Imagined Interactions and memorable messages

Students’ Imagined Interactions as Intrapersonal Explanations for Instructional Dissent Alan K. Goodboy, San Bolkan, & Zachary W. Goldman

The purpose of this study was to examine how college students’ intrapersonal

communication experiences (i.e., imagined interactions) with disliked instructors

contribute to their proclivity to communicate instructional dissent (i.e., expressive, rhe-

torical, vengeful). Student participants (N¼ 181) completed a self-report questionnaire

measuring their use of imagined interactions with their worst instructor in the past

academic year, along with reports of their course-related dissent. Results of a canonical

correlation revealed that the frequency, valence, and rehearsal of students’ imagined

interactions with a low affect instructor are related to forms of instructional dissent.

Keywords: Affective Learning; Imagined Interactions; Instructional Dissent; Student

Dissent

When college students are displeased with their classroom experiences, they

frequently engage in instructional dissent (Goodboy, 2011a) which emerges ‘‘when

students express their disagreements or complaints about class-related issues’’

(Goodboy, 2011b, p. 423). Research suggests that a variety of instructor messages

and behaviors are likely to trigger instructional dissent from students including stu-

dent favoritism, grading mistakes, unfair testing, unrealistic classroom policies, and

student bullying, to name a few (Bolkan & Goodboy, 2013; Goodboy, 2011a,

2011b; Horan, Chory, & Goodboy, 2010; Martin, Goodboy, & Johnson, 2013).

Alan K. Goodboy (PhD, West Virginia University, 2007) is an Associate Professor in the Department of

Communication Studies at West Virginia University. San Bolkan (PhD, University of Texas-Austin, 2007) is

an Associate Professor in the Department of Communication Studies at CSU-Long Beach. Zachary W. Goldman

(MA, West Virginia University, 2012) is a PhD student in the Department of Communication Studies at West

Virginia University. Correspondence to: Alan K. Goodboy, 108 Armstrong Hall, P.O. Box 6293, Morgantown,

WV 26506-6293, USA. E-mail: [email protected]

Communication Reports

Vol. 28, No. 2, July–December 2015, pp. 115–127

ISSN 0893-4215 (print)/ISSN 1745-1043 (online) # 2015 Western States Communication Association

DOI: 10.1080/08934215.2014.936563

Goodboy (2011a) found that these triggering agents encourage students to dissent in

three distinct ways: expressive dissent, rhetorical dissent, and vengeful dissent. First,

expressive dissent occurs when students vent their frustrations about class to others

to gain sympathy or empathy. Next, rhetorical dissent occurs when students voice

their concerns directly to the instructor in hopes of rectifying a perceived problem.

Finally, vengeful dissent occurs when students spread negative messages about an

instructor to seek revenge and damage the instructor’s reputation or career.

Research has clearly established that much of instructional dissent is the result of

what ineffective instructors do (or fail to do) in class (Goodboy, 2011a, 2011b;

Holmgren & Bolkan, 2014; Horan et al., 2010; LaBelle, Martin, & Weber, 2013;

Martin et al., 2013; Vallade, Vela, & Martin, 2013) and that students attribute blame

to their instructors as the cause for their dissent responses (Goodboy, 2011a; LaBelle

& Martin, 2014). However, instructional dissent is influenced by a variety of student

characteristics and predispositions as well (Goodboy, 2012). For example, students’

personality traits (Buckner & Finn, 2013; Goodboy & Martin, 2013; Goodboy &

Myers, 2012), conflict styles (Goodboy & Bolkan, 2013), academic beliefs (Bolkan

& Goodboy, 2013; Goodboy & Frisby, 2014; LaBelle et al., 2013), and learning experi-

ences (Goodboy, 2011b) all play an important role in how students dissent.

Although some students confront their instructors with their class-related

problems (Horan et al., 2010), recent research suggests that most students decide

to withhold rhetorical dissent and keep their complaints to themselves (Bolkan &

Goodboy, 2013). One of the reasons students choose not to dissent to their instruc-

tors is because they fear retaliation or repercussions from the instructor (Bolkan &

Goodboy, 2013) which may reflect a student’s desire to avoid potential conflict.

Other research supports this conclusion as students with an avoiding conflict style

communicate less rhetorical dissent (Goodboy & Bolkan, 2013). Because many stu-

dents decide whether to dissent to instructors (or not) after thinking and carefully

weighing the consequences of their actions (Bolkan & Goodboy, 2013), it is likely that

some use intrapersonal forms of communication to manage their negative affect

toward the instructor and class (Goodboy, 2011b) when processing dissent triggering

episodes. Specifically, Stacks and Andersen (1989) noted that cognition and affect

‘‘often function conjointly’’ (p. 278) in the intrapersonal communication process,

and imagined interactions may represent a type of intrapersonal coping response stu-

dents use when they feel the need to dissent (Berkos, Allen, Kearney, & Plax, 2001;

Honeycutt, 2010).

