face-to-face arguing is an emotional experience: triangulating methodologies and early findings...

ARGUMENTATION AND ADVOCACY

42 (Fall 2005): 74-93

FACE-TO-FACE ARGUING IS AN EMOTIONALEXPERIENCE: TRIANGULATING

METHODOLOGIES AND EARLY FINDINGS

Dale Hample, Marceline Thompson-Hayes, Kelly Wallenfelsz,Paul Wallenfelsz, and Christiana Knapp

Emotional experience is important to people's understandings of interpersonal arguments. However, very little

research illuminates the moment-to-moment emotionality of arguing. This project used emotional data from

dyadic arguers. Arguer, partner, and coder ratings of anger, happiness, sadness, tenderness, and attitude toward

the other were gathered at 30-second intervals, using a cued-recall procedure. These three ratings do not converge

often or well, and appeared to be responsive to different information. Several trait instruments-measuring

personalization of conflict, emotional expressivity, and emotional reappraisal/suppression-were substantially

associated with the average levels of self-reported feelings. Emotions were very consistent throughout the

arguments, both within and between arguers. Own feelings also were highly responsive to the whole range of the

partner's emotions. Key words: interpersonal argument, Taking Conflict Personally, emotional

expressivity, emotional reappraisal, emotional suppression

People react emotionally to all communicative interactions, even if the level of arousal isso low as to be almost unnoticeable. Feelings are a fundamental part of what it is to behuman, and to act in the social domain. Although emotions are precursors of certain sorts ofcommunication, the essential content of particular messages, or the regnant set of memoriesfor some encounters, this study takes yet another tack. We see emotional experience as anobject of study in its own right, regardless of its causal role in larger processes. Thatemotional states have implications for other elements of human interaction is undoubted,and detailed evidence on that point is to be welcomed. However, we believe that feelings canbe so completely absorbing that little further cognition can occur simultaneously, that peoplecan feel personally inadequate either because they do not have an expected emotion orbecause they have a flash of a reprehensible one, and that both internal and displayed affectcan be central to definitions of self, other, and relationship. Research exploring emotionalexperiences in communication is necessary, not only for its intrinsic interest, but also becausesuch work will estabhsh the foundations for inquiries into the place of emotion in traditionaltopics such Eis message production, content, and reception.

We focus on the emotional experience of interpersonal arguing. Dillard (2004, p. 199)reports that no focused work on emotions and arguing currently exists. Only a few studieseven begin to suggest a qualification to his claim (see Hample, 2005b, ch. 5). A noticeablebody of work indicates that people often have negative expectations, preconceptions, andreactions to arguing (Benoit, 1982; Benoit & Hample, 1998; Dailinger & Hample, 2002;Hample & Benoit, 1999; Hample, Benoit, Houston, Purifoy, VanHyfte, & Wardwell, 1999;Martin & Scheerhorn, 1985; Trapp, 1986). Naive actors often associate arguing with hostility.

Dale Hample, Marceline Thompson-Hayes, Kelly Wallenfelsz, Paul Wallenfelsz. and Christiana Knapp, Department of Com-munication, Westem Illinois University. Marceline Thompson-Hayes is now at Department of Communication Studies,Arkansas State University. An earlier version of this paper was presented at the annual meeting of the NationalCommunication Association, Chicago, Illinois, November 2004. Correspondence concerning this article should beaddressed to Dale Hample, Department of Communication, Westem Illinois University, Macomb, Illinois 61455.E-mail: [email protected]

75

ARGUMENTATION AND ADVOCACY HAMPLE ET AL.

uncontrolled negative emotionality, stubbornness, frustration, and a host of related consid-erations. However, this research is restricted to retrospective or impressionistic data, anddoes not offer evidence concerning the direct, immediate emotional experience of arguing.The argumentativeness (Infante & Rancer, 1982), argument framing (Hample, 2005a), andTaking Conflict Personally scales (Hample & Dailinger, 1995) all contain items askingrespondents to indicate whether they find arguments and conflicts to be enjoyable. However,these instruments normally are administered either without reference to a particular encoun-ter or after one's end. None has been applied, as far as we know, to the concurrentlyexperienced feelings of a participant in an ongoing interaction. A recent exception toDillard's statement (although he could not have known about it) indicates that observersreliably can code displays of affect during arguments, and that the resulting data revealpatterns of emotional experience (Hample, 2004). That investigation, however, examinedonly two dyadic arguments, which is too small a sample to support any firm conclusions.

Because we are traversing relatively unexplored ground, methodology is a fundamentalconcern. Consequently, we tested three sorts of operationalization in this study: ownself-reports of emotions, one's argument partner's estimates of one's emotions, and observ-ers' ratings of affect displays. Naturally, we wanted to determine whether these methodswould give similar results and whether there might be a reason to prefer one of them infurther work. Our second impulse was to provide an initial, basic description of theemotional experience of arguing. We sought to determine what feelings are most salient andwhether they connect with one another. We looked for associations within each person'sown emotional field and for the possibility of relationships between interactants' feelings. Wealso explored several personality traits that may well be relevant to the display or suppres-sion of one's affective reactions to arguing. Before elaborating our specific aims, however, weexamine what is known about emotions in communication generally.

SOCIAL INFLUENCES ON THE EXPERIENCE AND EXPRESSION OFARGUERS' EMOTIONS

Research on emotional expression has contrasted the roles of biological and social factors(Buck, 2003). From a biological standpoint, emotional displays can be viewed as a reflectionof underlying emotional states. From a social standpoint-our main emphasis here-emo-tional displays can be viewed as processes that regulate social interaction. Four key ideaselaborate the social aspects of emotional experience: display rules, emotion work, reap-praisal, and emotional contagion.

Display rules may require people to minimize, exaggerate, or alter their expressions insocial situations in order to manifest socially appropriate emotions (Burgoon & Bacue, 2003,pp. 189-190; Ekman & Friesen, 1975). Arguers, of course, have such display rules, whichserve as both expressive and interpretive resources. Each culture has its own rules (Porter &Samovar, 1998), and particular relationships, such as marriages, also may develop their own.Display rules not only influence the expected emotional behavior in a given situation butalso provide information for the decoding of emotional displays (Hess & Kirouac, 2000). Forinstance, men and women who express fear usually appear to be less dominant than thosewho express anger, but the effect is particularly pronounced for women due to a greatersocial proscription against their expression of anger (Hess, Blairy, & Kleck, 2000).

