“Sorry You Had to Go Through That”

Abstract

Social suffering is distress engendered by cultural, social, and political forces. Responses to stories of such suffering serve as rejoinders in an identity negotiation process and can foster either healing or compounded distress—for both individuals and communities alike. Because there is no published study (to our knowledge) that focuses on the language individuals use in responding to stories of social suffering, the present study aimed to develop a theoretically grounded and empirically derived taxonomy of these responses. In order to develop and validate such a taxonomy, we collected a total of 172 audio-recorded responses to two true stories of race-based social suffering from two samples of undergraduate students at a large university in the greater Los Angeles area. The resulting coding scheme is presented here along with evidence of its reliability and validity.

Keywords

racial and ethnic relations dialogue pitch electrodermal activity

I would say that it’s completely unacceptable for authorities in our government system to act like that and it’s embarrassing that this even happens considering that racism is something that at least I thought of was a thing of the past. And, uh, clearly, clearly it’s an issue that we still struggle with today.

I have a hard time believing that this story is true . . . Um, I honestly feel like I’m being lied to.

—(Anonymous Study Participants)

Suffering is an endemic and unpleasant fact of our world. Defined as “distress brought about by the actual or perceived impending threat to the integrity or continued existence of the whole person” (Cassell, 1991a, p. 24), suffering is typically considered an individual affliction. Indeed, although the phenomenology of suffering is ultimately private (Cassell, 1991b), there are many overlooked aspects to suffering that are social in nature and origin. To begin, the “whole person” threatened by suffering is a socially constructed identity. As Cassell notes, “when suffering exists, [it is an] identity that the sufferer fears will disintegrate . . . ‘myself’ is what is injured in illness and lost in suffering” (1991a, p. 24). In addition, the causes of suffering are often the result of social processes. As Farmer (2003) explains, “social forces ranging from poverty to racism become embodied as individual experience” (p. 19). Consequently, anthropologists, sociologists, and others have studied how these avowedly social processes come to manifest as distress through the concept of social suffering.

Social suffering extends the concept of suffering by highlighting the effects that cultural, social, and political forces have in shaping and producing individual and collective experiences of distress. As Kleinman, Das, and Lock put it (1997), “social suffering results from what political, economic, and institutional power does to people” (p. ix). This stands in some contrast to more widespread approaches that may isolate (e.g., Charmaz, 1983), pathologize (e.g., Nicholl & Thompson, 2004), or address the social nature of suffering from an interpersonal (not intergroup) perspective (e.g., Nordgren, Banas, & MacDonald, 2011). Rather than being the product of mere antagonism and exclusion, social suffering results when such behaviors are predicated on identities that are marginalized by larger social forces.

Social suffering can manifest in a variety of experiences, including isolation or a lack of opportunity for work and social engagement (e.g., Dossa, 2003). It can also be constituted by experiences of domination, repression, or negative emotions (e.g., humiliation) that may accompany discrimination on the basis of race, class, or gender. Social suffering can thus take many forms, including acts of (a) thinking, (b) feeling, (c) responding, and (d) acting based on experiencing distress in active (e.g., embodied) or inactive (e.g., disembodied) ways (Frost & Hoggett, 2008). As such, social suffering is a valuable extension of the suffering concept because it implicates all members in society through reference to larger forces and not to just the distressed individual alone.

Because social suffering entangles everyone, there is a lot at stake and much worth understanding from many different disciplinary vantage points. Social psychology, in particular, has a stake in better appreciating social suffering because of the central place that the concept affords social identities. One person’s distressing experience now becomes linked to others who avow an identity either allied with or oppositional to the hegemonic social forces that have contributed to the suffering (Polletta & Jasper, 2001). When individuals begin telling stories of their experiences, they may help others make meaning in a way that affords connections to additional experiences of social stigma and marginalization (Zraly & Nyirazinyoye, 2010). At the same time, they may find that the story telling itself mitigates their own distress when it is received by empathic others (Verdura, Alaimo, Borzì, Fazio, & Scavo, 2005). However, other audiences may not receive these stories hospitably because denial of suffering constitutes an important form of hegemonic influence (Cohen, 2013). For example, the removal of Indigenous children from their families in Australia remains a contested history, thus narrators of Stolen Generations testimonies often face denial when telling their stories to non-Indigenous audiences—from disputing the number of children who were removed to accusing witnesses of “false memory syndrome” (Kennedy, 2004). As these examples suggest, individual responses to stories of social suffering can foster either healing or compounded distress—for both individuals and communities alike. As a result, it is important to understand and begin modeling the ways in which such accounts of social suffering are addressed. Unfortunately, outside of a few passing descriptions (Seth, 2007), often emergent in interview data from the victims themselves (Browne et al., 2011; Lafrance, 2014), there is no published study (to our knowledge) that focuses on the language individuals use in responding to stories of distress labeled as “social suffering.” Consequently, the present study aimed to address this lacuna by developing a theoretically grounded and empirically derived taxonomy of such open-ended responses.