Imagined Interactions

Imagined interactions (IIs) refer to a type of daydreaming (Honeycutt, 2003) in

which ‘‘individuals imagine themselves in anticipated or recalled interaction with

others’’ (Honeycutt & Ford, 2001, p. 316). Imagined interactions reflect a process

of social cognition in the form of intrapersonal communication (Honeycutt, 2010)

in which individuals conjure verbal or visual imagery in mentally rehearsed hypo-

thetical conversations (Zagacki, Edwards, & Honeycutt, 1992). As Rosenblatt and

116 A. K. Goodboy et al.

Meyer (1986) explained, ‘‘These interactions may be fragmentary or extended, may

ramble, stay on track, or recurrently go over the same matter’’ (p. 319). Imagined

interactions tend to involve more self-talk versus other-talk, occur more frequently

before actual interactions than after conversations, tend to be about more personal

than impersonal topics (Honeycutt, Zagacki, & Edwards, 1990), and are equally

pleasant and unpleasant (Edwards, Honeycutt, & Zagacki, 1988).

Imagined interactions have a variety of characteristics and serve numerous

functions (Bodie, Honeycutt, & Vickery, 2013). The characteristics of IIs include fre-

quency (i.e., how often they occur), proactivity (i.e., the extent to which they occur

before anticipated encounters), retroactivity (i.e., the extent to which they occur after

an encounter), variety (i.e., how they occur across a variety of topics and people),

discrepancy (i.e., IIs that play out differently than actual conversations), self-

dominance (i.e., the extent to which they involve self-talk), valence (i.e., the degree

of pleasantness), and specificity (i.e., the details of the imagery during IIs; Honeycutt

& Ford, 2001). Moreover, the functions of IIs are self-understanding (i.e., to better

understand oneself), rehearsal (i.e., to mentally plan out what to say), catharsis

(i.e., to relieve tension or uncertainty), compensation (i.e., to serve in place of a real

conversation), relational maintenance (i.e., to keep a relationship alive), and conflict

management (i.e., to address conflict). The functions and uses of IIs are predicted by

individual differences such as the five factor model of personality (Honeycutt, Pence,

& Gearhart, 2012–2013), covert narcissism (Honeycutt, Pence, & Gearhart, 2013),

attachment (Honeycutt, 1998–1999), argumentativeness and verbal aggressiveness

(Bolkan & Goodboy, 2011), Machiavellianism (Allen, 1990), locus of control

(Honeycutt, Edwards, & Zagacki, 1989–1990), communication apprehension (Bolkan

& Goodboy, 2011; Honeycutt, Choi, & DeBerry, 2009), taking conflict personally

(Wallenfelsz & Hample, 2010), and Myers-Briggs personality preferences (Honeycutt

& Keaton, 2012–2013). In addition, the use of IIs are associated with relational

features such as uncertainty (Van Kelegom & Wright, 2013), anxiety (Allen &

Honeycutt, 1997), emotional responses (Honeycutt et al., 1989–1990; Honeycutt,

Nasser, Banner, Mapp, & DuPont, 2008), loneliness (Honeycutt et al., 1990), marital

ideology (Honeycutt, 1998–1999), relational quality (Honeycutt, 2008–2009), talk in

marriage (Honeycutt & Wiemann, 1999), and intrapersonal communication satisfac-

tion (Honeycutt & McCann, 2008).

Imagined interactions have been studied across a variety of relationships including

college roommates (Honeycutt & Patterson, 1997), parent=child relationships (Allen,

Edwards, Hayhoe, & Leach, 2007), married couples (Honeycutt & Keaton, 2012–

2013; Honeycutt & Wiemann, 1999), small groups (Turner, Crisp, & Lambert,

2007) and consumer=business relationships (Bolkan & Goodboy, 2011). However,

important to our purposes is the fact that IIs play an important role in the college

classroom as well. In particular, research suggests that college students use IIs to cope

with and process unwanted interactions with their instructors. For example, Berkos,

Allen, Kearney, and Plax (2001) revealed that college students use IIs to process

instructor misbehaviors, and that they use IIs as a substitute for confronting misbe-

having instructors. Moreover, Berkos (2012–2013) discovered that students who use

Communication Reports 117

IIs before e-mailing an instructor are more likely to use prosocial compliance-gaining

strategies and less likely to make verbal demands in an e-mail. Because instructional

dissent is considered a response to potential instructor–student conflict (Goodboy &

Bolkan, 2013), and because IIs are most commonly used to process and rehearse per-

ceived conflict (Allen & Berkos, 2005–2006; Honeycutt, 2003–2004; Zagacki et al.,

1992), it is our contention that IIs are used by students who have a desire to

communicate instructional dissent.

Crucially, the characteristics of IIs are important to consider in dissent expression.

For instance, as it pertains to frequency and pleasantness, researchers have found that

these characteristics of IIs are related to positive relational outcomes (Honeycutt &

Wiemann, 1999). Specifically, the authors found that individuals who have positive

and frequent imagined interactions are more likely to enjoy serious discussions

and are likely to believe that their relationships are more egalitarian than individuals

who do not. Moreover, Honeycutt and Wiemann (1999) found that the sharing of

interpersonal influence was associated with relational satisfaction and positively

valenced IIs as well. Considering that two types of dissent (i.e., expressive and venge-

ful dissent) communicate little desire to engage in relational maintenance (Bodie

et al., 2013), and given that students with unsatisfactory relationships with their

instructors tend not to dissent in constructive ways (Bolkan & Goodboy, 2013), we

expected that the frequency and negative valence of IIs will predict these two dissent

types. Particularly, we expected that students would engage in more antisocial (i.e.,

vengeful) and selfish (i.e., expressive) forms of dissent when they have frequent= negative valenced IIs about their instructor. Therefore, the first hypothesis is offered:

H1: II characteristics (i.e., frequency and negative valence) will predict students’ expressive and vengeful dissent responses with low affect instructors.

