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Pathways to Working Alliances: Special Educators’ Emotional Labor and Relationships With Students With Emotional and Behavioral Disorders


by Michael Valenti, Elizabeth Levine Brown, Christy Galletta Horner, Duhita Mahatmya & Jason Colditz - 2019

Background/Context: Research has also shown that educators who are more socially and emotionally competent are more likely to create nurturing relationships and high-quality classroom environments that result in more academic success for students. Despite the importance of teacher-student relationships on student outcomes, limited research has investigated factors that contribute to development of these relationships, particularly for special education teachers (SETs) working with students with emotional/behavioral disorders (EBDs).

Focus of Study: This study explores educators’ emotions in the classroom through emotional labor theory, a framework for understanding how employees engage in the deliberate suppression or expression of emotions to achieve an organization’s goals. We empirically investigate the potential connections between SETs’ perceptions of their administrators’ expectations about emotional displays, SETs’ emotional acting strategies, and teacher-student relationships.

Setting: This study was conducted within three schools in Western Pennsylvania serving students with EBDs in self-contained classrooms.

Participants: Participants included SETs (N = 61) serving K–12 students who have been identified as having EBDs. SET demographics were as follows: 75% female, average age 32 (range 23–51), and 97% Caucasian. Participants averaged 4.62 years of teaching experience with the study site.

Research Design: All SETs reported on their perceptions of emotional labor and their working alliances with each of their students in the fall semester. Students were nested within teachers, so we used multilevel path analyses to estimate mediational effects of emotional display rules and emotional acting on the teacher-student working alliance.

Results: The results of this study suggest important connections between SETs’ perceptions of emotional display rules, their use of emotional acting strategies, and their working alliances with students with EBDs. Specifically, SETs’ reported perceptions of negative display rules affected how they engaged in surface acting when interacting with students. SETs’ ratings of surface acting were associated with their working alliance tasks scores.

Conclusions/Recommendations: These findings confirm recent research showing that educators engage in emotional acting and that some dimensions of this acting contribute to their relationships with students. Our findings may also suggest that surface acting is an acceptable emotional acting strategy that supports SETs’ relationships with students. Because the emotional labor research in special education has yet to extrapolate on what display rules lead to the emotional acting strategies that the organization desires, how we make these rules more explicit could help teachers establish more sensibility regarding this area of their job.



Special education teachers (SETs) play a crucial role in promoting the development of students’ social, behavioral, and academic competence (Akey, 2006; Crosnoe & Cooper, 2010; Garner, Moses, & Waajid, 2013; Myers & Pianta, 2008). This is especially imperative for students with emotional/behavioral disorders (EBDs), who can display significant externalizing (e.g., aggression) and/or internalizing (e.g., depression, anxiety) behaviors (Kauffman & Landrum, 2013) that put them at higher risk for poor school-based outcomes such as dropout, low achievement, or incarceration (Bradley, Doolittle, & Bartolotta, 2008; U.S. Department of Education, Office of Special Education Programs [OSEP], 2016; Wagner, Kutash, Duchnowski, Epstein, & Sumi, 2005).


SETs support their students’ competence through the context of their relationships (Hamre et al., 2013; Murray & Pianta, 2007; Sabol & Pianta, 2012), in particular, providing emotionally secure teacher-student relationships, prosocial peer relationships, and social and emotional interventions (E. L. Brown, Vesely, Mahatmya, & Visconti, 2017). Research has also shown that educators who are more socially and emotionally competent are more likely to create nurturing relationships and high-quality classroom environments (Pianta & Hamre, 2009) that result in more academic success for students (Crosnoe & Cooper, 2010; Rimm-Kaufman et al., 2002).


Despite the importance of teacher-student relationships to student outcomes, limited research has investigated factors that contribute to development of these relationships, particularly for SETs working with students with EBDs. To unpack the socioemotional mechanisms that may inform these relationships, current research explores educators’ emotions in the classroom through emotional labor theory, a framework for understanding how employees engage in the deliberate suppression or expression of emotions to achieve an organization’s goals (Grandey, Diefendorff, & Rupp, 2013). Although emotional labor literature broadly describes a potential connection between a school’s emotional display rules (EDRs) and how these rules might affect an educator’s choice of emotional acting (EA) strategies, this link has not been tested empirically among SETs and also may function differently for schools and educators with students with EBDs. In this study, we extend this growing body of literature by empirically investigating the potential connections among SETs’ perceptions of their administrators’ expectations about emotional displays, SETs’ EA strategies, and teacher-student relationships with SETs working in three alternative education programs serving students with EBDs.