Review of the Literature

Although responses to stories of social suffering have been understudied, a developed literature has emerged regarding language use in response to individual suffering (i.e., distress that does not implicate a social identity). Within the communication and psychology literatures, the circumstances of distress range from “everyday” problems to traumatic events; in the medical literature distress is typically understood in the context of physical pain and end-of-life concerns. Despite such contextual differences, all of these literatures employ a distinction between responses that express a hearer’s openness toward, involvement with, and focus on the person in distress versus those that suggest avoidance and reluctance (e.g., Barbee & Cunningham, 1995; Dieltjens, Heynderickx, Dees, & Vissers, 2014; Levinson, Gorawara-Bhat, & Lamb, 2000; Mikulincer, Shaver, Gillath, & Nitzberg, 2005). Additionally, these literatures also distinguish between emotionally oriented responses (i.e., those that provide nurturance, reassurance, and support) and those that are instrumental in character (i.e., those that are active, rational, and problem focused; e.g., Cossette, Frasure-Smith, & Lespérance, 2002; Cutrona & Suhr, 1994; Miller, 2007; Perrine, 1993). Moreover, some studies suggest that these response characteristics may be related when, for example, respondents attempt to manage their involvement with another’s suffering through problem-focused responses (Lazarus, 1993; Marmar et al., 2006). Thus, the language used to respond to individual suffering may be categorized on at least two, perhaps related dimensions.

In addition to classifying the nature and content of responses to individual suffering, scholars have also developed several constructs with which to examine them, including “next moves” (Barker, 2007; Coupland, Coupland, Giles, & Henwood, 1991), “self-disclosure convergence” (Le Poire, Ota, & Hajek, 1997), and “communicative responsiveness” (Stiff, Dillard, Somera, Kim, & Sleight, 1988). Among these, the most widely used construct has been that of a “person centered message” (e.g., Burleson, 1982, 1983; Clinton & Hancock, 1991; High & Dillard, 2012; Samter, Whaley, Mortenson, & Burleson, 1997; Weger & Polcar, 2002). According to Burleson, person centeredness reflects messages that “explicitly acknowledge, elaborate, legitimize, and contextualize the distressed other’s feelings and perspective” (2008, p. 208). The term explicit is noteworthy because the emphasis is on verbal person centeredness, not implicit or nonverbal person centeredness. Although the construct of “nonverbal immediacy” (Andersen, 1985) is sometimes investigated in conjunction with person centeredness, it is treated separately from it (e.g., Jones, 2004; Jones & Guerrero, 2001; Woods, 1996).

Despite the usefulness of the person-centered message construct, neither it nor any of the other constructs capture the full spectrum of individuals’ communicative responses to suffering. Indeed, as Bippus (2001) notes in her review, “additional [response] characteristics are also important” (p. 303). To begin, responses to suffering are often conveyed primarily through nonverbal channels (Andersen & Guerrero, 1998). As a result, any attempt to chart responses to suffering should employ a lens that captures both verbal and nonverbal behaviors (Jones & Guerrero, 2001). Additionally, several constructs place an (over)emphasis on the therapeutic insight of responses (e.g., high scoring messages in the person-centered coding scheme are those which help the other to gain perspective on his/her own feelings, Burleson, 1982). Although such insight is important in many contexts, there are times when it is less important or even undesirable (Lemieux & Tighe, 2004). Indeed, testimonies of social suffering commonly appear during interracial dialogue (Drew, 2012; Simpson, 2008; Srivastava & Francis, 2006) and many facilitators find that the most helpful responses are compassionate or nonjudgmental in tone, but not necessarily therapeutic (e.g., Cargile, 2010; Carson & Johnston, 2000). Thus, although the communication literature provides useful constructs to understand language use in response to individual suffering, these constructs are potentially limited when considering application to contexts of social suffering.

Despite the fact that the constructs in this literature are not ideally adapted to the task at hand, it is worth noting that the psychology literature offers great insight with regard to emotional reactions (Batson, Fultz, & Schoenrade, 1987) and behavioral responses to both individual (e.g., Masten, Morelli, & Eisenberger, 2011; Stocks, Lishner, & Decker, 2009) and social suffering (e.g., Eisenberg, Eggum, & Di Giunta, 2010; Hein, Silani, Preuschoff, Batson, & Singer, 2010). Although sometimes investigated in terms of sympathy (i.e., awareness that another’s suffering warrants allieviation; Wispé, 1986), emotional reactions to suffering have been studied largely through the lens of empathy (i.e., an emotional response congruent with that perceived in another; Batson, 1991). Thanks to human neurobiological structures (e.g., mirror neurons), individuals often experience the distress of another on observing it, whether the source of distress is social or physical in origin (Eisenberger & Lieberman, 2004). Not only have studies begun to chart the conditions of empathic reacting to directly witnessed suffering (e.g., Beeney, Franklin, Levy, & Adams, 2011; Shamay-Tsoory et al., 2013; Xu, Zuo, Wang, & Han, 2009), but more germane research has explored empathic reactions to stories of social suffering.

