Abstract
The student cohort model is one in which a group of students begin an academic program together, progress through the program as a group while creating a distinct group culture, and finish the program around the same time (Hubbell & Hubbell, 2010; Lei et al., 2011). This model is widely used in occupational therapy programs and in health care education programs in general. The cohort experience, however, does not always produce uniformly beneficial results. Studies have had mixed results; cohorts have the ability to create either a positive or a negative culture that can influence the student educational experience (Lewis et al., 2010). Although the cohort model has multiple benefits for its members, such as increased social support (Seifert & Mandzuk, 2006), cohesiveness, student satisfaction, and feelings of purpose and affiliation (Greenlee & Karanxha, 2010), these results are not guaranteed.
When dysfunctional, the cohort model can produce negative outcomes that can include interpersonal conflict among members (Lewis et al., 2010) and the creation of negative norms regarding what is acceptable behavior within the group (Mandzuk et al., 2005). The difficulty for educational administrators and faculty who work with the cohort model is that the positive or negative outcomes of using the cohort model can be difficult to predict (Bista & Cox, 2014). Increased research is needed to better understand what variables influence a cohort’s development of either a positive or a negative culture.
In particular, a theoretical lens is needed to better understand cohort variability and its relationship to the strong sense of cohesion among cohort members (Greenlee & Karanxha, 2010) while accounting for research that has suggested that the primary consequence of the student cohort model is affective (Scribner & Donaldson, 2001). An explanatory model that accounts for unpredictable affective consequences at the group level is needed to gain insight into the variable outcomes associated with the student cohort model.
Group affective tone, which was originally described as “consistent or homogeneous affective reactions within a group” (George, 1990, p. 108), is the phenomenon by which group members’ affect becomes increasingly congruent over time to the level at which a distinct group-level affective identity is created. This affective identity can have either positive or negative consequences at the group and organizational levels. Positive group affective tone has been associated with decreased absenteeism (George, 1990) and improved cooperation and decreased conflict (Barsade, 2002). Negative group affective tone has been linked to dysfunctional team behavior and decreased performance (Cole et al., 2008). The occurrence and ramifications of group affective tone have been studied across a wide variety of laboratory and field conditions (Klep et al., 2013; Sy et al., 2005; Tanghe et al., 2010), although group affective tone has not to this point been explored in the context of the student cohort model. Examining the student cohort model through the lens of group affective tone may provide greater insight into how cohorts form a positive or a negative culture.
An examination of variables that may influence the formation of group affective tone would provide an even greater level of understanding of the mechanisms behind the formation of cohort culture. A theoretical model that has been used to explain the phenomenon of group affective tone is emotional contagion theory (Hatfield et al., 1994). This theory describes how emotions are passed from one person to another through a process of emotional synchronization leading to a congruent emotional state. Emotional contagion has been shown to occur within complex group-level dynamics (Dezecache et al., 2013), and it has been posited to influence the formation of group affective tone (Collins et al., 2013). This link is important because research has found that people have varied degrees of susceptibility to emotional contagion (Bhullar, 2012) and varied degrees of emotional expressivity, which has been shown to be associated with the transmission of emotional contagion (Sy et al., 2013). Taken together, research on the influence of cohort members’ affect and susceptibility to emotional contagion and on the effect of emotional contagion on the formation of group affective tone may provide insight into the variable nature of student cohorts’ positive or negative affective identity.
The purpose of this study was to examine whether student cohorts in university health care programs, including occupational therapy assistant student cohorts, develop group affective tone. Emotional contagion theory was also used to examine the possible influences of susceptibility to emotional contagion and transmission of emotional contagion on the strength of affective convergence (i.e., degree of affective similarity) within cohorts. The study addressed the following three research questions:
Does affective convergence occur within student cohorts and form group affective tone?
Does susceptibility to emotional contagion positively correlate with the strength of affective convergence among cohort members?
Does transmission of emotional contagion positively correlate with the strength of affective convergence among cohort members?
Method
I used a nonrandomized cross-sectional survey design that allowed for participant grouping at the cohort level and for group-level analysis.
Participants
The population of interest was university student cohorts in undergraduate programs, including the occupational therapy assistant program. The participants were a convenience sample of students from a private, nonprofit multicampus university system enrolled in a primarily on-campus undergraduate health care program (operationalized as a program of study with ≤25% online course delivery) that used a student cohort model. The target cohort size varied on the basis of the number of students per cohort and rate of participation, although a minimum of three cohort members per existing cohort were required to run the required statistical analysis (Chi et al., 2013; Sy et al., 2005; Tanghe et al., 2010; Volmer, 2012).
Measures
Positive and Negative Affect Schedule.
The Positive and Negative Affect Schedule (PANAS; Watson et al., 1988), was used to measure the study participants’ affect. The PANAS assesses people on two independent dimensions: positive affect (PA) and negative affect (NA). The PA scale consists of 10 items that measure a person’s PA, or “the extent to which a person feels enthusiastic, active, and alert. High PA is a state of high energy, full concentration, and pleasurable engagement, whereas low PA is characterized by sadness and lethargy” (Watson et al., 1988, p. 1063). The NA scale consists of 10 items that measure a person’s NA, or the “general dimension of subjective distress and unpleasurable engagement that subsumes a variety of aversive mood states, including anger, contempt, disgust, guilt, fear, and nervousness, with low NA being a state of calmness and serenity” (Watson et al., 1988, p. 1063). The PANAS has demonstrated strong reliability (Cronbach’s α = .86–.90 for the PA scale and .84–.87 for the NA scale) and factorial validity (PA scale, between .89 and .95; NA scale, between .91 and .93). The PANAS has been used as a measure of affect in testing for emotional contagion across multiple studies (Bhullar, 2012; Du et al., 2011; Ilies et al., 2007).