SPECIAL EDUCATORS IN SELF-CONTAINED CLASSROOMS


Approximately 18% of students in the United States with EBDs are served in self-contained classrooms (OSEP, 2016). Typically, these students exhibit the most substantial learning and behavior challenges (Lane, Wehby, Little, & Cooley, 2005) and are at the highest risk for negative outcomes (Wagner, Newman, & Javitz, 2014). The self-contained setting is a part of the continuum of placements available through the least restrictive environment provision of the Individuals with Disabilities Education Improvement Act (IDEA, 2004). These classrooms exist to provide an opportunity for students whose behaviors pose a danger to themselves or others to access general education curricula, while developing social and behavioral competencies necessary to safely return to inclusive settings (Bullock & Gable, 2006; Hoge, Liaupsin, Umbreit, & Ferro, 2014). However, research examining factors that support SETs in providing effective instruction in these settings is exceptionally limited (Bettini, Crockett, Brownell, & Merrill, 2016; Bettini, Cumming, Merrill, Brunsting, & Liaupsin, 2017). Bettini and colleagues (2017) conducted a comprehensive literature review and found that only 10 studies since 1990 have explored SETs’ support systems in self-contained settings for students with EBDs, and none of these studies examined potential relationships between SETs and students, and the mechanisms that support those relationships.


However, existing studies have found that interacting with students who present significant social and behavioral challenges may take an emotional toll on SETs (E. L. Brown, Horner, Kerr, & Scanlon, 2014). Because of the nature of their responsibilities, SETs serving this population of students often feel isolated from colleagues (Albrecht, Johns, Mounsteven, & Olorunda, 2009), and they experience high levels of stress and burnout (Cross & Billingsley, 1994; Embich, 2001; Nichols & Sosnowsky, 2002). These challenges have been shown to be more prevalent among SETs than other populations of teachers (Brunsting, Sreckovic, & Lane, 2014; Jones & Youngs, 2012), and more prevalent among SETs serving students with EBDs than among other SETs (Adera & Bullock, 2010; Cassidy, 2011; Cross & Billingsley, 1994; Embich, 2001; Nichols & Sosnowsky, 2002).


DEFINING THE EMOTIONAL WORK OF A SPECIAL EDUCATOR


Special educators can experience a range of emotions while working in a school environment. Over the course of one school day, an educator may feel angry, frustrated, joyful, satisfied, disappointed, relaxed, anxious, etc. These emotions are often experienced—and articulated or withheld—during interactions with students (Schutz & Zembylas, 2009). In particular, SETs, who are often charged to work with the most challenging students, may have more emotionally intense interactions than their general education counterparts (Jones & Youngs, 2012). It is likely that SETs working with students with EBDs in alternative education settings (e.g., self-contained private schools, schools in residential treatment centers, juvenile detention centers) experience these emotionally charged interactions daily, given that students are typically transitioned into these settings because of a range of difficult behaviors displayed elsewhere (e.g., physical aggression, disruptive behaviors, behaviors associated with mental health needs; Carver, Lewis & Rice, 2010). These interactions may expose SETs to an increased level of emotional variance over the course of a school day and higher levels of stress (Jones & Youngs, 2012).


One way of conceptualizing this “emotional work” of teaching is through the emotional labor framework, which describes how educators manage and display their emotions while on the job (Grandey et al., 2013). Grounded in organizational psychology, emotional labor involves the assessment of two key constructs: emotional display rules (EDRs) and emotional acting (EA). Next, and in Table 1, we define these key constructs and how they apply to special education. Over the past decade, studies on educators’ emotional labor have been conducted in early childhood (R. T. Lee & Brotheridge, 2012), K–12 (Yin & Lee, 2012; Zembylas, 2002; Zembylas & Schultz, 2009), and special education (E. L. Brown & Valenti, 2013; E. L. Brown, Valenti, & Kerr, 2015; Kerr & Brown, 2015). These studies have shown that emotional labor theory is a useful framework for understanding educators’ work, because educators’ performances of emotional labor relate to perceptions of professional identity (E. L. Brown et al., 2014), emotional support of students and classroom organization (E. L. Brown et al., 2017), perceptions of commitment and job satisfaction (Isenbarger & Zembylas, 2006), and retention (Zhang & Zhu, 2008).

  

Table 1. Key Concepts in Emotional Labor

 

     Definition

     Example

Emotional Display Rules

The “standards for the appropriate expression on the job” (Ekman, 1973, as cited in Diefendorff et al., 2005, p. 343)

Teachers should welcome students’ inquiries graciously (positive display rule).


Teachers should not raise their voices or yell when upset with a student (negative display rule).

Surface Acting

On the surface, an employee portrays emotions that are not felt internally (Hochschild, 1983)

Despite being bored by a student’s third retelling of a story, the teacher feigns enthusiasm.

Deep Acting

The employee changes internally felt emotions to align with required emotional expressions of the organization (Morris & Feldman, 1996)

The teacher initially feels frustration that a student continues to make errors when completing an assignment. Eventually, the teacher recognizes that the student is working hard to learn the material and works to shift her frustration to appreciation for the student’s efforts.