However, given the relationship between frequent and positive IIs and both percep-

tions of relational power and relational satisfaction, we believed that if students have

frequent=positive valenced IIs about their instructors, they should be more likely to

approach these individuals with class-related concerns (Honeycutt & Wiemann,

1999). Therefore, the second hypothesis is offered:

H2: II characteristics (i.e., frequency and positive valence) will predict students’ rhetorical dissent responses with low affect instructors.

Likewise, since instructor misbehaviors are a leading cause of student dissent

(Goodboy, 2011a, 2011b; Vallade et al., 2013), and because many students use IIs

as a substitute for direct communication when misbehaviors are present (Berkos

et al., 2001), it is likely that students who have a desire to dissent use IIs as an coping

mechanism to accompany actual dissent. To examine this idea, the following research

question is offered:

RQ: To what extent do the functions of IIs (i.e., rehearsal, self-awareness, catharsis) predict students’ instructional dissent responses (i.e., expressive, rhetorical, vengeful) with low affect instructors?

118 A. K. Goodboy et al.

Method

Participants

Participants were 181 undergraduate students (107 men, 70 women, 4 participants

did not report their sex) whose ages ranged from 18 to 35 years (M¼ 21.24,

SD¼ 2.13). Participants were recruited from a large Northeastern university. One

hundred four students reported on a class with a male instructor and 77 students

reported on a class with a female instructor. Approximately 48% (N¼ 87) of the sam-

ple reported on a college course required for their major. Class sizes varied with 67

students (37.0%) reporting on a class consisting of 30 students or less, 38 students

(21.0%) reporting on a class with 31 to 100 students, 48 students (26.5%) reporting

on a class with 101 to 200 students, and 28 students (15.5%) reporting on a large

lecture class with over 200 students enrolled.

Procedures and Instrumentation

After obtaining IRB approval, participants completed a questionnaire using methods

proposed by Richmond, McCroskey, Kearney, and Plax (1987) that asks students to

report on their ‘‘worst’’ instructor in the past academic year. This method was used

to ensure that students reported on a low affect instructor in order to maximize

potential instructional dissent episodes resulting from student dissatisfaction. The

questionnaire included measures of students’ imagined interactions, instructional

dissent, affective learning toward the instructor, and demographic items.

Instructional dissent was operationalized using the Instructional Dissent Scale (IDS;

Goodboy, 2011b), which is 22 items and asks students to report on how often they

express their disagreements or complaints about class-related issues by using express-

ive dissent (10 items), rhetorical dissent (6 items), and vengeful dissent (6 items).

Responses were solicited using a 5-point Likert-type response format ranging from

(0) never to (4) very often. In this study, obtained Cronbach alphas were .91 for

expressive dissent (M¼ 2.63, SD¼ .89), .92 for rhetorical dissent (M¼ 1.47, SD¼ 1.11), and .90 for vengeful dissent (M¼ .91, SD¼ 1.05).

Imagined interactions were operationalized by using items from the Survey of

Imagined Interactions (SII; Honeycutt, 2010) and Students’ Imagined Interactions with

Teachers Scale (Berkos et al., 2001). All II responses were solicited on a 7-point Likert

response format ranging from (1) strongly disagree to (7) strongly agree. The SII was

adapted slightly to reflect IIs about a specific instructor instead of a global assessment

of IIs. Two subscales were used to measure the frequency (4 items) in which students

used IIs, and the valence (4 items) of IIs with a target instructor (higher scores indi-

cate a positive valence). In this study, obtained Cronbach alphas were .86 (M¼ 3.62,

SD¼ 1.51) and .74 (M¼ 2.90, SD¼ 1.15) respectively. The Berkos et al. (2001) scale

was used to measure three functions of students’ IIs with an instructor including

rehearsal (9 items), self-awareness (9 items), and catharsis (9 items). In this study,

obtained Cronbach alphas were .91 (M¼ 4.33, SD¼ 1.40), .87 (M¼ 4.04, SD¼ 1.17),

and .85 (M¼ 4.24, SD¼ 1.16) respectively.

Communication Reports 119

Affective learning was operationalized by using the Construct 7: Attitude Toward

Instructor subscale from Mottet and Richmond’s (1998) Revised Affective Learning

Measure. This measure was used to ensure that students were reporting on low affect

instructors who were likely to provoke dissent. This subscale is 4 items and asks stu-

dents to report on how favorable they view a target instructor. Responses were soli-

cited using a 7-point semantic differential response format using the following

anchors: good=bad, worthless=valuable, fair=unfair, and positive=negative. The

obtained Cronbach alpha for this subscale was .72 (M¼ 3.26, SD¼ 1.18). Individual

item means ranged from 2.84 (SD¼ 1.56) to 3.65 (SD¼ 1.54) on a 7-point scale. The

composite item mean of 3.26 was significantly lower (t (178)¼�8.35, p< .001; mean

difference¼�.74) than a theoretical mean of 4.0. These results indicate that students

did, in fact, report on low affect instructors.