As with witnessed suffering, stories of suffering often engender empathic reactions under certain conditions. For instance, in a study by Bruneau, Dufour, and Saxe (2012), participants reported compassion for unfamiliar outgroup protagonists (i.e., South Americans) telling stories involving physical and emotional suffering, but not for conflict outgroup protagonists (i.e., Arabs or Israelis). In a like manner, male participants in a study by Rice, Chaplin, Harris, and Coutts (1994) reported compassion for female narrators of stories of rape with victim suffering, but rapist male participants did not. As these studies indicate, social identities clearly affect participant empathic reactions to stories of social suffering. One identity, in particular, that has attracted attention in media stories of social suffering is gender (Ong, 2014). Höijer (2004), for example, found that televised accounts of suffering aroused more compassion among female than male participant viewers. Likewise, Cameron and Seu (2012) found that female participants not only greeted accounts of suffering with great empathy but also noted the ways in which these empathic reactions were diminished when the victims (or their advocates) were perceived to threaten the participants’ literal or metaphorical safe spaces. For these women, empathy seemed limited to circumstances in which they felt in control.

In addition to exploring the contexts of empathic reacting, research has also investigated behavioral responses to stories of social suffering. As many authors have noted (e.g., Eisenberg & Miller, 1987; Griffin, Babin, Attaway, & Darden, 1993), empathic reactions often lead people to help others in need. Consequently, many studies have found that stories of social suffering are oftentimes greeted with prosocial actions. For example, R. W. Smith, Faro, and Burson (2013) found that participants donated to the educational needs of six undereducated children in Africa when they were described as siblings, more so than in a condition in which the story of the children described them as unrelated. Similarly, an identifiably needy family was found to increase participant donations to Habitat for Humanity compared with a condition in which the needy family was merely hypothetical (Small & Loewenstein, 2003). These and other studies (e.g., Merchant, Ford, & Sargeant, 2010; Oliver, Dillard, Bae, & Tamul, 2012) indicate that accounts of social suffering can, under certain conditions, encourage prosocial actions. Despite this, it must be noted that stories of social suffering can also encourage antisocial reactions and behavioral responses. In particular, research has found that individuals who portray stories of negative life events as social suffering often face derogation, both in terms of perception (e.g., Kaiser & Miller, 2001) and actual treatment (e.g., Maass, Cadinu, Guarnieri, & Grasselli, 2003). Thus, listener emotional reactions and behavioral response to stories of social suffering are indeed multifaceted and complex.

Despite the fact that the reviewed literature provides much in terms of understanding a host of reactions and responses to social suffering, the communication and psychology literatures still lack a framework well suited to the task at hand: charting individuals’ communicative responses to stories of social suffering. None of the constructs (e.g., person-centered messages, feelings of empathy, or helping behaviors) offer a fitting starting point. The medical literature, on the other hand, holds promise in this regard.

In clinical settings, caregivers frequently encounter patients suffering with physical pain and/or end-of-life concerns. Consequently, the medical literature has devoted much research to describing and understanding caregiver responses in these situations. Investigations of these responses have traditionally focused on the efficient use of language and material provisions (e.g., Arendt, 1968) with the assumption that direct communication and appropriate physical care are ideal caregiving behaviors. Some scholars, however, have challenged this assumption and have promoted growing recognition that interpersonal reactions and emotional support also play an important role in patient care (e.g., Fine & Therrien, 1977; Johnson, 2014). As Wright, Watson, and Bell (1996) note, “the capacity of clinicians to be ‘witnesses’ to the stories of suffering of patients and families is central to providing care; it is frequently the genesis of healing, if not curing” (p. 161). As a result, many more recent studies have investigated socioemotional aspects of the communicative responses that caretakers offer suffering patients (e.g., Ford, Fallowfield, & Lewis, 1996; Morse, Edwardsen, & Gordon, 2008; Sheldon, Hilaire, & Berry, 2011). Though the findings are diverse, several taxonomic schemes have emerged to characterize these responses.