RULES FOR MANAGING EMOTIONS IN SCHOOLS


EDRs are organizational expectations that describe how educators should display certain emotions and hide others depending on the situation (Diefendorff, Croyle, & Gosserand, 2005; Glomb & Tews, 2004). Display rules can be positive or negative; a positive display rule describes expectations for expressing emotions, whereas a negative display rule describes the expectation to withhold emotions. These rules describe how educators should regulate their emotions experienced on the job.


A school’s EDRs can be explicit or presumed. When these display rules are explicit, school administrators specifically define their expectations for emotional expression during training and supervision (“Don’t show your students that you are angry”). Otherwise, EDRs are presumed, leaving educators to discern them without support from administrators (“My principal probably wouldn’t want me to appear angry in front of my students”). When display rules are presumed, educators often rely on their perceptions of the professional norms and cultural commonalities of school environments to govern their emotional expressions (Yin & Lee, 2012).


Emotional labor theory postulates that how individuals manage their emotions is affected by their perceptions and knowledge of the EDRs in their setting (Hochschild, 1983). Therefore, in schools, how SETs perceive and interpret their school’s EDRs could influence whether and how they display certain emotions to students. The strategies that educators engage in to display or withhold these emotions in the classroom fall under the umbrella of EA.


EMOTIONAL ACTING IN SCHOOLS


Emotional acting involves how educators manage their emotions using two potential forms of acting: surface acting and deep acting (Brotheridge & Lee, 2003). Surface acting (SA) is the ability to disguise one’s feelings and is often described as “faking it to make it.” In contrast, deep acting (DA) involves the deliberate cognitive shift of emotions to align with the organization’s EDRs (Hochschild, 1983). For example, consider an educator who has devoted a significant amount of time and attention to helping a student complete homework. When this student arrives at school yet again without having done so, the educator may become frustrated. If the educator perceives a display rule preventing the display of this frustration, she could respond by SA: maintaining the internal experience of frustration but suppressing its expression. In contrast, she might engage in DA by reminding herself to assume the best and imagining possible positive explanations for the student’s missing homework (e.g., “the student is often responsible for caring for siblings in the afternoon and maybe they didn’t have time”) that evoke feelings of compassion, thus allowing her to shift her emotional state away from the initial frustration.  


HOW SPECIAL EDUCATORS’ EMOTIONAL WORK INFORMS RELATIONSHIPS WITH STUDENTS


Teacher-student relationships have important implications for student development and adjustment, especially in special education (Myers & Pianta, 2008). Specifically, developing supportive, caring relationships between SETs and students with EBDs can facilitate improved academic and behavioral outcomes (Mihalas, Morse, Allsopp, & Alvarez-McHatton, 2009). However, recent research calls for a more expansive definition of teacher-student relationships that includes additional relational components from Bordin’s (1979) conceptualization of the working alliance (Toste, Bloom, & Heath, 2014). Bordin’s definition contains three processes: (1) agreement on goals, (2) agreement on tasks, and (3) a positive bond between a helping adult and a person receiving services. This definition of alliance—termed the working alliance—underscores the importance of collaboration and agreement between these two parties to achieve positive outcomes.


Decades of research demonstrate that healthy alliances influence outcomes such as improvement in behavior and overall well-being, regardless of study design or treatment approach (Horvath, Del Re, Flückiger, & Symonds, 2011). For example, studies have found that developing strong alliances with youth can lead to reduced aggressive behaviors (Bickman et al., 2004) and decreasing trajectories of behavior problems (Ayotte, Lanctot, & Tourigny, 2016).

In schools, both public and alternative, SETs engage in instruction and intervention through the lens of their alliances with students. As such, teacher-student interactions are constantly influenced by the quality of these alliances. Extrapolating Bordin’s definition to schools, a healthy teacher-student alliance begins with a strong bond, which includes a shared sense of warmth, trust, and connectedness. A positive alliance is also strongly rooted in a sense of agreement between the SET and the student regarding the educational/behavioral goals of the school setting, as well as the daily tasks performed by both parties to achieve those goals.


It is possible that the emotional work of teaching, or emotional labor, may be directly related to the quality of these alliances. However, research on SETs’ emotional labor and their alliances with students, especially students with EBDs, remains sparse. One literature review posited that SETs’ management and expression of emotions informs their working alliances, including how they bond with students and establish educational goals and tasks with them (E. L. Brown & Valenti, 2013). More empirical research is needed to also investigate the presumed connection between educators’ perceptions of emotional display rules (EDRs) and their choice of emotional acting (EA) strategies. Previous studies in other fields have demonstrated this connection between display rules and EA. C. Lee, An, and Noh (2015) found that EDRs influenced flight attendants’ selection of EA strategies, and in a heterogeneous sample of working adults (sales, healthcare, education, clerical, etc.), Gosserand and Diefendorff (2005) found that awareness of and commitment to EDRs increased the likelihood that those rules would influence how individuals expressed or suppressed emotions.