Results

Prior to exploring the hypothesis and research question, a first order correlation

matrix was computed for the variables. Because affective learning is related inversely

to student reports of dissent (Goodboy, 2011b), partial correlations were calculated

for the variables of interest by controlling for student affect for the instructor. This

was done to control for any confounding effects stemming from students’ varying

reports in negative affect intensity because students’ feelings about an instructor

are a cause of dissent to begin with (Bolkan & Goodboy, 2013; Goodboy, 2011a,

2011b). In this study, student affect for the instructor was correlated significantly

with the frequency (r¼�.22, p< .01) and valence (r¼ .44, p< .001) functions of

IIs, the rehearsal characteristic of IIs (r¼�.18, p< .05), along with expressive

(r¼�.31, p< .001) and vengeful dissent types (r¼�.27, p< .001). Therefore, affect

toward the instructor served as an appropriate covariate. Results of partial

correlations are available in Table 1.

Table 1 Partial Correlations Controlling for Affective Learning

Variables 1 2 3 4 5 6 7

Imagined Interactions

1. Frequency of IIs —

2. Valence of IIs .02 (.09) —

3. Rehearsal .61 (.68)^ .04 (.10) —

4. Self-Awareness .53 (.61)^ .15 (.24) �

.72 (.81)^ —

5. Catharsis .42 (.49)^ .01 (.06) .55 (.63)^ .63 (.73)^ —

Instructional Dissent

6. Expressive .29 (.31)^ �.19 (�.18) ��

.23 (.24) ��

.13 (.13) .10 (.09) —

7. Rhetorical .30 (.36)^ .09 (.10) .18 (.21) ��

.12 (.14) .04 (.06) .34 (.39)^ —

8. Vengeful .21 (.22)�� .08 (.18) .02 (.00) .00 (�.01) .02 (.00) .26 (.26)^ .53 (.61)^

Note. Partial correlations control for affective learning toward instructor and are flagged with �p< .05, ��p< .01,

^p< .001. Disattenuated partial correlations are in parentheses.

120 A. K. Goodboy et al.

Hypotheses 1 and 2 predicted that II characteristics (i.e., frequency and valence)

would predict students’ instructional dissent responses (i.e., expressive, rhetorical,

vengeful) with low affect instructors, and the research question inquired about the

role of II functions (i.e., rehearsal, self-awareness, catharsis) in this process. To

examine these relationships, a canonical correlation was computed with the five IIs

variables serving as predictors of the three dissent types. Only structure coefficients

above .45 were interpreted (Sherry & Henson, 2005). Collectively, the full model

across all functions was statistically significant, Wilks’s k¼ .69; F(15, 475.22)¼ 4.54, p< .001. With 1 – k yielding the full model effect size, the full model explained

31% of the variance shared between the variable sets. Dimension reduction analysis

showed that the full model (functions 1 to 3) was significant (see above), and func-

tions 2 to 3 were also statistically significant (F(8, 346)¼ 3.12, p¼ .002). Function 3,

which was the only function tested in isolation, did not explain a statistically signifi-

cant amount of shared variance in the variable sets, (F(3, 174)¼ 1.87, p¼ .14).

Results of the first two functions, including structure coefficients, squared structure

coefficients, communality coefficients, and redundancy coefficients, are available in

Table 2.

The first function (Rc¼ .453, R2 c ¼ .206) revealed that when students had frequent,

negatively valenced, and rehearsed IIs about their low affect instructor, they commu-

nicated more expressive dissent, and to a lesser extent, vengeful dissent. The second

function (Rc¼ .320, R2 c ¼ .102), which accounts for variance remaining after the first

function has been extracted (Thompson, 1984), revealed that when students had

Table 2 Canonical Solution for Characteristics and Functions of IIs Predicting

Instructional Dissent for Functions 1 and 2

Function 1 Function 2

Variables rs r2 s (%) rs r2

s (%) h2

Set 1: Imagined Interactions

Frequency .793 62.88 .542 29.38 92.26

Valence �.646 41.73 .725 52.56 94.29

Rehearsal .567 32.15 .214 4.58 36.73

Self-Awareness .317 10.04 .176 3.10 13.14

Catharsis .314 9.85 �.068 .46 10.31

Redundancy Coefficient (.313) (.180)

Set 2: Instructional Dissent

Expressive .985 97.02 �.046 .21 97.23

Rhetorical .311 9.67 .943 88.92 98.59

Vengeful .473 22.37 .453 20.52 42.89

Redundancy Coefficient (.089) (.037)

Note. Wilks’s k¼ .69; F(15, 475.22)¼ 4.54, p< .001. rs¼ structure coefficient; r2 s ¼ squared structure coefficient;

h2¼ communality coefficient. Structure coefficients (rs) greater than .45 are in bold. Communality coefficients

(h2) greater than 45% are in bold.

Communication Reports 121

frequent, but positively valenced IIs about their low affect instructor, they commu-

nicated rhetorical dissent, and to a lesser extent, vengeful dissent.