Attempts to describe and categorize caregiver communicative responses to suffering patients run the gamut, including one-dimensional coding schemes for pencil-paper responses (Winefield & Chur-Hansen, 2000), two-factor schemes (Zachariae et al., 2003), four-factor observer-coded schemes for prescriptive behavior (Krupat, Frankel, Stein, & Irish, 2006), and four verbally and nonverbally expressed emotion types (Preston, Hofelich, & Stansfield, 2013). Undoubtedly, the most common approach to categorizing caregiver responses in a clinical setting is in terms of empathic content (Hemmerdinger, Stoddart, & Lilford, 2007) and one such widely used taxonomic scheme is the Empathic Communication Coding System (Bylund & Makoul, 2002). In this system, physician responses are categorized according to the level of validation provided for a patient’s statement of emotion, progress, or challenge. This system codes both verbal and nonverbal behavior; however, it is modeled after Burleson’s (1994) person-centered message construct and is thus one-dimensional. In addition to empathy, compassion too has more recently emerged as an important caregiving construct (e.g., Fan & Lin, 2013) and an excellent scheme has been developed to code three elements of a caregiver’s expressed compassion: recognition of the patient’s suffering, emotional resonance and movement toward addressing suffering (Cameron, Mazer, DeLuca, Mohile, & Epstein, 2013). Although detailed and well suited for capturing both verbal and nonverbal expressions of caregiver compassion for witnessed suffering, this coding scheme remains an incomplete instrument for assessing the full spectrum of responses to social suffering. Any coding scheme that focuses exclusively on the presence (or plain absence) of a prosocial response is not complex enough for social suffering because group identities invite responses that may not merely lack empathy or compassion but may also be pointedly ambivalent (Katz, 1981) or antagonistic (Maier, 2008).

Despite the fact that the medical literature does not provide a ready-made taxonomy for coding communicative responses to stories of social suffering, it does offer a useful theoretical framework, nonetheless. Specifically, the model of empathetic communication by Morse, Bottorff, Anderson, O’Brien, and Solberg (1992) is suitable for grounding development of the needed taxonomy. This model organizes communicative responses along two orthogonal dimensions: focus (sufferer focused vs. self-focused) and affect type (“first level,” reflexive vs. “second level,” learned). These dimensions correspond roughly (though not precisely) to the basic distinctions discussed earlier (i.e., involvement vs. avoidance; emotional vs. instrumental) but their orthogonal arrangement allows for a wide variety of response types to be categorized in relation to one another. In this manner, the model offers more terrain over which to chart a rich understanding of complex response patterns (e.g., those that adopt a problem-focused reaction in an effort to manage involvement with another’s suffering and those that adopt such a focus because the hearer is deeply invested in another’s suffering). Moreover, by emphasizing the affective character of responses, the model encourages attention to both nonverbal and verbal behaviors. In sum, the model of empathetic communication (Morse et al., 1992) provides a well-suited theoretical foundation to address a lacuna in the literature: the development of a taxonomy of individual, open-ended responses to stories of social suffering constituted by both verbal and nonverbal behaviors.

Method

Taxonomy Development

In order to develop the taxonomy, we began with open-ended responses to two true stories of race-based social suffering collected from 47 undergraduate students at a large university in the greater Los Angeles area, recruited to take part on a voluntary basis in exchange for extra course credit. Participants were informed that the research concerned reactions to stories of racial profiling, thus after hearing each testimony, they were instructed to imagine that this person told this story to them and to respond as if they were speaking directly to this person. Their responses were audio-recorded, using a stereo headset with a microphone, as they sat alone in a campus laboratory room. In order to minimize social desirability bias, they were informed that the audio recordings would be reviewed neither by the study proctor nor their course instructor.

The stories of social suffering were audio recordings (only) taken from video clips in a series of oral histories (Glide Racial Justice, n.d.). Both were approximately 1:45 minutes in length and were presented in random order. One story was told by a man named Terry describing a time when he was treated differently than White customers when purchasing a pair of shoes, and another told by a woman named Diane describing her experiences in New Orleans during hurricane Katrina. Terry’s story was selected to represent a testimony of “individual racism” (i.e., events experienced on a personal level), whereas Diane’s story represented an experience of “collective racism” (i.e., when organized racial others seek to restrict the rights of African Americans, see Dovidio & Gaertner, 1986; Utsey & Ponterotto, 1996). Because of technical difficulties, three participants were not recorded. Additionally, three participants were excluded because of their extremely limited fluency in spoken English. This left a sample of 41, including 12 males and 29 females. They were on average 22.13 (SD = 2.36) years old, reported a variety of racial/ethnic backgrounds (13 Caucasian, 12 Hispanic, 7 Asian, 2 African American, 4 multiethnic, 2 “other,” and 1 declined to state) and included 5 noncitizens/ nonnative English speakers.

It was determined a priori that the unit of analysis would be the entire response and not individual messages, even though responses can contain “multipart” or “mixed messages” (e.g., Shifflett-Simpson & Cummings, 1996). This decision was made in light of a theory of modern racism (McConahay, 1986), which contends that ambivalence is a real, though less socially objectionable form of bias. Studies have demonstrated that feelings of both friendliness and rejection toward stigmatized group members can exist side by side (Hoffarth & Hodson, 2014; Katz, & Hass, 1988); thus, if an individual message was selected as the unit of analysis, the taxonomy would be unable to detect ambivalent or other complex response patterns. For this reason, we sought to induce categories that could be applied to participants’ entire responses.