People are sensitive to display rules and, therefore, may alter their emotional expressionsto some degree (Burleson & Planalp, 2000). Gross and John (2003) indicate that, although

76

ARGUING AND EMOTIONS FALL 2005

sometimes emotions are automatically displayed, at other times careful thought goes into theexpression of an emotion. We use, shape, and construct our displays to fit both the situationand the goals at hand (Gross & John, 2003). Arguers feeling some of the negative reactionsmentioned above may choose to express them or not. Other arguers who lack such feelingsmight display them purposefully.

Whereas display rules are a useful resource in understanding what arguers look and soundlike, emotion work is a parallel construct for studying arguers' internal states. According toHochschild (1979), emotion work is an individual's attempt to generate appropriate feelingsfor specific social situations. Unlike display rules, which concern outward displays ofemotion, emotion work is directed internally. It is a response to feeling rules, which stipulatewhat is expected and appropriate to feel in a given situation (Hochschild, 1979). Forexample, if one person feels pleasure at another's failure to answer an argumentativechallenge, he or she also may regard this emotion as inappropriate and engage in emotionwork to feel sad instead.

Similar to emotion work, reappraisal is a form of cognitive change in which peopledecrease negative emotions by altering their thoughts (Gross &John, 2003). Reappraisal mayinclude taking a more optimistic attitude, attempting to repair bad moods, and reframingstressful situations. Gross and John (2003) report that, compared to those who merelysuppress their negative emotions, people who engage in reappraisal (thus altering theseemotions) also experience and express more positive and less negative emotion, resulting incloser friendships and greater well-being. The common appearance of negative feelingsabout arguing in the retrospective studies mentioned earlier suggests that reappraisal oughtto be an early point of inquiry here.

Emotions also are shaped and regulated through contagion, which occurs when oneperson experiences a response parallel to another's emotional display (Coke, Batson, &McDavis, 1978; Davis, 1983; Deutsch & Madle, 1975; Feshbach, 1975; Stotland, 1969).Because arguing is an emotional experience, we are interested in knowing the degree towhich these feelings spread from one arguer to another. Totterdell (2000), for instance, foundthat individual moods of professional sports players are associated with the collective moodsof teammates. Something similar might well be true of dyadic arguers.

In combination, display rules, emotion work, reappraisal, and emotional contagion revealhow the experience and expression of emotion is regulated in part by social processes. Mettsand Planalp (2002, p. 362) note, however, that more studies of moment-to-moment emo-tionality during interactions are needed. The present study examined such phenomenaduring interpersonal arguments.

METHODOLOGY FOR STUDY OF ARGUERS' EMOTIONS

Several scholars call for more study of emotions in ordinary encounters, with attention tothe appropriateness of various methodologies (Dillard & Wilson, 1993; Fiehler, 2002). Mostresearchers employ subjective or experiential measures to operationalize emotion (Dillard &Wilson, 1993), but external coding sometimes also has been included, with useful results(e.g., Bohner & Schwarz, 1993; Senecal, Murard, & Hess, 2003).

In our design, arguers self-reported their feelings, conversational partners rated those sameemotions, and external coders rated them via a videotape of the conversation. Althoughassessing the same thing (i.e., person A's feelings at a given moment), these three sorts ofpeople had access to different information (Folger, Hewes, & Poole, 1984). In a relationship.

ARGUMENTATION AND ADVOCACY

TABLE 1.CORRELATIONS AMONG RATING TYPES FOR ANGER

77

HAMPLE ET AL.

Time Segment

123456789

101112131415

N

83-8483-8479-8473-7668-7261-6655-6148-5338-4628-3222-2619-2118-2015-1812-14

Self-Other

.54***

.38***

.51***

.53***

.40***

.45***

.56***

.57***

.48***-.03

.07

.00

.13-.31

.12

Self-Coder

.18-.12-.02-.15-.11

.06-.14

.02

.02

.10-.13

.01-.33

.10-.28

Other-Coder

.08-.08

.10

.00-.01-.03-.19-.14-.09- .06- .00- .25- .32- .40- .36

***/> < .001.

person A has privileged access to own feelings, of course, and might be presumed to be mostaccurate (although the possibilities of self-deception and general lack of self-awarenessshould not be ignored). The conversational partner lacks such access, but does possessinformation about the flow and climate of the argumentative interaction. Presumably,participation is more involving and informative than viewing a videotape. Further, most ofour participants already were acquainted; their relational history helped in drawing emo-tional inferences (see Burgoon & Bacue, 2003). Finally, coders had a reasonably full recordof the verbal and nonverbal behaviors enacted, but no genuinely personal connection to theargument. Would these different informational stances generate different codings, or wouldthe common coding target result in highly similar ratings?

Several studies cast light on the extent to which emotional reports from self, partner, andcoder may be expected to agree. Most are studies of marital or relational communication,and none directly concerns face-to-face arguing. As we will see, the levels of congruenceamong their various measures are quite uneven. Generally, these studies suggest, one canexpect statistically significant levels of association among the various coders, but the levelsof congruence rarely are high enough to encourage one to believe that everyone is reactingin the same way to the same information.

A common task is to ask people to rate whether behaviors are positive, negative, oraffectively neutral. One data set has been analyzed repeatedly (Noller, 1980, 1981, 1982,1985). Noller videotaped 48 married couples as they intentionally tried to convey the threesorts of affect, and asked people to classify their spouse's messages. Overall, spousal ratingswere about 68% accurate (Noller, 1980, our calculation from Table 3). Spouses also codedvideotapes of strangers and showed about 68% agreement with one another (Noller, 1981,our calculation from Table 3). In addition, external coders' ratings showed about 80%agreement (Noller, 1982). Noller and Feeney (1994) had spouses undertake a similar task atseveral points in a two-year period, and showed 74% mutual accuracy (our calculation fromTable 1). Margolin, Hattem, and John (1985), who employed a different videotape stimulusdesign, report noticeably lower rates of agreement, varying from 25% to 50%. Overall, thestudies reported rates of agreement ranging from 25% to 80%. However, the task of

78


TABLE 2.CORRELATIONS AMONG RATING TYPES FOR HAPPINESS

Time Segment

123456789

101112131415

N

83-8483-8479-8473-7667-7261-6655-6148-5338-4628-3222-2619-2018-2015-1812-14

Self-Other

-.23*-.08-.06-.17-.12

.04

.07-.07

.03-.10

.13

.12-.38-.25

.14

Self-Coder

.32**

.19

.28*-.04-.02

.33**

.35**

.50***

.28-.02

.16

.18

.23

.28

.36

Other-Coder

.05

.16

.23*

.30**

.37**

.23

.20

.16

.37*

.05-.18-.09-.04-.13

.00

*p < .05; **p < .01; ***p < .001.

classifying behavior as positive, negative, or neutral is simpler than the one used in thepresent study.