There is a need for additional research investigating how schools’ EDRs relate to SETs’ EA strategies and how that potential relationship may influence the quality of teacher-student alliances. Research has yet to demonstrate that SETs’ EA strategies are influenced by their school’s EDRs. Establishing and maintaining alliances with students may require SETs to use effective EA strategies, so examining the mechanisms that may foster or influence the use of different strategies is critical. Using a multilevel path analysis, the current study asks the following research question: Does SETs’ reported use of EA strategies mediate a relationship between their perceptions of their school’s EDRs and their working alliances with students?


METHOD


SITES AND PARTICIPANTS


Participants were SETs and students with EBDs in self-contained classes within three schools in Western Pennsylvania. These schools work closely with neighborhood public schools to serve the most severe student populations both within the neighborhood school settings and in more private environments. The schools provide services to students with the highest emotional/behavioral needs, while also trying to move students back to inclusive settings in their neighborhood school. It is not uncommon for students in this state to attend a school other than their own neighborhood school they require self-contained classroom services.


Special Educators


Participants included SETs (N = 61) in self-contained classrooms serving K–12 students who have been identified as having emotional/behavioral disorders (EBDs). Each self-contained classroom contains SETs who share instructional tasks and collaborate to facilitate students’ social, behavioral, and academic development. All SETs were eligible to participate, and there were no exclusion criteria. Participation was not mandatory, and SETs could opt out of the study at any time. SETs were introduced to the study during prescheduled in-service days at each school site. During these meetings, SETs were able to sign consent forms indicating their willingness to participate in the study. Previous research within this school district used the same recruitment technique, which resulted in a response rate of 84%.


SET demographics were as follows: 75% female, average age 32 (range 23–51), and 97% Caucasian. Concerning education level, 62% of participants had a bachelor’s degree, 30% had a master’s degree, and the remaining 8% either had a two-year degree or had taken some college classes. Participants averaged 4.62 years of teaching experience with the study site (range: < 1 year to 21 years).


Students


Each school serves K–12 students (total N = 243) identified as having special needs (IDEA, 2004). Across the three school sites selected for this exploratory study, all students are diagnosed with EBDs and have goals to transition back to inclusive classrooms. Collectively, student demographics were as follows: 82% male, average age 15 (range 7–20), 69% Caucasian, 23% African American, and 8% biracial or other ethnicity. A total of 33% of students were enrolled in Grades 10–12; 36% in Grades 7–9; 22% in Grades 4–6; and 9% in Grades 1–3.


MEASURES


All SETs reported on their perceptions of emotional labor and their working alliances with students. Surveys were completed in the fall, which provided educators with time to establish alliances in the classroom before assessing the relationships across study variables.


Emotional Labor


The Emotional Labor of Teaching Scale (TELTS) measured SETs’ reported experience of emotional labor. The TELTS is an adapted measure, combining Brotheridge and Lee’s (2003) Emotional Labor Scale and Diefendorff and colleagues’ (2005) Emotional Labor Strategies Scale to fit educational contexts. Cukur’s (2009) Teacher Emotional Labor Scale also adapted the two aforementioned scales to study Turkish teachers’ emotional labor, but the translation from Turkish to English compromised the comprehension of the survey items. Our version of the TELTS was developed for American teachers and has been administered previously to general and early childhood education in-service teachers (E. L. Brown, 2011; E. L. Brown et al., 2017).


The TELTS includes two scales that measure the main components of emotional labor: emotional display rules (EDRs) and emotional acting. The EDRs scale includes two subscales: positive display rules (PDRs; e.g., “My school tells me to express positive emotions to students as part of my job”) and negative display rules (NDRs; e.g., “I am expected to suppress my bad moods or negative reactions to students”). These questions use a 5- point Likert scale, with items ranging from 1 (I strongly disagree) to 5 (I strongly agree). Higher scores are more indicative of the perceived existence of EDRs in the school setting. The EA scale includes two subscales: surface acting (SA; e.g., “To work with my students I act differently from how I feel”) and deep acting (DA; e.g., “I work hard to feel the emotions that I need to show”). Questions on the EA scale are measured on a 5-point Likert scale, ranging from 1 (never) to 5 (always). Higher scores indicate more frequent use of each form of EA. Mean scores were computed by aggregating the items for each subscale of the TELTS. Internal consistency estimates for the EDR instrument were PDRs (α = .831) and NDRs (α = .746); alphas for the EA instrument were SA (α = .730) and DA (α = .692).