Working with 82 responses, a coding scheme was developed by the first author in order to categorize the latent, projective content (Potter & Levine-Donnerstein, 1999) of each response with respect to the two dimensions articulated by Morse et al. (1992): focus and affect type (see Figure 1). The dimension of focus regards whether the respondent is oriented toward engaging the other’s experience of suffering (i.e., other-focus) or protecting the self from the other’s experience (i.e., self-focus). The dimension of affect type describes the emotional quality of the response: “reactive” responses are reflexive, spontaneous, and arousing, whereas “mitigated/mixed” responses employ some strategy or cultivated habit to manage or direct one’s emotions (e.g., Gross & John, 2003; Woltering & Lewis, 2009) or are generated by processes of ambivalence, dissociation, or dysregulation.¹ After listening, several times, to the entire population of oral responses, nine response types were identified through analytic induction (Bulmer, 1979) by the first author and then fit within the two-dimensional framework. Subsequently, the second author was trained to identify the response types and then coded all 82 responses independently. She discussed differences in her codings with the first author and helped refine the final taxonomy, presented schematically in Figure 1 and described in the appendix.

Figure 1.

A two-dimensional model of individual responses to stories of social suffering.

Reliability and Validity Assessment

After this theoretically grounded and empirically derived taxonomy of individual responses to stories of social suffering was developed, it was submitted to analyses of both reliability and validity. Alongside the first author, two additional trained coders (not involved in the development of the coding scheme) applied it to a separate sample of 79 audio-recorded responses to the woman’s story of collective racism (only) in an effort to assess its reliability.² These responses came from a second sample of 95 participants recruited on a volunteer basis from the same population of undergraduate students. Participants included 30 males and 65 females, were on average 23.19 (SD = 4.46) years old, reported a variety of racial/ ethnic backgrounds (38 Caucasian, 25 Hispanic, 13 Asian, 6 African American, 8 “other,” and 5 declined to state), and included 2 noncitizens/nonnative English speakers. Additionally, concurrent validity of the taxonomy was judged by considering the coded responses in relation to a series of other collected measures, including levels of self-reported trust and empathic concern, the observed pitch and length of oral responses, and mean levels of electrodermal activity (EDA).

Validity in coding the first dimension of “focus” (i.e., self or other) was judged against three measures. First, trust of the speaker offering testimony of social suffering was measured using four adjective items from the Individualized Trust Scale (trustworthy, candid, honest, reliable; Wheeless & Grotz, 1977; Sample 1: α = .78; Sample 2: α = .86). It was expected that responses coded as other-focused would come from participants reporting more trust of the speaker, compared with participants whose responses were coded as self-focused. Second, the word count of each response was expected to vary in relation to its coded focus. Longer responses have been found to indicate greater empathy (K. D. Smith, Keating, & Stotland, 1989), increased verbal person centeredness (Burleson & Samter, 1985), greater involvement (Leshed, Hancock, Cosley, McLeod, & Gay, 2007), and intentional social support (Galegher, Sproull, & Kiesler, 1998). Consequently, responses coded as other-focused were anticipated to be longer. Third, empathic concern for the speaker was measured using six items adapted from the empathic concern subscale of the most widely used measure of empathy—the Interpersonal Reactivity Index (IRI; Davis, 1983; Sample 1: α = .81; Sample 2: α = .85). Other-focused responses were anticipated to come from respondents who reported greater empathic concern for the speaker.

In addition to the first dimension of focus, validity for the second dimension of “affect type” (i.e., mitigated/mixed vs. reactive) was assessed in relation to three measures of emotional arousal. Left- and right-sided EDA values were recorded for each participant using two Q sensors (a small, commercially available device that measures skin conductance). EDA is a widely used index of emotion-related autonomic nervous activity (i.e., sympathetic arousal, Boucsein, 1992). A 6-second sample was registered 6 seconds prior to the period when participants rated their trust and empathic concern for the speaker—a window of time that immediately followed (or was simultaneous to the end of) their oral response. Because changes in tonic EDA occur gradually (Figner & Murphy, 2011), and because a latency period is recommended when measuring these levels (Dawson, Schell, & Filion, 2000), mean EDA was measured during a window of time following (most of) the oral response in order to better represent the sympathetic arousal that occurred during the response.

Additionally, a third measure of emotional arousal was used here: fundamental frequency or pitch. Pitch is a reliable indicator of arousal (Juslin & Scherer, 2005) because muscles tighten with arousal. When vocal muscles tighten, they vibrate at a higher frequency, which, in turn, produces a higher pitch (Titze & Martin, 1998). Consequently, mean levels of the standard vocal measurement of pitch (F0) were calculated for the initial 7 seconds of each response (following Elkins & Derrick, 2013) using the Phonetics software Praat (Boersma, 2002). Responses coded as reactive were anticipated to come from respondents with higher levels of observed arousal as measured by both mean EDA and pitch.