Another common approach has been to ask relational partners simply to report own andother's behaviors, usually for a one-day period (Christensen, Sullaway, & King, 1983; Gable,Reis, & Downey, 2003; Jacobson & Moore, 1981). Some of these behaviors are more or lessobjective (e.g., "we went for a walk"), while others require something like an affectiveinference (e.g., "I tried to make my partner feel wanted"). Consistently, partners agreed moreabout the former and less about the latter. Accuracy rates range from 48% (Jacobson &Moore, 1981) to 73%-89% (Gable et al., 2003). Our design required only inferentialjudgments, but our method of stimulated recall avoided memory deficits.

Two studies have examined the accuracy of highly trained observers in Japan. Uehara etal. (1999) reported that, from five-minute speech samples, four trained coders could assessexpressed emotion (a clinical measure) of patients reliably, with an overall interclass corre-

TABLE 3.CORRELATIONS AMONG RATING TYPES FOR SADNESS

Time Segment

123456789

101112131415

N

8483-8478-8372-75

7261-6655-6048-5237-4528-32 •22-2619-2018-2015-1812-14

Self-Other

.23*

.32**

.28**

.36**

.17

.06

.29*

.29*

.54***

.37*

.34

.25-.02

.14

.11

Self-Coder

.09

.22-.02

_.07

-.15.09.39*.38*.15.41

-.09.16.16

Other-Coder

_.09.01

-.02

.06

.09

.12-.10

.14

.15

.16

.31-.18-.26

Note. The blank cells appear because one of the variables, the coder ratings, was in fact constant.*p < .05; **p < .01; ***p < .001.

79


lation of .91, although that estimate varied from .48 to 1.00 for different measures. Ota,Sasaki, and Yamauchi (2004) compared family members', residents', and psychiatrists'ratings of patients' moods. The correlations between those ratings and the patients' self-reports varied from r = .33 to ,57, The raters' correlations v dth one another varied from .28to .52. The family members' ratings were most similar to the patients' self-reports. Workingin a nonclinical context, Senecal et al. (2003) also determined that partners have moreempathic accuracy than observers. Ota et al.'s results again suggest that we should expectsignificant but modest cissociations among our measures of moment-to-moment affect duringarguments.

Other data relating to coder convergence come from Sabatelli, Buck, and Dreyer (1982),who asked participants to view diverse photographic slides (landscapes, sexual content, etc.),which might be expected to stimulate varied affective states. Researchers videotaped par-ticipants as they viewed the slides. From observation of the videotapes, their spouses werecisked to indicate the appropriate category of slide. Husbands were 43% accurate and waves51%, which was somewhat better than an external panel with access to the same videotapes.The spouses were 54% accurate in clcissifying strangers' slides (our calculation), Reanalysis ofthe same data indicated that most of the variance in accuracy was attributable to the uniquerelationship of the spouses, rather than to sender or decoder accuracy (Sabatelli, Buck, &Kenny, 1986).

The research reviewed to this point has emphasized nonverbal communication, eventhough verbal information also was available to raters in some studies, e.g., Noller's program,and was the case in our design. Greasley, Sherrard, and Waterman (2000) assessed people'sability to judge affect conveyed solely by verbal material. Respondents were given naturalsamples of emotional speech and asked to identify the emotion (anger, disgust, fear, sadness,or happiness) being communicated. With two-thirds agreement as a criterion, nearly half thesamples fell short. Our study employed a similar list of discrete emotions.

In sum, prior research has showTi consistently that people's ratings of the same emotionalstimuli are moderately associated. Few studies afford much confidence that the associationsare sufficiently high to make the ratings psychometrically interchangeable. None of thosereviewed, however, was concerned with interpersonal arguing. We seek to begin such acommunity project by studying our own area of scholarly interest. Therefore, we inquire,

RQ^l: Do self, other, and observer codings of emotional displays during face-to-facearguments reliably converge?

PREDICTORS OF THE EXPERIENCE AND EXPRESSION OF EMOTIONS

WHILE ARGUING

Our other questions directly concern the emotionality of arguing. The relevance of severaltraits to arguing or emotionality has been explored. Taking Conflict Personally (TCP;Hample & Dailinger, 1995; see Hample, 1999, for a review) is a set of predispositions andexpectations about interpersonal conflicts: whether they are seen as punishing or pleasant,and whether they have positive or negative relational consequences. The TCP instrumentshould help us to project the experience of, and therefore the potential to display, feelingsacross the range of valence. The Berkeley Expressivity Questionnaire (BEQ; Gross &John,1997) accesses people's reports of their willingness to express themselves positively andnegatively and provides a measure of the strength of people's emotional impulses. Finally,the Emotional Regulation Questionnaire (ERQ; Gross, &John, 2003) supplies measures of

80


people's tendencies to suppress their emotions or cognitively reappraise unwanted feelings.Together, these three instruments provide a wide-ranging description of people's emotionalpredispositions toward arguing, as well as their normal ways of dealing with emotions. Thus,our second research question:

RQ2: Do trait measures (TCP, BEQ, and ERQ) predict emotional states and displaysduring face-to-face arguments?

Our final question concerns the dynamics of the interpersonal emotional field during anargument. Evidence of emotional contagion would be important in explaining how argu-ments escalate out of control or orient irresistibly toward agreement. We restricted ourempirical descriptions of the emotional field to just a few feelings, but we still wanted to knowhow one's feelings relate to one another during an argument and how they relate to theother's displays. Thus, our final question:

RQ3: What, if any, connections are there among one's own feelings, and between ownfeelings and partner's affect displays, during face-to-face arguments?