Working Alliance


The Working Alliance Inventory, Short Form (WAI-SF; Tracey & Kokotovic, 1989) measured educators’ perceptions of their alliances with students. Educators completed a WAI-SF for each of their students separately. The WAI-SF is a 12-item survey that includes subscales for the three components of the alliance: goals (“We agree on what is important for [this student] to work on”), tasks (“I believe the way we are working with [this student’s] problems is correct”), and bond (“I believe [this student] likes me”). Each subscale contains four questions, which are then summed to produce subscale scores. Each item is answered using a 7-point Likert scale ranging from 1 (never) to 7 (always). Higher scores are indicative of a favorable or healthy working alliance. Cronbach’s alphas for the WAI-SF were: goals (α = .612), tasks (α = .972), and bond (α = .919).


DATA ANALYSIS


The goal of this study was to examine the association between SETs’ emotional labor and their working alliance with students with EBDs. Because SETs completed the working alliance measure for each student individually, the data are nested (i.e., multiple students for one educator) and required the use of multilevel models to accurately estimate variance and parameters within and between special educators (Raudenbush & Bryk, 2002). We determined intraclass correlations for goals (ICC = 0.19), tasks (ICC = 0.26), and bond (ICC = 0.29) to all be above the minimum threshold and an average cluster size of 7.04, which justifies the use of multilevel models for the study. Additionally, we investigated a mediational relationship between EDRs, EA, and the teacher-student working alliance. Thus, the model underlying the analysis to address our research questions was a two-level path model, with students at level 1 and educators at level 2. The two-level equation is as follows:


[39_22691.htm_g/00001.jpg]


Here, Yij, is the teacher-student working alliance measure for the ith student with the jth educator; all three working alliance measures are included in the model simultaneously and correlated to account for the relatedness of the components. Level 1 estimates the random intercept for the working alliance measure, capturing the nested nature of the data. Next, the random intercept, [39_22691.htm_g/00003.jpg]0j, estimated from the level 1 equation is subsequently estimated by the educators’ emotional labor. The described multilevel model was estimated using Mplus (version 7) software. Given that the dependent variables are continuous and correlated, Mplus uses full information maximum likelihood with robust estimation that allows for random intercepts, missing data, and nonindependence (Muthèn & Muthèn, 2004). The full information maximum likelihood procedure estimates the model parameters directly from the available data using an iterative expectation-maximization algorithm rather than doing imputations of the missing data first, as with other estimation procedures (Acock, 2005; Muthén & Muthén, 2004).


RESULTS


Our model included the two types of display rules (negative and positive), the two emotional labor subscales (surface and deep acting), and the three working alliance subscales (goals, tasks, and bond). Table 2 displays means and standard deviations of these variables, as well as the bivariate correlations among them. The two-level mediational model, with emotional acting (EA) (surface acting and deep acting) mediating the relationship between emotional display rules (EDRs; positive and negative) and educator-student working alliance (goals, tasks, and bond), had lower AIC (2220.155) and BIC (2321.121) model fit indices than the fully recursive model (AIC = 2228.275; BIC = 2352.542).1 Regression results for the fully recursive model are provided in Table 3. In addition, the corrected chi-square difference test determined that the mediational model is not significantly different from the fully recursive model. Mplus uses the Satorra-Bentler scaled chi-square; this calculated a difference of 3.929 with six degrees of freedom, which is not significant at the 0.05 level. Taken together, this analysis suggests that the mediational model was more parsimonious and better fitting for these data than the fully recursive model.



Table 2. Correlations, Means, and Standard Deviations

 

NDR

PDR

SA

DA

WA Goals

WA Tasks

WA Bond

M

SD

NDR

 

.44**

.49**

.10

-.03

.22**

.23**

3.71

.74

PDR

  

.24*

.11*

.01

.20**

.26**

4.15

.72

SA

   

.31*

.00

.28**

.23**

3.15

.59

DA

    

-.01

.03

.11*

3.52

.58

WA Goals

     

.18**

-.04

3.92

.36

WA Tasks

      

.77**

4.72

1.2

WA Bond

       

5.47

1.0

Note. NDR = negative display rules; PDR = positive display rules; SA = surface acting; DA = deep acting; WA = working alliance.

*p < .05. **p < .01.




Table 3. Standardized Regression Results for Fully Recursive Model Linking Emotional Labor to Teacher-Student Working Alliance


 

SA

DA

WA Goals

WA Tasks

WA Bond

 

β (SE)

β (SE)

β (SE)

β (SE)

β (SE)

Intercept, β 00

2.33 (1.16)*

4.79 (1.51)**

23.58 (4.97)***

4.90 (1.83)***

5.92 (1.99)***

NDR

0.45 (0.11)***

0.04 (0.15)

-0.00 (0.23)

0.11 (0.18)

0.06 (0.24)

PDR

0.09 (0.13)

0.10 (0.14)

-0.05 (0.16)

0.13 (0.17)

0.29 (0.19)

SA

  

0.01 (0.21)

0.38 (0.18)*

0.21 (0.23)

DA

  

-0.02 (0.19)

-0.12 (0.13)

0.08 (0.18)

Variance components, eij + r0j

0.76 (0.10)***

0.98 (0.04***

0.99 (0.02)***

0.75 (0.15)***

0.81 (0.15)***

Note. DA = deep acting; NDR = negative display rules; PDR = positive display rules; SA = surface acting; WA = working alliance.