Results

Reliability of the taxonomy was established through calculation of Krippendorff’s alpha—the most suitable metric for behaviors such as those coded here (Bakeman, 2000). When the 79 audio-recorded responses from the second sample of respondents were independently coded according to the nine response types by three coders, the resulting alpha level was calculated to be .71; agreement with regard to the collapsed four categories of the two-dimensional framework was .70 (i.e., I, reactive/other-focus; II, mitigated-mixed/other-focus; III, reactive/self-focus; and IV, mitigated-mixed/self-focus,). A criterion threshold of .70 is typically set for exploratory research such as this (Lombard, Snyder-Duch, & Bracken, 2002); thus, reliability among the three coders for both the nine response types and the four-category, two-dimensional model was deemed acceptable. The frequency of these responses across both samples of participants can be found in Table 1.

Table 1.

Frequency of Responses to the Male and Female Testimony.

	Sample 1 male frequency	Sample 2 male percent	Sample 1 female frequency	Sample 1 female percent	Sample 2 female frequency	Sample 2 female percent
Empathic anger	10	24.4	4	9.8	4	4.4
Empathic anger/sad	1	2.5	5	12.2	4	4.4
Empathic sadness	3	7.3	5	12.2	19	21.1
Problem solving	3	7.3	2	4.9	5	5.6
Composed caring	14	34.1	12	29.2	17	18.9
Victim blaming	0	0.0	1	2.5	3	3.3
Pro forma	6	14.6	3	7.3	30	33.3
Intellectualizing	3	7.3	3	7.3	3	3.3
Skepticism	1	2.5	6	14.6	5	5.6
Total	41	100.0	41	100.0	90	100.0

With demonstration of the four-category scheme’s reliability, its concurrent validity was assessed in relation to six measures. To determine whether responses coded as other-focused or self-focused were associated with significantly different levels of self-reported trust, observed word count, or empathic concern, a multivariate analysis of variance (MANOVA) was conducted for Sample 1. A significant multivariate effect was found, F(3, 78) = 2,761, p = .048; Wilk’s Λ = 0.904, partial η² = .096, along with significant univariate effects on self-reported trust, F(1, 80) = 5.242, p = .025, partial η² = .061, word count, F(1, 80) = 5.270, p = .024, partial η² = .062, but not for empathic concern, F(1, 80) = 1.381, p = .243, partial η² = .017. In each case, responses coded as other-focused were associated with higher levels of trust, word count, and empathic concern than responses coded as self-focused. The descriptive statistics are presented in Table 2.

Table 2.

Descriptive Statistics for Trust, Empathic Concern, and Word Count by Response Focus.

Variable	Sample 1 mean	Sample 1 SD	Sample 1 n	Sample 2 mean	Sample 2 SD	Sample 2 n
Other-focus
Trust	6.36	0.68	58	5.95	0.93	49
Empathic concern	4.23	0.76	58	4.14	0.72	49
Word count	117.47	72.78	58	72.53	64.02	49
Self-focus
Trust	5.72	1.33	24	5.09	1.18	41
Empathic concern	3.98	0.87	24	3.43	0.86	41
Word count	74.67	64.26	24	43.68	71.37	41

In order to further assess the concurrent validity of the other versus self-focused responses, a second MANOVA analysis was conducted for Sample 2. As before, there was a significant multivariate effect for focus, F(3, 86) = 9.05, p = .000; Wilk’s Λ = 0.760, partial η² = .240, along with significant univariate effects on self-reported trust, F(1, 88) = 15.003, p = .000, partial η² = .146, word count, F(1, 88) = 4.082, p = .046, partial η² = .044, and empathic concern, F(1, 88) = 18.218, p = .000, partial η² = .172. Again, responses coded as other-focused were associated with higher levels of trust, word count, and empathic concern than responses coded as self-focused. The descriptive statistics are presented in Table 2.

To determine whether responses coded as reactive or mitigated/mixed were associated with significantly different changes in both left- and right-sided EDA over baseline, a 2 (Affect Type) × 2 (Presentation Order) MANOVA was conducted for Sample 1; the inclusion of presentation order was deemed necessary here because tonic levels of EDA change gradually over time. A significant multivariate effect was found for affect type, F(2, 67) = 5.004, p = .009; Wilk’s Λ = 0.870, partial η² = .130, but not for presentation order, F(2, 67) = 0.374, p = .690; Wilk’s Λ = 0.989, partial η² = .011, or for the interaction term, F(2, 67) = 1.457, p = .240; Wilk’s Λ = 0.958, partial η² = .042. Univariate effects were significant for both left-sided, F(1, 68) = 9.497, p = .003, and right-sided EDA, F(1, 68) = 9.130, p =.004. Additionally, although there was an observed difference in the mean vocal pitch levels between reactive and mitigated/mixed responses, a multiple regression analysis was conducted in order to control for the effect of participant gender; it is widely recognized that women have higher vocal pitch levels than men, regardless of arousal level. Although coded affect type and gender together explained a significant amount of the variance in mean vocal pitch levels, R² = .676, F(2, 72) = 75.190, p = .000, coded affect type did not predict vocal pitch, b = .008, t(73) = 0.108, p = .914, above and beyond the variation predicted by gender, b = .825, t(73) = 11.719, p = .000. The descriptive statistics associated with the above analyses are presented in Table 3.