Discussion

The purpose of this study was to examine the role of students’ imagined interactions

in the instructional dissent process. Overall, the results suggest that the frequency and

valence of students’ imagined interactions with their low affect instructors matter the

most, and the rehearsal function plays a less important role. That is, students who

have frequently rehearsed IIs that are negative prefer to engage in expressive dissent,

but in contrast, students who have frequent IIs that are positive prefer to engage in

rhetorical dissent. Regardless of the valence, students reported that with low affect

instructors, they engaged in vengeful dissent to a small degree. These results lend full

support for hypothesis 1 and partial support for hypothesis 2.

The interpretations of these findings have pedagogical value. Although our findings

account for a modest amount of variance, they suggest that when students envision

negatively valenced IIs, they do not approach their instructors with their concerns

and instead communicate their disagreements to outside parties in an attempt to vent

their class-related frustrations. This imagined negativity might stem from students’

lack of confidence that even approaching an instructor with a concern will accomplish

anything. Research on why students withhold rhetorical dissent from their instructors

supports this conclusion. Bolkan and Goodboy (2013) discovered that many students

prefer to avoid dissenting directly to an instructor because they do not think that it is

worth the effort or that complaining would fix their problems. Bolkan and Goodboy

also found that when students withhold rhetorical dissent, they typically prefer to

communicate expressive dissent as an alternative response. Therefore, the extant

research on dissent may explain why students who have negatively valenced IIs end

up not communicating their dissatisfaction in person—they may perceive that an

actual conversation with their instructor will unfold in an unproductive manner.

If this is the case, unlike positively valenced IIs, which may indicate that students

are hopeful regarding their interactions with instructors, students who experience

negative IIs may not dissent rhetorically because they essentially ‘‘think themselves

out of it.’’ And, instead of dissenting in ways that might address their perceived

problem, they may act in ways that address their negative feelings by expressing their

discontent to friends, family members, or classmates to garner empathy or support.

On the contrary, after explaining variance from function 1, results suggested that

students who had frequent and positively valenced IIs were likely to communicate

rhetorical dissent directly to the instructor. It may be that students’ anticipated posi-

tive interactions in their thoughts helped encourage direct conversations with their

instructors to remedy their class-related concerns. Though these students may have

viewed their instructor as transgressing in the classroom, they may also believe that

their instructor might rectify their problems and therefore may be more likely to

speak with him=her. This result makes sense considering students expect fair and

satisfying responses from their instructors when they rhetorically dissent (Holmgren

122 A. K. Goodboy et al.

& Bolkan, 2014), and positively valenced IIs might play out mentally with a fair

anticipated response. Other researchers would agree with this conclusion as well.

For example, Mottet, Martin, and Myers (2004) found that students will communi-

cate concerns to an instructor to perform better in the course as long as they view the

instructor as approachable. In summary, the valence of IIs may be a reflection of stu-

dents’ anticipated outcomes regarding conversations with their instructors and, as a

result, may dictate whether or not students complain directly to their instructors or

communicate their displeasure to other parties instead in a less constructive manner.

The collective results of these intrapersonal findings also have practical implications

for instructors. First, because results of this study suggest that low affect instructors

spur imagined interactions with students, and that these interactions co-occur with

dissent, instructors who want to deter students from having negative IIs about them

should focus on fostering affective learning in the classroom. This may be done by uti-

lizing effective instructional behaviors such as immediacy, humor, clarity, and confir-

mation (Kramer & Pier, 1999). Second, although instructors will occasionally enact

behaviors that damage students’ affective learning (Kearney, Plax, Hays, & Ivey,

1991), being pleasant and welcoming to students after making mistakes may help stu-

dents form positively valenced IIs that address their concerns and allow them to forgive

the instructor’s transgression (Vallade et al., 2013). Likewise, Bolkan and Goodboy

(2013) suggest that instructors who openly welcome corrective feedback in the form

of rhetorical dissent may motivate their students to come to them with their classroom

problems. The results of this study suggest that students who have positive IIs will be

more likely to approach their instructors which may result in a constructive solution

for the student and the opportunity for instructors to change teaching practices for

the better (Bolkan & Goodboy, 2013). Third, regardless of the valence or rehearsal,

students who have frequent IIs about their low affect instructor report that they will

engage in vengeful dissent to some degree. Thus, it may be important for instructors

to realize that when students attempt to get their instructors in trouble with their

careers they are not necessarily reacting with little thought. Instead, they have probably

thought this decision out and imagined the consequences=outcomes of vengeful dis-

sent attempts. Though this may not be good news for offending instructors, these

results are informative because they suggest that vengeful dissent may not be not be

an impulsive reaction to student anger and may instead reflect a more deliberate

and planned student reaction toward instructors who fail to foster affective learning.