The meditational model demonstrated that one type of EA in particular, SA, mediated the relationship between EDRs and the tasks component of working alliance. As shown in Figure 1, NDRs were positively associated with SA (β = 0.45; SE = .114, p < .001), which in turn was positively related to the tasks component of working alliance (β = 0.47; SE = .152, p = .002). This means that a one-standard-deviation increase in NDRs corresponded with a 0.45-standard-deviation increase in SA; in turn, a one-standard-deviation increase in SA corresponded to a 0.47-standard-deviation increase in the task component of the working alliance. The standardized specific indirect effect from NDRs to tasks was also significant in a positive direction (β = 0.21; SE = .09, p = .019). None of the other pathways was significant. Thus, the proposed mediational model was partially supported.



Figure 1. Parameter estimates for the mediational model linking emotional display rules, emotional acting, and teacher-student working alliance


[39_22691.htm_g/00004.jpg]




DISCUSSION


The results of this study suggest important connections among SETs’ perceptions of emotional display rules (EDRs), their use of emotional acting (EA) strategies, and their working alliances with students with EBDs. Our findings show that SETs’ perceptions of certain EDRs in their schools influenced how they emotionally act in classrooms. Specifically, SETs’ reported perceptions of negative display rules (NDRs) affected how they engaged in surface acting (SA) when interacting with students. Second, special educators’ ratings of SA were associated with their working alliance tasks scores. All the other tested pathways were not significant, which provides some insight into this nuanced area of work in special education.


Previous research in service industry settings has established a relationship between display rules and acting strategies (Gosserand & Diefendorff, 2005; C. Lee et al., 2015). Consistent with prior research, our findings suggest that SETs’ perceptions of negative EDRs may indeed influence their use of SA strategies in the classroom. SETs who indicated the presence of NDRs also reported engaging in increased SA. This finding suggests a rather intuitive connection between NDRs and EA. NDRs describe situations in which it might be preferable to withhold unfavorable emotions (often anger or frustration). It may be that SETs who believe that their school wants them to hide certain emotions when in the presence of students with EBDs engage in SA more, because this form of EA requires the educator to suppress those emotions.


Results further demonstrated that SETs who reported increased SA expressed greater agreement with their students on the daily educational tasks of their classroom environment. One possible explanation for this connection is that SETs who frequently surface act are more adept at communicating and promoting the appropriateness, fit, and desirability of educational tasks to students. Potentially, SETs who are skilled surface actors can convincingly express excitement about a history lesson, or conceal their boredom when presenting the same lecture for the fourth time in a given the day. In this sense, SA may function as a skill that SETs can employ to increase commitment to educational tasks for students with EBDs and to help them to elucidate how these tasks are important for each student’s own individual goals.  


Further, SA mediated the relationship between NDRs and the working alliance; SETs’ perceptions of NDRs were associated with greater engagement in SA, which then was associated with greater agreement with students on educational tasks. This interplay between NDRs, SA, and agreement on educational tasks aligns with emotional labor research studied in other professions (Gosserand & Diefendorff, 2005; C. Lee et al., 2015), suggesting that negative EDRs influence surface EA. This relationship is straightforward, because display rules describe when an individual should engage in different forms of EA. This result also begins to describe how EDRs and EA may affect other classroom-level phenomena, such as the working alliance and, particularly, working alliances between SETs and students with EBDs.


While the lack of other significant pathways might be explained in various ways (e.g., statistical power), it is possible that these pathways just do not exist in this particular educational context. SETs in self-contained classroom settings may have different experiences with emotional labor than individuals in other settings, including teachers in inclusive educational settings. In this sample, the significant pathway runs from NDRs (e.g., do not show anger), to SA (e.g., hiding anger), to a better working alliance with students around tasks. In this setting, where the student population is made up of youth who have been removed from other environments after other support options were exhausted, negative emotions in response to challenging situations may abound. Likewise, SETs’ previous training and/or continued professional development may endorse the need for SA when teaching students with EBDs. Tamping down these emotions for the sake of preserving control during student escalations may be a prevalent emotional task that SETs working in self-contained classrooms are asked to perform. It makes sense, then, that those who more strongly perceive this type of expectation (that certain emotions should be suppressed) are more likely to engage in such suppression.