Table 3.

Descriptive Statistics for Left EDA, Right EDA, and Pitch by Response Affect Type.

Variable	Sample 1 mean	Sample 1 SD	Sample 1 n	Sample 2 mean	Sample 2 SD	Sample 2 n
Reactive
Change in left EDA	0.30	0.25	30	0.10	0.19	29
Change in right EDA	0.29	0.27	30	0.10	0.19	29
Pitch	175.63	30.64	31	184.73	30.55	30
Mitigated/mixed
Change in left EDA	0.14	0.16	42	0.07	0.15	48
Change in right EDA	0.13	0.16	42	0.06	0.17	48
Pitch	154.47	48.83	44	136.49	42.87	38

Note. EDA = electrodermal activity. Because of technical difficulties, EDA was not measured simultaneous to 10 responses. Additionally, pitch data do not exist for two responses; the participant provided no oral response yet these “nonresponses” were still coded as Pro Forma reactions. Change in EDA was calculated as the following ratio: (Mean responding EDA − Mean baseline EDA)/(Mean responding EDA + Mean baseline EDA).

As before, in order to further assess the concurrent validity of the reactive versus mitigated/mixed responses, a second MANOVA analysis was conducted for Sample 2. A significant multivariate effect was not found for affect type on a variate composed of both left and right mean EDA levels, F(2, 74) = 0.501, p = .608; Wilk’s Λ = 0.987, partial η² = .013. However, a significant multiple regression analysis, R² = .801, F(2, 65) = 130.892, p = .000, revealed that coded affect type did explain mean vocal pitch levels, b = .182, t(66) = 2.942, p = .005, above and beyond the effect of gender, b = .799, t(66) = 12.913, p = .000. The descriptive statistics associated with these analyses are also presented in Table 3.

Discussion

In hopes of encouraging social psychological investigation of communicative responses to social suffering, the present study sought to develop a taxonomy theoretically grounded in a clinical model of empathetic communication and empirically derived from open-ended responses to testimonies of both individual and collective racism. We began by adopting the two-dimensional framework articulated by Morse et al. (1992) and then analytically induced 9 response types from a sample of 82 responses. The resulting coding scheme of latent, projective content was then reliably applied to a new sample of 79 responses by three coders working independently. With reliability established, concurrent validity of the two-dimensional framework was subsequently assessed in relation to six measures, across the two samples. The taxonomy demonstrated validity for all measures, though inconsistently for measures of emotional arousal across both samples.

Validity with regard to response “focus” (i.e., self or other) was consistently established using three measures; across both samples, significant multivariate effects were found indicating that responses coded as other-focused were associated with greater self-reported trust, empathic concern, and higher word counts than those coded as self-focused. Although differences in empathic concern were not found to be univariately significant in the first sample (only), the consistent direction and otherwise marked degree of difference demonstrated by these three measures collectively encourage great confidence in the validity of the self- and other-focused coding provided by the taxonomy.

In contrast, validity with regard to the second dimension of “affect type” (i.e., mitigated/mixed vs. reactive) is adequate and mixed. Confidence can be placed in the coded categories that mark differences between highly and moderately arousing reactions (e.g., “empathic anger” vs. “composed caring”) because these responses were found to reflect differences in both bilateral EDA activity (Sample 1) and vocal pitch (when controlling for participant gender—Sample 2). However, both theoretical and methodological questions remain with regard to the role of affect in responses to social suffering.

As noted previously, the theoretical framework developed by Morse et al. (1992) used the term experience, not as “emotion” or “affect,” though experience was clearly intended to distinguish between the emotionally controlled responses of experienced caregivers and the emotionally reactive ones of novices. Because the concept of experience is not as relevant to the context of social suffering as it is to clinical suffering, we adopted the underlying distinction between emotionally mitigated versus emotionally reactive responses. Yet even the terms of that adoption indicated that the distinction is not an easy one; the present model’s description of emotionally mitigated responses is broad and also includes emotionally mixed ones as well (e.g., skepticism; see Figure 1). Thus, despite the fact that the class of “reactive” responses was found to be more emotionally arousing than the class of “mitigated/mixed” responses (as indicated by two different measures in two instances), theoretical questions remain whether “affect type” may be better conceived of as a dimension constituted by three or more categories (e.g., reactive, mixed, and mitigated), or perhaps even by two or more dimensions (e.g., autonomic nervous activity and valence). Relatedly, there is also the methodological question of the suitableness of the present measures (e.g., when should tonic vs. phasic measures of emotional activity be used?). Regardless of these outstanding questions, the present results nevertheless demonstrate that the developed taxonomy offers scholars an acceptably reliable and valid metric to employ in the close study of verbal and nonverbal reactions to accounts of distress engendered by cultural, social, or political forces.

