty of processing and self-reported processing

effort were predicted to additively affect the audience’s perceptions of argument

strength. Perceptions of argument strength were predicted to be positively associated

with attitudes toward the message advocacy.

The data were not, however, consistent with this model. To achieve adequate fit

several modifications had to be made. In both the weak argument model and the

strong argument model, one of the processing measures was dropped from the model

due to its insubstantial path coefficient. For the weak arguments condition, evaluative

processing was removed from the model whereas cognitive effort was removed in the

strong arguments condition. This result was not anticipated, but it suggests that the

Communication Quarterly 425

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processing style required to reject weak arguments may differ from the style required

to accept strong ones. For weak arguments, general cognitive effort decreased the

audience’s acceptance of the arguments whereas strong arguments required specific

evaluative cognitions such as carefully examining the proposed arguments and testing

their logic to be fully accepted. It may be that rejecting poor arguments requires only

that the audience be paying attention whereas to appreciate the merits of a well-argued

position, the audience needs to go a step further and actually engage in critical think-

ing. According to dual-process theories (Petty & Cacioppo, 1986), rejecting weak

arguments and accepting strong arguments utilize the same cognitive processes.

Although the results of this study question that assumption, additional research in this

area is required to determine if the ELM’s contention is invalid.

A path indicating a direct causal effect of the difficulty induction on attitudes was also

added to the model to achieve adequate model fit. The direction of the effect was mod-

erated by argument strength such that the easier to read arguments were less persuasive

than the difficult to read arguments when the arguments were weak and the effect was

reversed when the arguments were strong such that the easier to read arguments were

more persuasive than the difficult. The direct path was required because the effect of

the difficulty induction on attitudes was too strong to have been completely mediated

by the measured variables. The existence of other mediators is possible and seems likely

given that the path coefficient was still somewhat modest. This finding suggests that the

impact of comprehension difficulty induced by message complexity may have a more

complex relationship with attitudes than previous research and theory suggests.

The third revision to the proposed model required that perceived difficulty had a

non-spurious effect on cognitive effort in the weak arguments model and on evalua-

tive processing in the strong arguments model. This finding suggests that ability and

effort are not completely independent constructs. In both cases, self-reported effort

was reduced as self-reported difficulty increased. As the audience has greater dif-

ficulty understanding the message, they report putting forth less effort in processing

that message. Perhaps in the case of weak arguments overall cognitive effort was

reduced as difficulty increased because the audience concluded that it was not worth

their time to attempt to comprehend specious arguments. In the strong argument

condition, they may not have had enough cognitive effort left to spend on specific

evaluative processing when the task of understanding the message used up their cog-

nitive resources. The need for this revision to achieve model fit again illustrates the

importance of using causal modeling to understand these processes. Recall that

according to the ANOVA results, the difficulty induction did not have a substantial

impact on cognitive effort in the weak arguments condition and evaluative processing

in the strong arguments condition. It was the audience’s perception of difficulty of

processing that affected the effort variables.

Two-Factor Model of Elaboration

This study found data consistent with a two-factor model of cognitive elaboration.

Cognitive effort is usually defined as a unidimensional construct in persuasion

426 C. J. Carpenter & F. J. Boster

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research, yet there may be two different aspects measured by the two factors found

here. Although the two measures were correlated, they may be conceptually orthog-

onal. For some people, evaluating arguments may require cognitive skills that come

easily to them; thus, they can report that they performed these cognitive tasks on the

evaluative processing scale without reporting much exertion on the cognitive effort

scale. Those with extensive background knowledge on the subject may evaluate thor-

oughly with little effort. On the other hand, other people may believe they are exert-

ing a great deal of cognitive effort but are not specifically considering argument

implications. They may disagree with the statement that they are ‘‘taking it easy’’

because they are spending a great deal of cognitive effort on simply understanding

the message without also evaluating it.

The differential correlation of the two scales with attitude certainty is consistent

with the possibility that the two variables are measuring different aspects of cognitive

processing. Evaluative processing was related positively and substantially with atti-

tude certainty but cognitive effort had no substantial effect on attitude certainty.

Bizer et al. (2006) found that attitude certainty is the result of evaluative argument

elaboration using the kinds of evaluative cognitive skills described in the items that

make up the evaluative processing factor, rather than the simple comprehension of

the message.

Dual-process models include cognitive processing effort as a key construct; there-

fore, future work is needed to determine the nature of that construct by examining

the two aspects identified in this study. Future work could attempt to measure these

two aspects of elaboration separately and determine if increases in mere cognitive

effort or specific cognitive activities are related to an increased likelihood of distin-

guishing strong from weak arguments. Further research is required to determine if

the patterns detected in this study can be replicated.

Limitations

One limitation of this study is that it only attempted to model the effects of a message

variable on ability to process. It did not examine the effects of any of the variables

thought to affect motivation to process such as personal involvement (Petty et al.,

1981) or need for cognition (Cacioppo et al., 1983). Future work attempting to

model both motivation and ability variables would produce a more complete picture

of the cognitive processes associated with persuasion.

In addition, the sample size is somewhat small. Although this study was able to

detect statistically significant relationships, the paths in the model were not estimated

with enough precision as would be desirable. Future research with larger samples

would strengthen the case that the causal models presented, as well as the two-factor

structure in the RMEM, are robust.

When the two models are separated by argument strength, there may have been

range restriction in perceived argument strength. The standard deviation for the weak

argument condition (SD¼ 1.35) was nearly identical to the full sample (SD¼ 1.28),

Communication Quarterly 427

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but the standard deviation was lower in the strong argument condition (SD¼ 0.98).

Levene’s test for equality of variances between the strong and weak argument con-

dition was nearly statistically significant (p¼ .06), which is consistent with this

possibility.

Conclusion

This study has demonstrated the value of testing dual-process theories of per-

suasion by measuring the relevant constructs and examining the relationships

among those variables in a path model. This method revealed insights about the

impact of message complexity inductions not uncovered by previous research

employing ANOVA tests. In particular, message complexity may have effects on

attitudes not mediated by its effects on either ability or effort to process a persuas-

ive message. In addition, strong and weak arguments may require different kinds of

cognitive processing by the audience to be accepted or rejected. It is time per-

suasion researchers moved past the seductive simplicity of traditional tests of

dual-process models and embraced the complexity of modeling the cognitive pro-

cesses involved in persuading an audience.

Notes

[1] The data examined here were collected as part of a larger study such that other groups whose

data were not examined here were given additional processing instructions.

[2] The messages used can be obtained by contacting Christopher J. Carpenter.

[3] A pilot test established that comprehension difficulty and argument strength were success-

fully induced using the same messages and the same measures as the focal study (for details,

see Carpenter & Boster, 2011).

[4] The full scale is available from Christopher J. Carpenter.

[5] For all correlations reported used to test the model, scatterplots were examined and no evi-

dence of nonlinearity was found. Restriction in range in one of the variables for one group

and not the other or differential measurement reliability may explain why two correlations

are different. For all of the analyses reported, differential variance was unlikely because the

differences in variance were trivial and Levene’s test was not statistically significant for any of

them. Differential measurement reliability was also unlikely to be a problem as the reliability

of the scales also did not substantially differ by condition.

[6] The revisedmodels for strong and weak arguments were tested together using multiple-groups

analysis with Amos (Amos Development Corporation, Crawfordville, FL). The fit indexes

indicated excellent model fit, v2(8,N¼ 93)¼ 5.22, p¼ .73 (normed fit index¼ 0.97, compara-

tive fit index¼ 1.00, root mean square error of approximation¼ 0.00).

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