METHOD

Participants

Participants were 84 undergraduates (42 dyads) enrolled in upper level communicationclasses at a medium sized state university in the Midwest. They received extra credit for theirinvolvement. They were instructed to bring another person to complete the dyad. Thesample included 38 males and 46 females. Ages ranged from 18 to 26 years, with a mean of21 {s.d. = 1.4). The study was approved by the Institutional Review Board.

Measures

Participants completed three self-report measures: the Berkeley Expressivity Question-naire (BEQ), the Emotional Regulation Questionnaire (ERQ), and the Taking ConflictPersonally (TCP) scale. For the BEQ and ERQ, participants received the following instruc-tions: "We would like to ask you some questions about your emotional life. The questionsbelow involve two distinct aspects of your emotional life. One is your emotional experience,or what you feel inside. The other is your emotional expression, or how you show youremotions in the way you talk, gesture, or behave. For each item, please answer on theScantron sheet using the following scale," which ranged from 1 (strongly disagree) to 7(strongly agree).

The BEQ is a 16-item self-report measure of individual differences in emotional expres-sivity (Gross &John, 1997). Three subscales measure negative expressivity, positive expres-sivity, and impulse strength. For our sample, Cronbach's alphas were .70, .72, and .81,respectively, for the three subscales.

The ERQ assesses individual differences in the habitual use of two emotion regulationstrategies: cognitive reappraisal and expressive suppression (Gross & John, 2003). For thisstudy, Cronbach's alphas were .79 and .75, respectively, for the two subscales.

The TCP scale taps emotional reactions to participating in a conflict (Hample & Dailinger,1995). Its six subscales measure direct personalization, persecution feelings, stress reaction,positive relational effects, negative relational effects, and like/dislike valence. In this sample,Cronbach's alphas were .76, .78, .75, .84, .67, and .78, respectively, for the six subscales.

81


Argumentative Task

Participants in each dyad first completed a series of items concerning their positions on aseries of contemporary, controversial issues. They indicated their position on each issue ona scale ranging from 1 (strongly oppose) to 7 (strongly support). The topic that ehcited thegreatest range of disagreement in the dyad was chosen for discussion. If the participantsagreed on all the issues, additional topics were given until one ehciting disagreementemerged. The experimenters gave the following instructions: "Please attempt to come to anagreement on this topic. You have ten minutes to discuss the topic. If you reach agreementon the topic before the ten minutes has passed, please inform the experimenters who arewaiting in the hall."

Cued-Recall Procedure

Although there are variations of the stimulated recall procedure, we chose one describedby Waldron and Cegala (1992). The participants were videotaped (with their knowledge)while engaged in conversation. Two cameras were used. Each was behind a participant andslightly to the side, capturing an over-the-shoulder view of the other person's face and torso.Immediately following the conversation, participants were separated, watched the videotape,and participated in a cued-recall procedure designed to assess conversational cognitions(Waldron, 1990; Waldron & Cegala, 1992).

Each partner watched the tape containing all of the audio of the conversation and thevideo of the other from the original point of view, i.e., they viewed the tape from the camerabehind them. Prior to watching the tape, participants were instructed: "At each 30-secondinterval, the experimenter will stop the video playback so you can recall what your feelingswere. The experimenter will ask you to answer 10 questions, 5 about you and 5 relating tothe other person." The experimenter then started the tape and subsequently stopped it after30 seconds. The participants then responded to the 10 items. The first 4 were from theexpression of emotion battery (Balswick, 1988), which indexes an individual's expression ofhate, love, sadness, and joy. The anger scale options were: "I am expressing no anger at all/atiny bit of anger/some anger/a lot of anger." Three other scales, relating to tenderness,sadness, and happiness, were analogous. The fifth scale concerned the participants' attitudetoward their conversational partners: "My attitude toward the other person is very negative/somewhat negative/somewhat positive/very positive." Slight rewording produced the fiveitems about the conversational partner. Participants recalled the entire conversation in thismanner at 30-second intervals. After completion, the participants were debriefed andexcused.

Experimenter Ratings

Three graduate students were trained to code the arguments, using the same scales as theparticipants. The coders viewed 12% of the videotapes, using the same 30-second intervalsas did the participants. A separate Cronbach's alpha was computed for each pair of codersfor each of the emotions rated: .78 (anger), .94 (tenderness), .96 (sadness), .87 (happiness),and .78 (attitude toward other). After reaching acceptable intercoder reliability, all threesubsequently rated the remaining videotapes. The ratings of the coder pair with the highestCronbach's alpha were averaged to achieve an overall rating for each segment coded.

82


TABLE 4.CORRELATIONS AMONG RATING TYPES FOR TENDERNESS

Time Segment

123456789

101112131415

N

83-8483-8479-8473-7667-7261-6655-6048-5238-4628-3222-2619-2018-2015-1812-14

Self-Other

.29**

.19

.37***

.46***

.29*

.38**

.16

.40**

.38**

.30-.09-.09

.27-.32-.07

Self-Coder

-.22*-.19-.10

.00-.08

.05

.04-.04

.06

.38*

.08

.20

.14-.33

.75**

Other-Coder

-.22*-.16-.02-.08-.19-.11

.14

.01

.24-.05

.16-.07-.06

.23

.00

*p < .05; **p < .01; ***p < .001.

RESULTS

Convergence of the Measures

The first matters considered here are the reliability and convergence of the three opera-tionalizations (self-report, partner-report, and coder-report) of emotional experience. Assess-ing reliability is normally a straightforward process, but our use of single-item measures, aswell as the inconvenience of certain of our results, made this a complicated matter.

Tables 1-5 present the correlations among the three methods for anger, happiness,sadness, tenderness, and attitude toward other. It is immediately obvious that these associ-ations did not rise to the level required to sustain the conclusion that the scales were reliable,although they did resemble the results reported in our review of previous research. None-theless, we concluded that our instrumentation was reliable for several reasons.

TABLE 5.CORRELATIONS AMONG RATING TYPES FOR ATTITUDE TOWARD THE OTHER

Time Segment

123456789

101112131415

N

83-8483-8479-8473-7668-7261-6655-6048-5238-4628-3222-2619-2018-2015-1812-14

Self-Other

.40***

.58***

.44***

.42***

.41***

.47***

.51***

.47***

.46***

.41*

.44*

.55*

.50*

.49*

.48

Self-Coder

.23*

.07

.13

.11

.25*

.19

.23

.25

.28

.22

.19

.04

.13

.08

.13

Other-Coder

.05

.15

.09

.03

.31**

.39**

.14

.15

.36*

.20

.26

.09

.26

.45

.75**

*p < .05; **p < .01; ***p < .001.