What sort of research can this metric facilitate? One example includes the tally of descriptive statistics as a measure of a sample’s receptivity toward a particular account of social suffering. As mentioned earlier, responses to suffering help to establish pathways that lead either toward healing or compounded distress, thus charting the frequency of different responses types can indicate a community’s readiness for reconciliation. In the present instance, the descriptive statistics suggest that participants comprising these two samples of undergraduate students are mostly disposed to connect with the social suffering of racism; across both samples and both speakers, the majority of responses were “other-oriented.” Despite this preponderance, responses nevertheless varied with respect to the speaker and his or her story. For example, the most frequent responses to the male testimony of individual racism included problem solving and composed caring (i.e., Quadrant II: 41.4%), yet these responses less frequently greeted the female testimony of collective racism (i.e., 34.1% and 24.5% in Samples 1 and 2, respectively). Moreover, the most frequent responses to her testimony among Sample 2 participants included pro forma, intellectualizing, and skepticism (i.e., Quadrant IV: 42.2%). These differences suggest that the population of undergraduate students studied here may adopt a less defensive posture with regard to claims of individual racism but is not (yet) entirely accepting of collective racist claims. This suggests that victims of collective racism (e.g., mass incarceration, Alexander, 2012) may (still) find their distress compounded by the responses of others, even among others from a relatively liberal population such as undergraduate students at a large university in the greater Los Angeles area.

Beyond generating such descriptive statistics, this taxonomy can also be employed in a wide variety of ways to help scholars better understand processes related to social suffering. For example, although we have speculated that “self-focused” responses compound victim distress compared with “other-focused” reactions, this claim can now be assessed in an externally valid manner by comparing the effects of genuine (verbal and nonverbal) responses identified by this coding scheme (either here or prospectively elsewhere). In addition, actual communicative responses can now be used as a dependent measure when investigating the influence of any number of variables, including those believed to either discourage (e.g., right-wing authoritarianism) or engender (e.g., openness to experience) empathic reactions, as well as the impact of prosocial treatments such as intergroup dialogue programs (e.g., Nagda et al., 1999).

In sum, this taxonomy offers a valid and reliable manner to systematically observe extemporaneous verbal and nonverbal responses to stories of social suffering and thus provides scholars an additional tool for use in research across many different settings, including nonlaboratory ones.

Despite the overall success of this taxonomy, there are limitations that should not be overlooked. First, as already discussed, outstanding questions persist regarding the model’s ability to capture all variation in response affect. Specifically, additional distinctions may be warranted regarding the emotional quality of responding beyond arousal. Second, this taxonomy requires coders to judge the latent, projective content of the responses—a demanding task that necessitates much training and full attention. Such judgments necessarily depend on the coder’s own intersubjective experiences, thus other coders working under different conditions may not necessarily replicate the decisions rendered here. Third, although there was some variety with regard to both the speakers and the testimonies of race-based social suffering used in this study, there may be other stories that evoke responses outside the universe of those observed here. Moreover, questions remain about the generalizability of these categories to responses that occur in the context of other intergroup relations (e.g., gender- or age-based social suffering). Last, the responses from which this taxonomy was derived are audio-recordings of undergraduate students seated alone in a lab. Responses gathered from other participants in other settings, including those in which the storyteller is physically present, may vary from those studied here. Thus, despite our efforts, the present taxonomy may prove to be incomplete. Certainly, much remains to be done to more completely investigate the content and application of these nine response types. Nevertheless, the theoretically grounded and empirically derived coding scheme developed here represents an important first step toward charting and more fully understanding individual communicative responses to accounts of social suffering.

Footnotes

Appendix

Acknowledgements

The authors would like to thank the two coders whose work was vital to this study: Kalifa J. Sproul and Lizette Álvarez. Additional thanks go to the Department of Communication Studies for material support. We are also grateful for the recommendations and suggestions provided by the reviewers and the editor.

Declaration of Conflict Interests

The author(s) declared no potential conflicts of interest with respect to the authorship and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Aaron Castelán Cargile (PhD, University of California, Santa Barbara) is a professor of communication studies at California State University, Long Beach. He has dialogued about race with more than 4,000 students and continues to search for humanistic, compassionate, and pragmatic responses to the challenges of facilitating interracial/intercultural discussion.

Leslie Ramos Salazar (PhD, Arizona State University) is an assistant professor of business communication and decision management in the Department of Management, Marketing, and General Business at West Texas A&M University. She conducts research in the areas of compassion, conflict management, and interpersonal communication.

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