As with any research the current study had several limitations. One limitation is

that students only reported on low affect instructors by referencing their worst

instructor in the past academic year. This methodological decision was used in order

to maximize reports of IIs and subsequent dissent responses. It is possible that

effective instructors, who are likely to trigger minimal dissent from their teaching

(Goodboy, 2011b; LaBelle et al., 2013), provide students with little desire to have

IIs about their instructors because their academic needs are being fulfilled. Therefore,

future researchers should carefully discern the types of data collection methods used

for collecting instructional dissent data. That is, in this study students (a) reported on

their worst instructors, but in other research, students have (b) reported on their

Communication Reports 123

instructor from the class immediately before data collection to obtain a variability in

instructors (e.g., Goodboy, 2011b; Goodboy & Myers, 2012), (c) have been provided

with definitions of instructional dissent and have referenced an instructor that met

the definition (e.g., Holmgren & Bolkan, 2014; LaBelle et al., 2013), or (d) provided

open-ended responses to dissent-related questioning (e.g., Bolkan & Goodboy, 2013;

Goodboy, 2011a). Instructional dissent researchers should be guided by their research

questions or hypotheses when determining which data collection method best suits

their purposes.

Another limitation was that we did not measure the full range of II characteristics

and functions; yet, these measures may need improvement according to data in this

study.1 A third limitation is that causality cannot be inferred from the data; although

we predicted that IIs predict dissent, it is possible that dissent episodes trigger IIs too.

Future researchers may consider the role that culture plays in IIs and the dissent

process considering that Americans are the most self-dominant in IIs (McCann &

Honeycutt, 2006). Moreover, future research might consider how IIs function in

the small group context with class assignments because individuals have third-party

imagined interactions about others, not just themselves (Porter, 2010–2011). Finally,

much like research on interpersonal conflict, research on instructional dissent should

provide more comprehensive models showcasing distal and proximal factors that

influence dissent messages and outcomes.

In summary, this project confirms that students have IIs about low affect instruc-

tors and that features of those IIs dictate the type of dissent that students’ respond

with. Moreover, it is important for instructors to realize that many students have

imagined conversations with them and that the valence of these self-talks either

encourages or discourages students to communicate with them directly to rectify

their problems. Moreover, these IIs also encourage students to retaliate with instruc-

tors by spreading negative publicity when affective learning is low. For these reasons,

instructors should be careful to avoid triggering agents of dissent in the first place

(Goodboy, 2011a), and they should attempt to maintain students’ affective learning

which acts as a buffer against unproductive dissent (Goodboy, 2011b).

Note

[1] For validity purposes, all measures were subjected to confirmatory factor analyses (CFAs)

using maximum likelihood estimation (ML). Model fit was assessed using the minimum

fit function chi square, CFI, SRMR, and RMSEA (Kline, 2011). The CFA results for each

measure are as follows: (1) Survey of Imagined Interactions: Frequency and Valence sub-

scales (2 factors: x 2¼ 55.85, df¼ 13, p< .01, RMSEA¼ .13, CFI¼ .93, SRMR¼ .11);

Students’ Imagined Interactions with Teachers Scale (3 factors: x 2¼ 1011.97, df¼ 321,

p< .001, RMSEA¼ .12, CFI¼ .93, SRMR¼ .09); Instructional Dissent Scale (3 factors:

(x 2¼ 517.11, df¼ 206, p< .01, RMSEA¼ .09, CFI¼ .95, SRMR¼ .08); Revised Affective

Learning Measure: Construct 7 subscale (1 factor: (x 2¼ 5.92, df¼ 2, p¼ .05, RMSEA¼ .11,

CFI¼ .98, SRMR¼ .04). Considering the fit statistics for the Survey of Imagined Interactions

and the Students’ Imagined Interactions with Teachers Scale, future researchers should

consider how to improve the measurement of IIs.

124 A. K. Goodboy et al.

References

Allen, M. W., Edwards, R., Hayhoe, C. R., & Leach, L. (2007). Imagined interactions, family money

management patterns and coalitions, and attitudes toward money and credit. Journal of

Family and Economic Issues, 28, 3–22. doi:10.1007=s10834-006-9048-1

Allen, T. H. (1990). An investigation of Machiavellianism and imagined interaction. Communi-

cation Research Reports, 7, 116–120. doi:10.1080=08824099009359864

Allen, T. H., & Berkos, K. M. (2005–2006). Ruminating about symbolic conflict through imagined

interactions. Imagination, Cognition and Personality, 25, 307–320.

Allen, T. H., & Honeycutt, J. M. (1997). Planning, imagined interaction, and the nonverbal display

of anxiety. Communication Research, 24, 64–82. doi:10.1177=009365097024001003

Berkos, K. M. (2012–2013). Imagined interactions as a deterrent to students’ antisocial email

requests. Imagination, Cognition and Personality, 32, 23–40. doi:10.2190=IC.32.1.c

Berkos, K. M., Allen, T. H., Kearney, P., & Plax, T. G. (2001). When norms are violated: Imagined

interactions as processing and coping mechanisms. Communication Monographs, 68,

289–300. doi:10.1080=03637750128066

Bodie, G., Honeycutt, J. M., & Vickery, A. J. (2013). An analysis of the correspondence between

imagined interactions attributes and functions. Human Communication Research, 39,

157–183. doi:10.1111=hcre.12003

Bolkan, S., & Goodboy, A. K. (2011). Consumer complaining behavior, imagined interactions,

and communication traits: Cognitive processing following an organizational failure.