.87

.95

.92

.91

.96

.90

.95

.92

.91

.95

.90

.86

.85

.82

.88

.78

.87

.96

.94

.78

83


TABLE 6.

CRONBACH'S ALPHAS FOR THE SAME ITEM FOR THE FIRST 10 TIME SEGMENTS, AND ACTUAL CRONBACH'SALPHA RELIABILITY FOR CODERS.

actual coderself other coder reliability

AngerHappinessSadnessTendernessAttitude to Other

Note. N for the first three columns is 32. The last column, included for comparison, is a standard Cronbach's alphacovering all the time segments for 12% of the sample.

First, the three methods actually measure different things. When making a self-report ofemotion, a respondent has privileged access to his or her own feelings, which supposedly arereflected in the ratings. Both partner and coder, on the other hand, were rating emotionaldisplays rather than felt emotions. As mentioned previously, because people can suppress ordisguise their feelings, displays can be misleading. This would yield inconsistent or even nullcorrelations with the self-reports. Even partners and coders possessed different information.With the exception of two or three respondents who came alone and had to be paired withsome other student who happened to be wandering the hallways, all the participants knewone other and probably were friends (the students were recruited from classes, and instructedto bring someone not in their class to the study session). Nearly all partners, therefore, couldhave drawn on a relational history and long term acquaintance with the other's feelings whenrating. Coders, on the other hand, did not know any of the participants personally andreacted only to what they saw on videotape. That the three methods operationalize differentthings (own feelings, friend's display, stranger's display) does not mean, of course, that themeasures were reliable. However, it does caution against a simple prima facie reading ofTables 1-5.

One constructive reason to believe that the scales were reliable is that they are simple.When a researcher finds unreliable scales, he or she usually can see, in retrospect, oppor-tunity for misunderstanding or differing interpretations. But rating "anger" or "happiness,"for example, on straightforward scales ("none, a tiny bit, some, a lot") is not a task that shouldprove difficult for average college students.

Second, the same scales in fact have proven to be rehable. In this study, Cronbach's alphafor three coders (graduate students) ranged from .78 to .96. Hample (2004) used undergrad-uates from the same general population as coders and reported Cronbach's alphas rangingfrom .67 to .94. If the scales themselves were poor, no one could use them reliably. Even ifthe scales were flawed in some way that produced systematic errors (e.g., always making"nice" ratings, or only using the extreme codes), these errors actually would make reliabilityestimates more positive, not less (Magnusson, 1966). Hence, the items themselves seemreasonable.

Perhaps the problem was that people were using perfectly fine scales in error-prone ways.Table 6, however, offers evidence that participants in this study used the scales consistently.The Cronbach's alphas were calculated on the statistical assumption that the participants hadthe same levels of anger, happiness, and so forth, throughout the first ten time segments (afterthe tenth, sample sizes declined noticeably). This was only a statistical assumption, not a

84


theoretically expected result, but notice that it was very conservative. Any jumpy affectivechanges throughout an argument would reduce Cronbach's alpha. Nonetheless, Table 6shows highly consistent ratings for each method. Scale reliability in the face of fairly stableaffect is the best, and only plausible, explanation for these results. In short, we believe thatthere is convincing evidence that the scales are reliable.

Let us return to Tables 1-5 and treat the data as substantive results bearing on conver-gence of affect ratings and seen from three different points of view. Several tables show aninteresting pattern: Self- and other reports were consistently correlated, but nothing else was.This pattern was clearest for anger (Table 1) and attitude toward the other (Table 5), but alsoperceptible for sadness (Table 3) and tenderness (Table 4). Happiness, in contrast, showedno convergence between private feelings and partner's readings (Table 2). Notice how rarelycoder reports correlated with self- or even partner reports in any of the tables. This requirescareful interpretation of Hample's (2004) results, which entailed coders' data only.

Tables 1-5 also show reasonable consistency in the correlations for the first 10 timesegments (after which sample sizes decline, making sampling error a more forceful elementin the correlations). We saw no evidence that the display, negotiation, or reading of feelingsdeveloped during the arguments. Any motivation and ability to display and decode thatexisted was present from the outset and did not evolve during these interactions. This maybe attributable to the prior acquaintance of most of the respondents or the artificiality of theargument context.

Traits and Emotions

The next results focus on the connections between the trait measures and emotions. Thetrait measures consisted of the subscales of the TCP instrument (direct personalization, stressreaction, persecution feelings, positive relationjil consequences, negative relational conse-quences, and conflict valence; Hample & Dailinger, 1995), the BEQ (positive expressivity,negative expressivity, and impulse strength; Gross &John, 1997), and the ERQ^ (cognitivereappraisal and expressive suppression; Gross &John, 2003). Table 7 shows the correlationsamong these instruments and the mean self-report measures of affect.

Perhaps more interesting is Table 8, which connects these trait measures to arguers'emotions. Self-reports of own feelings were averaged across however many time segmentsthe argument lasted. Table 8 reveals substantial relationships between traits and how arguersfelt while interacting. The first root involved nearly all the variables. Tenderness and attitudetoward the other were highest (and anger, happiness, and sadness lowest) for respondentswho were willing to express their positive or negative feelings, disinclined to suppressemotions, willing to undertake cognitive reappraisal when necessary, and have negativevalence for conflict. This suggests people open about interaction and emotional sharing, animpression weakly supported by the difference between their optimistic and pessimisticprojections for the relational consequences of arguing. These participants had some reser-vations, however, as indicated by their dislike of conflict and their tendency to personalizeit. Nonetheless, their essential expressivity was predictive of positive tenderness and attitudetoward the other, as well as low anger and sadness. Happiness does not quite fit this pattern.The importance of anger in arguing is well known (e.g., Canary, Spitzberg, & Semic, 1998),but happiness also would seem to merit special attention.

Roots 2 and 3 also were substantial but interpretation must be tentative because so muchvariation was extracted by the first root (Stevens, 2002, ch. 12). Root 2 suggested that one's

85


TABLE 7.