Other pathways for which we did not find evidence, such as one in which perceptions of positive display rules (PDRs; e.g., show excitement) are met by EA (e.g., feigning excitement though SA) and contribute to improved working alliances, may simply not occur in this setting. Though SETs in our sample did, on average, perceive PDRs to a similar extent that they perceived NDRs, the correlation between perceiving PDRs and the reported use of SA was quite weak. Future research in special education settings, and particularly for SETs working with students with EBDs in self-contained classrooms, should work to unpack the unique emotional labor that may be taking place here by attending to the emotional experiences that SETs may have, the preparation that SETs receive around how to manage the emotional labor of their work, and the unique expectations that they may perceive surrounding their emotional displays at work. These insignificant findings actually provide new insight into emotional labor research and how our understanding of emotional labor may contextualize differently not only across different helping professions but also within the teaching profession itself.


LIMITATIONS


Although these findings highlight nuances for the field of special education, some limitations related to measurement and generalizability of the findings to other special education contexts are noted. First, the working alliance task and bond subscales were strongly correlated (see Table 2).  However, a single working alliance composite obscures the unique variability of each subscale. To navigate these challenges, we included all three subscales in the model and used a two-level model that accounts for some of the overlap in the WAI measures. By estimating the path model with all three WAI measures simultaneously and allowing the measures to correlate, the analysis accounts for shared variance and some potential issues of measurement error, especially given that all the variables were self-reported measures (T. D. Brown, 2015).


Another consideration is the conceptualization and measurement of emotional display rules (EDRs) and emotional acting (EA). The mediational analyses presented in this study provide a much-needed variable-centered approach to measuring these constructs with SETs. However, both EA and EDRs are highly nuanced phenomena that might benefit from a more detailed, person-centered, ethnographic measurement approach. Observing classrooms and interviewing SETs might provide a more thorough understanding of how educators experience these constructs.


Finally, though we were able to account for the nesting of students within educators, we did not account for the nesting of educators within schools or for the possibility that multiple SETs might share a classroom (and thus influence each other’s perceptions of EDRs). Our sample size of 61 educators and 243 students was insufficient to examine three-level models. Studies with greater sample sizes are needed to investigate more complex multilevel models that also include factors such as educators’ roles, grade level, and students’ diagnoses. Still, although our sample size was relatively small, it met the minimum recommended number of units at level 2 (i.e., educators), and the large ICCs calculated for the WAI outcomes required us to account for the nested nature of the data on two levels (Maas & Hox, 2005; Snijders & Bosker, 1999).


DIRECTIONS FOR FUTURE RESEARCH


Given the nuance of this work in special education, future research could include measurement strategies that tease apart situation-specific display rules related to different emotions. For instance, this work could begin by conducting an in-depth qualitative exploration with SETs to unpack how they become aware of specific emotional display rules (EDRs), how these rules and their training influence their emotional acting (EA) strategies, and how they believe their EA influences their alliances with students with EBDs. Future qualitative study could also examine how a school’s culture and milieu might contribute to educators’ unique (or shared) perceptions of display rules and choices of EA strategies. For example, research on professional social networks shows that the relationships between individuals within a work group influence the behavior and performance of both the individuals and employees (Daly, 2010). Interviewing educators would allow researchers to identify how these school-based collaborations might alter perceptions of existing display rules and influence EA behavior.


Similarly, observational and ethnographic techniques could add to this literature base by expanding our working knowledge of these constructs in different ways. First, observing SETs’ classroom behavior would add further validity to self-report data and would also allow researchers to more accurately describe EA behavior in schools. Second, ethnographic studies could also provide more context that cannot be captured via variable-centered analyses. For example, researchers can review school documentation (employee handbook, training materials, and so on) and physical spaces (teacher’s lounge, main office) for concrete evidence of EDRs communication and expectations.


IMPLICATIONS FOR PRACTICE AND POLICY


Several implications for practice and policy stem from these findings. First, these studies confirm recent research (E. L. Brown et al., 2015, 2017; R. T. Lee & Brotheridge, 2013) showing that educators engage in emotional acting (EA), and that some dimensions of this acting contribute to their relationships with students. Recent research into SETs’ emotional labor describes the lack of explicit emotional display rules (EDRs) in special education classrooms (Kerr & Brown, 2015; E. L. Brown et al., 2014). Beyond one study noting how the perceptions of EDRs is related to early childhood educators’ EA and teacher-student interactions (E. L. Brown et al., 2017), this is the first investigation examining this connection with SETs and students with EBDs. Our findings may also suggest that surface acting (SA) for SETs is an acceptable EA strategy that supports their relationships with students. Because the emotional labor research in special education has yet to extrapolate on what display rules lead to the EA strategies that the organization desires, how we make these rules more explicit could help teachers establish more sensibility regarding this area of their job. By providing dialogic opportunities in schools, SETs may find safe space to discuss the emotional complexities of their job, while also situating those experiences within organizational expectations to support working alliances.