Communication Quarterly, 59, 465–483. doi:10.1080=01463373.2011.597273

Bolkan, S., & Goodboy, A. K. (2013). No complain, no gain: Students’ organizational, relational,

and personal reasons for withholding rhetorical dissent from their college instructors.

Communication Education, 62, 278–300. doi:10.1080=03634523.2013.788198

Buckner, M. M., & Finn, A. N. (2013). Academic locus of control as an individual factor influencing

student dissent. Communication Research Reports, 30, 333–341. doi:10.1080=08824096.

2013.837389

Edwards, R., Honeycutt, J. M., & Zagacki, K. S. (1988). Imagined interaction as an element of

social cognition. Western Journal of Speech Communication, 52, 23–45. doi:10.1080= 10570318809389623

Goodboy, A. K. (2011a). Instructional dissent in the college classroom. Communication Education,

60, 296–313. doi:10.1080=03634523.2010.537756

Goodboy, A. K. (2011b). The development and validation of the instructional dissent scale.

Communication Education, 60, 422–430. doi:10.1080=03634523.2011.569894

Goodboy, A. K. (2012). Sex differences in instructional dissent. Psychological Reports, 111, 189–195.

doi:10.2466=11.07.16.PR0.111.4.189-195

Goodboy, A. K., & Bolkan, S. (2013). Instructional dissent as a function of student conflict styles.

Communication Research Reports, 30, 259–263. doi:10.1080=08824096.2012.763027

Goodboy, A. K., & Frisby, B. N. (2014). Instructional dissent as an expression of students’ academic

orientations and beliefs about education. Communication Studies, 65, 96–111. doi:10.1080= 10510974.2013.785013

Goodboy, A. K., & Martin, M. M. (2013, November). Student temperament and motives as predictors

of instructional dissent. Paper presented at the National Communication Convention,

Washington, DC.

Goodboy, A. K., & Myers, S. A. (2012). Instructional dissent as an expression of students’ verbal

aggressiveness and argumentativeness traits. Communication Education, 61, 448–458.

doi:10.1080=03634523.2012.699635

Holmgren, J. L., & Bolkan, S. (2014). Instructor responses to rhetorical dissent: Student perceptions

of justice and classroom outcomes. Communication Education, 63, 17–40. doi:10.1080= 03634523.2013.833644

Communication Reports 125

Horan, S. M., Chory, R. M., & Goodboy, A. K. (2010). Understanding students’ classroom justice

experiences and responses. Communication Education, 59, 453–474. doi:10.1080=03634523.

2010.487282

Honeycutt, J. M. (1998–1999). Differences in imagined interactions as a consequence of marital

ideology and attachment. Imagination, cognition and personality, 18, 269–283.

Honeycutt, J. M. (2003). Imagined interactions: Daydreaming about communication. Cresskill, NJ:

Hampton Press.

Honeycutt, J. M. (2003–2004). Imagined interaction conflict-linkage theory: Explaining the

persistence and resolution of interpersonal conflict in everyday life. Imagination, Cognition

and Personality, 23, 3–26. doi:10.2190=240J-1VPK-K86D-1JL8

Honeycutt, J. M. (2008–2009). Symbolic interdependence, imagined interaction, and relationship

quality. Imagination, cognition and personality, 28, 303–320. doi:10.2190=IC.28.4.b

Honeycutt, J. M. (2010). Imagine that: Studies in imagined interaction. Cresskill, NJ: Hampton

Press.

Honeycutt, J. M., Choi, C. W., & DeBerry, J. R. (2009). Communication apprehension and

imagined interactions. Communication Research Reports, 26, 228–236. doi:10.1080= 08824090903074423

Honeycutt, J. M., Edwards, R., & Zagacki, K. S. (1989–1990). Using imagined interaction features

to predict measures of self-awareness: Loneliness, locus of control, self-dominance, and

emotional intensity. Imagination, Cognition and Personality, 9, 17–31. doi:10.2190= 02L8-1GMP-JV5C-JQ7X

Honeycutt, J. M., & Ford, S. G. (2001). Mental imagery and intrapersonal communication: A

review of research on imagined interactions (IIs) and current developments. In W. B.

Gudykunst (Ed.), Communication yearbook 25 (pp. 315–345). Mahwah, NJ: Lawrence

Erlbaum Associates.

Honeycutt, J. M., & Keaton, S. A. (2012–2013). Imagined interactions and personality preferences

as predictors of relationship quality. Imagination, Cognition and Personality, 32, 3–21.

doi:10.2190=IC.32.1.b

Honeycutt, J. M., & McCann, R. M. (2008). Predicting intrapersonal communication satisfaction

on the basis of imagined interactions in the Pacific Rim. Journal of Intercultural Communi-

cation Research, 37, 25–42. doi:10.1080=17475750802077362

Honeycutt, J. M., Nasser, K. A., Banner, J. M., Mapp, C. M., & DuPont, B. W. (2008). Individual

differences in catharsis, emotional valence, trauma anxiety, and social networks among hur-

ricane Katrina and Rita victims. Southern Communication Journal, 73, 229–242. doi:10.1080= 10417940802219728

Honeycutt, J. M., & Patterson, J. (1997). Affinity strategies in relationships: The role of gender

and imagined interactions in maintaining liking among college roommates. Personal