CORRELATIONS FOR MEAN SELF-REPORTED FEEUNGS AND TRAIT MEASURES

HappinessSadnessTendernessAttitudePersonalztnStressPersecutionPos RelatnsNeg RelatnsValencePos ExpressNeg

ExpressImpulseReappraiseSuppress

A H S

.31

.56 .54- .26 - .24 - .42- .53 - .36 - .69- .28 - .16 - .18

.08 - .00 .11

.29 .07 .18- .23 - .06 - .10

.14 .12 .26

.44 .23 .3102 - 38 - 25

- .50 - . 43 - . 6314 04 01

- .41 - .27 - . 43.55 .41 .59

T At

.60

.34 .31

.06 - .01- .03 - .28

.03 .10- .17 - .09- .39 - .64

34 41

.58 .8329 19.29 .40

- .50 - .71

DP Str Per

.12

.21 .24- .04 - .14 - .26

.10 .27 .12- .30 - .21 .24

31 03 .07

.41 .05 - .2919 18 - 02

.31 - .04 - .09- .23 - .14 .36

PosR

- .44- .10

.09

.0706

- .0601

NegR

.02- .18

- .17- 07- .05

.07

Val

- .36

- .64- 18- .27

64

PosX NegX Imp Reap

.48

.46 .42

.33 .47 .11- .25 - .78 - .26 - .39

NoU. N= Si. r= .22 is significant at p < .05, two-tailed. The first five variables (A, H, S, T, and At) are the meanself-reports of feelings. The next six are from the TCP instrument, followed by three from the BEQ, and two fromthe ERQ.

mean level of anger was predicted by one's willingness to express positive emotions (noticehow completely the first root absorbs negative expressivity) and one's general impulsestrength. Root 3 indicated that happiness and sadness are least hkely to be felt by people whohave positive valence for conflict. Here again, the happiness results are odd, but the inverserelation between valence and sadness makes sense.

TABLE 8.

CANONICAL CORRELATIONS BETWEEN TRATT MEASURES AND AVERAGE SELF-REPORTS OF EACH EMOTION

AngerHappinessSadnessTendernessAttitude To Other

variance

Direct PersonaltnStressPersecutionPos RelationalNeg RelationalValenceNeg ExpressPos ExpressImpulseReappraiseSuppress

varianceRe

Root 1

- .71- .54- .79

.61

.9553.7%

.38- .03- .32

.16- .16- .70

.93

.42

.14

.51- .84

20.8%.90***

Root 2

.67- .18

.18

.16

.1811.3%

-.04.20.15

- .27.21

- .12.19.67.51

- .23.04

3.8%.64***

Root 3

.16- .45- .55

.10- .1211.2%

-.18- .15

.28- .11- .43

.55- .02

.19- .11

.15- .001.9%.54*

R 2

.59***

.34***

.61***

.39***

.76***

.17**

.03

.14*

.06

.10

.49***

.72***

.35***

.17**

.24***

.58***

Note. The variance rows refer to the variance in the just-above group of variables that is explained by the canonicalvariate. The R^ column refers to the squared multiple correlation predicting that variable from the other set ofvariables. *p < .05; **p < .01; ***p < .001.

86


TABLE 9.

CORRELATIONS BETWEEN OWN SELF-REPORTED ANGER AND OWN ESTIMATE OF OTHER'S ANGER IN THESAME TIME SEGMENT, AND OWN AND OWN ESTIMATE OF OTHER'S ANGER AT THE PREVIOUS TIME SEGMENT

Own Self-Reported Anger

segment:

own, n-1other's, n-1other's, n

n

segment:


n

1

.71***846

.63***

.51***

.77***66

2

.66***

.61***

.56***847

.64***

.65***

.62***61

3

.63***

.56***

.69***848

.77***

.61***

.77***53

4

.63***

.63***

.65***769

.79***

.77***

.72***46

5

.68***

.67***

.65***7210

.52**

.33

.52**32

segment: 11 12 13 14 15

own, n-1other's, n-1other's, nn

.70***

.33

.73***26

.70***

.69***

.79***21

.55*

.69***

.77***20

.38

.15

.68**18

.09

.40

.67**14

Note. In the left hand column, n refers to the same time segment, and n-1 refers to the previous one. n refers to thenumber of respondents contibuting to the correlations for that time segment. *p < .05; **p < .01; ***p < .001.

Interrelations Among Emotions

Finally, we were interested in the interrelationships among emotions. Two sets of results,concerning within-emotion and between-emotion associations, are relevant.

Within emotions. Tables 9-13 contain within-emotion information. For each emotion, weassociated one's own self-report at time N with three other measures: own self-reported

TABLE 10.

CORRELATIONS BETWEEN OWN SELF-REPORTED HAPPINESS AND OWN ESTIMATE OF OTHER'S HAPPINESSIN THE SAME TIME SEGMENT, AND OWN AND OWN ESTIMATE OF OTHER'S HAPPINESS AT THE

PREVIOUS TIME

Own Self-Reported Happiness

segment:


nsegment:


nsegment:

1

.74***846

.82***

.79***

.89***6611

2

.75***

.69***

.78***847

.82***

.73***

.81***6112

3

.70***

.70***

.76***848

.69***

.65***

.86***5313

4

.65***

.66***

.87***769

.84***

.77***

.87***4614

5

.72***

.77***

.86***7210

.63***

.50**

.82***3215


.84***

.82***

.79***26

.45*

.70***

.73***21

.63**

.55*

.55*20

.75***

.43

.85***18

.74**

.78***

.81***14

Note. In the left hand column, n refers to the same time segment, and n-1 refers to the previous one. n refers to thenumber of respondents contibuting to the correlations for that time segment. *p < .05; **p < .01; ***p < .001.

87


TABLE 11.

CORRELATIONS BETWEEN OWN SELF-REPORTED SADNESS AND OWN ESTIMATE OF OTHER'S SADNESSIN THE SAME TIME SEGMENT, AND OWN AND OWN ESTIMATE OF OTHER'S SADNESS AT THE

PREVIOUS TIME SEGMENT

Own Self-Reported Sadness

segment:


segment:


1

__

.66***846

.84***

.78***

.85***66

2

.76***

.67***

.70***847

.82***

.72***

.82***61

3

.78***

.72***

.87***848

.70***

.73***

.92***53

4

.69***

.67***

.77***769

.88***

.83***

.93***46

5

.77***

.84***

.89***7210

.62***

.54***

.88***32

segment: 11 12 13 14 15


.61***

.64***

.82***26

.56**

.62**

.80***21

.66***

.79***

.90***20

.72***

.75***

.75***18

.38

.73**

.83***14

Note. In the left hand column, n refers to the same time segment, and n-1 refers to the previous one. n refers to thenumber of respondents contibuting to the correlations for that time segment. *p < .05; **p < .01; ***/) < .001.

emotion at time N-1, own estimate of the degree to which the other arguer was feeling thatsame emotion at time N-1, and own estimate of the degree to which the other arguer wasfeeling that same emotion at time N.