Further, socioemotional curriculum that reviews connections to educators’ emotions and how displays of their emotions inform their interactions could prepare future SETs to navigate and manage their feelings when emotionally charged interactions occur. With more stringent accreditation policies for teacher education programs (U.S. Department of Education, 2014), many preparation programs struggle to weave socioemotional course content into their curriculum. However, findings here support the investment of designing professional development that helps SETs acknowledge, identify, and evaluate their performances of EA, and how their EA informs their working alliances with students with EBDs. Further, aspects of this professional development could promote SETs’ understanding of different forms of EA and also provide opportunities for educators to deduce when forms of EA may enhance healthier interactions with students with EBDs.


Finally, these findings underscore how SETs’ EA could benefit from ongoing observation and supervision when working with children with EBDs. Research shows that teachers of students with exceptionalities experience higher rates of stress, burnout, and dropout, which can be related to expressions of negative emotional affect (Banks & Necco, 1990; Hamama, Ronen, Shachar, & Rosenbaum, 2012). Further, prior research on SETs’ emotional labor speaks to how some special educators see their knowledge and execution of SA as a “survival mechanism” (Kerr & Brown, 2015, p. 8). Through supervising SETs on the EA performed in classrooms, we acknowledge the emotional underpinnings involved in working alliances and present professional approaches for managing those emotional exchanges. We allow SETs to examine how the use of SA serves as a tool in their toolbox for relationship building and instruction, rather than a hindrance to their professional delivery.



Notes


1. AIC = Akaike information criterion; BIC = Bayesian information criterion.


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Cite This Article as: Teachers College Record Volume 121 Number 7, 2019, p. 1-24
https://www.tcrecord.org ID Number: 22691, Date Accessed: 10/21/2021 2:45:27 PM

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About the Author
  • Michael Valenti
    University of Pittsburgh
    E-mail Author
    MICHAEL VALENTI has a doctorate in applied developmental psychology from the University of Pittsburgh and is the senior research coordinator in the Organizational Performance Department at Pressley Ridge. His current practical work includes program evaluation in the nonprofit sector, establishing quality improvement frameworks for systems of care, and coordinating grant activities. Michael also designs and leads several ongoing, longitudinal research projects. His research interests include effective behavioral assessment/planning, staff and client perceptions of the working alliance, and examining emotional labor and collaboration in special education settings.
  • Elizabeth Brown
    George Mason University
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    ELIZABETH LEVINE BROWN has been an assistant professor in the College of Education and Human Development (CEHD) at George Mason University since August 2011. Grounded in an ecological systems perspective, Brown's research and scholarship focuses on developmental (i.e., social and emotional) and psychosocial influences on learning for marginalized children across PreK–12 schooling. Specifically, her agenda investigates the developmental and psychosocial competencies (e.g., socioemotional, school mental health) of teachers and students that shape children and youth’s developmental and academic outcomes over time. With more than 40% of U.S. children living in low-income families, coupled with staggering turnover rates in education, Brown's research centers on exploring these concepts among diverse, low-income communities, particularly in our most vulnerable schools. Dr. Brown affiliates with elementary education (ELED) and human development and family science (HDFS) programs and teaches courses in child development, curriculum, foundations, research methods, and individual and family development.
  • Christy Galletta Horner
    Bowling Green State University
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    CHRISTY GALLETTA HORNER is an assistant professor of assessment, research, and applied statistics at Bowling Green State University. She earned a PhD at the University of Pittsburgh, where she studied applied developmental psychology and research methods in the School of Education. Her research focuses on the role of emotional culture in the promotion of healthy individual and social functioning. Viewing emotions as sociocultural in nature, she prioritizes youths’ perspectives while also seeking to uncover quantifiable links between emotion-related constructs and developmental outcomes. She often uses mixed-methods designs and creative methodological approaches to address the challenges involved in this line of inquiry. Her aim is to find ways that emotional transactions can be leveraged in settings such as schools, after-school programs, and social media sites to help youth thrive in their environments.
  • Duhita Mahatmya
    University of Iowa
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    DUHITA MAHATMYA received her BS in psychology from Drake University and her MS and PhD in human development and family studies from Iowa State University. She is currently an assistant research scientist in the College of Education at the University of Iowa. Her research examines how family, school, and community environments shape the attainment of developmental milestones from early childhood to young adulthood.
  • Jason Colditz
    University of Pittsburgh
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    JASON COLDITZ is a PhD scholar in clinical and translational science at the University of Pittsburgh. He completed his MEd in social and comparative analysis in the University of Pittsburgh’s Department of Administrative and Policy Studies. Jason currently works in Pitt’s School of Medicine as a program coordinator at the Center for Research on Media, Technology, and Health. In this role, he directs interdisciplinary efforts in analyzing health-related data from the Twitter social media platform and manages Pittsburgh Supercomputing Center resources for the Center. He is also active in local nonprofit organizations that focus on community-centric revitalization and evidence-based behavioral health care.
 
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