Relationships, 4, 35–46. doi:10.1111=j.1475-6811.1997.tb00129.x

Honeycutt, J. M., Pence, M. E., & Gearhart, C. C. (2012–2013). Associations between imagined

interactions and the ‘‘Big Five’’ personality traits. Imagination, Cognition and Personality,

32, 273–289. doi:10.2190=IC.32.3.d

Honeycutt, J. M., Pence, M. E., & Gearhart, C. C. (2013). Using imagined interactions to predict

covert narcissism. Communication Reports, 26, 26–38. doi:10.1080=08934215.2013.773051

Honeycutt, J. M., & Wiemann, J. M. (1999). Analysis of functions of talk and reports of imagined

interactions (IIs) during engagement and marriage. Human Communication Research, 25,

399–419. doi:10.1111=j.1468-2958.1999.tb00451.x

Honeycutt, J. M., Zagacki, K. S., & Edwards, R. (1990). Imagined interaction and interpersonal

communication. Communication Reports, 3, 1–8. doi:10.1080=08934219009367494

Kearney, P., Plax, T. G., Hays, E. R., & Ivey, M. J. (1991). College teacher misbehaviors: What

students don’t like about what teachers say and do. Communication Quarterly, 39,

309–324. doi:10.1080=01463379109369808

126 A. K. Goodboy et al.

Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY:

Guilford Press.

Kramer, M. W., & Pier, P. M. (1999). Students’ perceptions of effective and ineffective communi-

cation by college teachers. Southern Communication Journal, 65, 16–33. doi:10.1080= 10417949909373153

LaBelle, S., & Martin, M. M. (2014). Attribution theory in the college classroom: Examining the

relationship of student attributions and instructional dissent. Communication Research

Reports, 31, 110–116. doi:10.1080=08824096.2013.846257

LaBelle, S., Martin, M. M., & Weber, K. (2013). Instructional dissent in the college classroom: Using

the instructional beliefs model as a framework. Communication Education, 62, 169–190.

doi:10.1080=03634523.2012.759243

Martin, M. M., Goodboy, A. K., & Johnson, Z. (2013, November). Workplace bullying: The impact of

faculty members bullying graduate students. Paper presented at the National Communication

Convention, Washington, DC.

McCann, R. M., & Honeycutt, J. M. (2006). A cross-cultural analysis of imagined interactions.

Human Communication Research, 32, 274–301. doi:10.1111=j.1468-2958.2006.00276.x

Mottet, T. P., Martin, M. M., & Myers, S. A. (2004). Relationships among perceived instructor

verbal approach and avoidance relational strategies and students’ motives for commu-

nicating with their instructors. Communication Education, 53, 116–122. doi:10.1080= 0363452032000135814

Mottet, T. P., & Richmond, V. P. (1998). Newer is not necessarily better: A reexamination of affect-

ive learning measurement. Communication Research Reports, 15, 370–378. doi:10.1080/

08824099809362136

Porter, M. A. (2010–2011). Third-party imagined interactions: Expanding mental imagery to the

experience of others’ communication. Imagination, Cognition and Personality, 30, 147–169.

doi:10.2190=IC.30.2.d

Richmond, V. P., McCroskey, J. C., Kearney, P., & Plax, T. G. (1987). Power in the classroom VII:

Linking behavior alteration techniques to cognitive learning. Communication Education, 36,

1–12. doi:10.1080=03634528709378636

Rosenblatt, P. C., & Meyer, C. (1986). Imagined interactions in the family. Family Relations, 35,

319–324. Retrieved from http://www.jstor.org/stable/583641

Sherry, A., & Henson, R. K. (2005). Conducting and interpreting canonical correlation analysis in

personality research: A user-friendly primer. Journal of Personality Assessment, 84, 37–48.

doi:10.1207=s15327752jpa8401_09

Stacks, D. W., & Andersen, P. A. (1989). The modular mind: Implications for intrapersonal

communication. The Southern Communication Journal, 54, 273–293. doi:10.1080= 10417948909372761

Thompson, B. (1984). Canonical correlation analysis: Uses and interpretation. Thousand Oaks, CA:

SAGE Publications.

Turner, R. N., Crisp, R. J., & Lambert, E. (2007). Imagining intergroup contact can improve intergroup

attitudes. Group Processes & Intergroup Relations, 10, 427–441. doi:10.1177/1368430207081533

Vallade, J., Vela, L., & Martin, M. M. (2013, November). An investigation of students’ forgiveness,

instructional dissent, and learning in the college classroom. Paper presented at the National

Communication Convention, Washington, DC.

Van Kelegom, M. J., & Wright, C. N. (2013). The use of imagined interactions to manage relational

uncertainty. Southern Communication Journal, 78, 91–106. doi:10.1080=1041794X.2012.726688.

Wallenfelsz, K. P., & Hample, D. (2010). The role of taking conflict personally in imagined

interactions about conflict. Southern Communication Journal, 75, 471–487. doi:10.1080= 10417940903006057

Zagacki, K. S., Edwards, R., & Honeycutt, J. M. (1992). The role of mental imagery and emotion

in imagined interaction. Communication Quarterly, 40, 56–68. doi:10.1080=01463379209369820

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