We sought evidence regarding the possibilities that one's own emotions are stable fromone moment to the next, that one's feelings mirror the other arguer's, and that one's emotions

TABLE 12.

CORRELATIONS BETWEEN OWN SELF-REPORTED TENDERNESS AND OWN ESTIMATE OF OTHER'S TENDERNESSIN THE SAME TIME SEGMENT, AND OWN AND OWN ESTIMATE OF OTHER'S TENDERNESS AT THE


Own Self-Reported Tenderness

segment:


nsegment:


n

1

.57***846

.63***

.58***

.77***66

2

.61***

.54***

.64***847

.64***

.59***

.81***61

3

.75***

.60***

.63***848

.82***

.66***

.69***53

4

.70***

.53***

.60***769

.83***

.62***

.73***46

5

.78***

.51***

.53***7210

.81***

.75***

.89***32

segment: 11 12 13 14 15


.51**

.45**

.89***26

.69***

.64**74***21

.70***

.74***

.73***20

.88***

.64**

.77***18

.55*

.55*

.92***14

Note. In the left hand column, n refers to the same time segment, and n-1 refers to the previous one. n refers to thenumber of respondents contibuting to the correlations for that time segment. *p < .05; **/> < .01; ***p < .001.


TABLE 13.

CORRELATIONS BETWEEN OWN SELF-REPORTED ATTITUDE AND OWN ESTIMATE OF OTHER'S ATTITUDEIN THE SAME TIME SEGMENT, AND OWN AND OWN ESTIMATE OF OTHER'S ATTITUDE AT THE


Own Self-Reported Attitude toward Other

segment:


n

segment:


n

1

_-

.8884

6

.84

.88

.8266

2

.83

.85

.8584

7.90.82.9061

3

.83

.82

.92848

.90

.89

.9153

4

.91

.87

.87769

.89

.88

.8046

5

.86

.86

.8472

10

.68

.76

.7832

segment:


n

11

.87

.96

.9226

12

.90-

.92

.9321

13

.89

.80

.9620

14.90.89.8618

15.92.89.9614

Note. In the left hand column, n refers to the same time segment, and n-1 refers to the previous one. n refers to thenumber of respondents contibuting to the correlations for that time segment. All correlations in the table aresignificant at p < .001.

are mirroring the other's affect at the just-pdor time segment. Whatever associations ap-peared, they should have reflected the arguer's feelings and his/her estimate of what theother person is feeling. Therefore, we used each respondent's estimates of his/her own andother's emotions. We felt that the other arguer's self-reports of emotion were irrelevantbecause the first arguer could respond only to whatever the other displayed. Similarly, thecoder data were irrelevant because the arguer could not be responding to coders' impres-sions.

The most striking features of the data in Tables 9-13 were the consistent and very largeassociations between own current emotion and all three predictors. Although we draw somedistinctions momentarily, the most general conclusion is that every measure of affect wasclearly predictable by own previous feeling, impression of other's previous emotion, andestimate of other's current emotion.

Self-consistency (i.e., from time N-1 to time N), of course, was prefigured in Table 6. InTables 9-13, one sees evidence of considerable stabihty in arguers' emotions throughout theinteractions. This was most obvious in the case of attitude toward the other (Table 13), butthe levels of self-association were consistent and high for each of the discrete emotions aswell.

The associations between own and (perceived) other's emotions indicated whether the twoparticipants mirrored affect. We also wondered whether such mirroring lagged. Obviously,there must be some delay if another's emotions are to influence one's own. At minimum, a"beat" would interpose between uptake of the other's display and adaptation of one's ownfeelings. In this study, we collected data in 30-second intervals. It may be that the lag, ifpresent, was sufficiently brief that it occurred within this interval. By comparing own feelingswith perception of other's affect at the same time (N) or the just previous time (N-1), wegained some insight into whether the lag extended for more than 30 seconds.

.84***

.33**

.56***- .24*-.64***

.41***

.93***

.49***- .13-.40***

.54***

.57***

.91***-.38***-.67***

-.29**- . 2 3 *- .45***

.78***

.55***

-.52***- .43***-.70***

.52***

.92***

89


TABLE 14.

CORRELATIONS BETWEEN OWN SELF-REPORTED FEEUNGS AND OWN PERCEPTION OF OTHER'S FEEUNGS

owni other—* Anger Happiness Sadness Tenderness Attitude to Other

AngerHappinessSadnessTendernessAttitude to O

Note. Nis either 83 or 84. *p < .05; **p < .01; ***/> < .001.

The data in Tables 9-13 show clear evidence of mirroring. With few exceptions (which weview as aberrations), other's perceived emotions at both times N and N-1 were predictive ofown feelings. For anger (Table 9) the associations for the two time segments were essentiallyequivalent. Given the stability of everyone's emotions through the arguments, this suggeststhat arguers were reacting no more to current than to previous emotional displays. Thehappiness data (Table 10), however, consistently revealed greater predictiveness from theother's current data. For nearly every time segment, other's perceived feelings at time N wereassociated more strongly with the arguer's current feelings than were the other's perceivedfeelings at time N-1. Immediacy, not lagging, was the rule for happiness. Results for sadnessand tenderness (Tables 11 and 12) revealed this same pattern, which again suggests thatemotional mirroring typically occurred more quickly than 30 seconds (or 15 seconds, to takethe average available time for reaction). Attitude toward the other (Table 13) was more likeanger in that the predictions from segments N and N-1 were essentially equivalent.

Between emotions. Data in Table 7 show how feelings interrelate in each arguer's emotionalsystem. The top portion of this table reveals interrelations among the mean self-reportedscores for anger, happiness, sadness, tenderness, and attitude toward the other. All weresubstantially intercorrelated and each correlation was statistically significant. Own anger wasassociated positively with own happiness and sadness but negatively with tenderness andattitude toward the other. Happiness was associated direcdy with sadness but inversely withtenderness and overall attitude. Sadness was negatively related to tenderness and attitude.Once again, happiness' role was unclear but the other results make intuitive sense.

Unlike the other affective measures, attitude toward the other is not a discrete emotion. Insome respects, it encapsulates all of the arguer's feelings. We conducted a multiple regressionin which we predicted own mean self-report of attitude toward the other by means of the fourdiscrete emotions, with R= .79, p< .001, n= 83. All of the emotions except happiness hadsignificant beta weights: anger (j3 = -.20, p < .05), happiness (j3 = .04, ns), sadness (|3 =-.45, p < .001), and tenderness (/3 = .36, p < .001). Multicolinearity among the predictorsmeans that these beta weights are extremely unreliable, but the overall R value still shouldbe accurate (Stevens, 2002, pp. 91-93). Table 7 is a better guide to the relationships amongattitude and the predictors. Our conclusion is that, within one person, the feelings assessedin this study are clearly connected and change in synchrony with one another duringinterpersonal arguments.

The next question was whether and how one arguer's feelings connect to his/her percep-tions of the other arguer's emotional states. Table 14 shows the correlations between meanself-report for each feeling eind mean perception of the other's feelings. In each case, thediagonal correlations (e.g., between own anger and perception of other's anger) are the

90


largest. The results in Tables 9-13 render these diagonal results unsurprising, but theynevertheless provide overall estimates of emotional convergence. In every case, the other'sparallel emotion was strongly predictive of one's own.

Table 14 also shows how the different affect states interrelated, which is genuine news.Anger was most indicative of other's sadness and other's attitude toward self (i.e., the otherarguer's "attitude toward other"). The happiness results are simply puzzling. Sadness waspredictive of other's anger, tenderness, and attitude toward self. Tenderness direcdy reflectedother's attitude toward self, and moved oppositely to other's anger and sadness. Own attitudetoward other was strongly correlated with all perceptions of other's feelings.

Again regarding attitude as a general sort of affective summary, we conducted a multipleregression involving all five measures of perception. The data revealed a high correlation,R = .93, p < .001, A = 82. Only two of the predictors had significant beta weights: anger(j3 = .09, ns), happiness (j3 = .04, ns), sadness 0 = -.19, p < .01), tenderness 0 = .04, ns),and other's attitude toward self (j3 = .86, p < .001). As before, substantial intercorrelationsamong the predictors make the beta weights essentially worthless, but the R value is accurate(Stevens, 2002, pp. 91-93). The relationships between own attitude toward other and one'sperceptions of the other's emotional states appear in Table 14. That information should bepreferred over the beta weights.

DISCUSSION

This study's major contributions lie in its results and in our experience with these variousmethodologies. Some particular points deserve to be highlighted.

1. The different methods of measurement did not converge. Self-reports, partner reports,and coder ratings gave substantially different results. These patterns could occur for tworeasons. First, the differences in information on which the ratings were based may bemuch more important that any common emotional information in the interactions. Thesecond is that several of these emotions may simply be easier for partners or observers todecode (Burgoon & Bacue, 2003, p. 191).

2. Our scales were reliable even though they did not seem to be at first glance. This resultshould be simple to replicate when using multiple coders. However, if future researchersintroduce unusual affective experience into the episodes (e.g., via confederates trained todo unexpected things), the sort of data we were able to generate in support of reliabilitywill be unavailable.

3. Several of the emotions seemed to be a communicative priority. The results suggest thatarguers apparendy work with their partner until some sort of uptake appears. Anger, oncefelt, may need to be conveyed, and so might one's attitude toward the other. Since thearguer and the partner in this study normally were friends, less emotional negotiation mayhave been needed for partner uptake than for coder identification of the arguer's feelings.Happiness, however, is apparendy a more personal matter and seems to be treated as noone else's business. Our results imply that it was important to arguers that the otherperson knew when they were angry and what their attitude toward the other was. Sincethis understanding is easier to achieve with a friend than with an unknown coder, partnersin argument read emotional states more accurately than did coders.

4. Several trait measures-TCP, BEQ, and ERQ-were highly predictive of arguers' emo-tional states during the interactions. State and trait measures are not often so closelyassociated. This finding may be explained at least pardy by the next point.

5. Felt emotions were stable throughout the interactions. Although we understand that this

91


pattern may be related to the particular experimental conditions, it does imply thatstability may be the default desideratum in the emotional domain while arguing. Ourarguers were engaged in a study, did not spontaneously initiate a topic, should never havebeen surprised or disappointed that their partner disagreed with them, discussed essen-tially public topics (cf. Johnson, 2002), and had no special emotional investment in theargument. If the interactions in this study are regarded as typical of low-impact, "default"arguments, one might suppose that arguments tend to have a stable emotional base.Departures from that "resting" stability would be interesting phenomena for future work.

6. Each felt emotion mirrored the perceived emotional state of the other arguer. Theseresults cannot be explained by the counterhypothesis that people projected their ownfeelings onto the other person. The correlations between own feelings and perception ofother's feelings were consistently higher than the correlations between self-ratings andother's ratings for the same emotion and person. If emotional projection were the keyphenomenon, the ratings of other's feelings would match self-ratings closely. This wouldbe occurring for both arguers simultaneously and we would have found much greaterconvergence between self- and other ratings.

7. Each felt emotion was highly indicative of the whole range of emotional states perceivedwithin the other arguer. Each of an arguer's feelings not only were predicted by percep-tions of the other's parallel emotion, as seen in Tables 9-13 and again in Table 14, butalso were responsive to perceptions of the other's full range of emodonal displays. Inanother research context, Dillard, Plotnick, Godbold, Ereimuth, and Edgar (1996) dis-covered that a change in one discrete emotion is likely to have consequences for the levelsof several other emotions. Our data support this conclusion. Face-to-face argumentsinvolve an emotional climate, an affective system, with interactive dynamics. Emotionsare not merely internally generated states but also are responsive to interlocutors.

Although farther work is highly desirable, we believe our findings and conclusions areserviceable as an initial description of what people feel during face-to-face arguments.

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face-to-face arguing is an emotional experience: triangulating methodologies and early findings...

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