Emotional Contagion Scale.
The Emotional Contagion Scale (Doherty, 1997) is a 15-item scale that measures a person’s susceptibility to emotional contagion across five subscales: Love, Happiness, Fear, Anger, and Sadness. The scale, which contains questions such as “If someone I’m talking to begins to cry, I get teary eyed” and “I tense when overhearing an angry quarrel” (Doherty, 1997, p. 136), has been shown to have significant internal reliability (Cronbach's α = .90), and its construct validity has been confirmed across multiple established and relevant measures.
Emotional Expressivity Scale.
The Emotional Expressivity Scale (Kring et al., 1994) is a 17-item scale that measures emotional expressivity, or the “individual differences in the extent to which people outwardly display emotions” (p. 934). The scale contains statements such as “People can read my emotions” and “Even if I am feeling very emotional, I don’t let others see my feelings” (Kring et al., 1994, p. 938). The scale has been proven to be a reliable measure of emotional expressivity (Cronbach’s α = .91; 4-wk test–retest reliability = .90), and it has demonstrated both convergent and discriminant validity (Kring et al., 1994).
Data Analysis
I used both group-level and correlational analysis. IBM SPSS Statistics (Version 24; IBM Corp., Armonk, NY) was used for statistical analysis. Testing for the formation of group affective tone was completed by analyzing cohort-level student affect operationalized by the PANAS (Watson et al., 1988). The preferred statistical method for measuring group affective tone is a combination of statistical analyses (triangulation) to assess interrater agreement (r wg) and interrater reliability, as measured by intraclass correlation coefficient (ICC; Collins et al., 2013; Du et al., 2011; Sy et al., 2005). An r wg analysis assesses the level of consensus among cohort members’ affect through an estimation of the relative interchangeability of cohort members’ scores (LeBreton & Senter, 2008). ICC(1) is a measure of consistency that compares one rater’s scores, randomly selected from the population of raters, with the mean score of all raters (Bliese, 2000; LeBreton & Senter, 2008). ICC(2) examines whether the mean rating of the group of raters is reliable (Bliese, 2000; LeBreton & Senter, 2008).
I used Pearson correlation analysis to assess the variables of susceptibility to emotional contagion and transmission of emotional contagion and their relationship to affective convergence among cohort members. The average deviation (AD) index was used to measure group-level affective convergence within the cohorts. This method, established by Tanghe et al. (2010), measures “the extent to which group members are in agreement with other group members regarding their affective states” (p. 346).
Results
Surveys were distributed to 56 health care cohorts across 20 academic programs at 10 campuses located in urban centers. One hundred ninety-six completed surveys were returned across 47 cohorts. Of this initial number, 24 cohorts ranging in size from 3 to 18 respondents (N = 159; mean cohort size = 6.58) met the minimum number of participants required for aggregation (Table 1).
Demographic Characteristics of the Sample (N = 159)
Statistical analysis supported the formation of positive group affective tone: r wg = .90, which exceeds the benchmark level of r wg > .70 recommended to justify aggregation (LeBreton & Senter, 2008). A one-way random-effects analysis of variance (ANOVA) was completed, F(23, 135) = 2.87, p = .00, to calculate both ICC(1) and ICC(2), following the protocol established by Bliese (2000). ICC(1) = .22, which demonstrates a large effect size (LeBreton & Senter, 2008) and indicates that 22% of the variability in cohort members’ affect scores could be explained by cohort membership (Bliese, 2000). This is significant because ICC(1) values >.30 are relatively rare in field research (Bliese, 2000). The interpretation of ICC(2) is less well defined. The results (ICC[2] = .65) fall just short of one accepted threshold of .70 (Collins et al., 2013); however, researchers have noted that decisions to aggregate should be influenced by high r wg values and significant ICC(1) scores (Chen & Bliese, 2002). This wholistic conceptualization of aggregation to a group-level construct has been used in multiple studies (Chi & Huang, 2014; Chi et al., 2013; Cole et al., 2008; Collins et al., 2016; Ilies et al., 2007; Tanghe et al., 2010), and this study’s ICC(2) is higher than those of the aforementioned studies. Taken together (r wg = .90, ICC[1] = .22, ICC[2] = .65), the analysis justified the aggregation of the individual participants to the group level and supported the existence of cohorts’ forming a positive group affective tone. The formation of negative group affective tone was not supported because the ANOVA used to measure ICC did not meet the level of statistical significance, F(23, 135) = 0.84, p = .68.
Subsequent analysis examined how the traits of susceptibility to emotional contagion and emotional expressivity influenced the strength of affective convergence among cohort members operationalized through the use of the AD index. An inverse relationship would be expected if the hypotheses were confirmed because higher AD numbers would indicate a higher level of affective dispersion, and, per emotional contagion theory, susceptibility to emotional contagion and emotional expressivity should decrease the level of dispersion of cohort affect scores.
On initial analysis, a nonsignificant relationship was found between level of susceptibility to emotional contagion and average deviation of affect scores (r = −.08, p = .70) and between emotional expressivity and average deviation of affect scores (r = .07, p = .73). Additional analysis was subsequently completed of the relationship of susceptibility to emotional contagion and affective convergence because three of the subscales (Love, Fear, and Sadness) had limited relevance to the study of affect in the context of university cohort membership. Subsequently, the Happiness and Anger subscales were analyzed independently because these emotions were more congruent with descriptions of cohort emotions found in the literature (Lei et al., 2011; Lewis et al., 2010). Susceptibility to happy emotions had no statistically significant relationship to emotional convergence in cohorts (r = −.03, p = .88). Susceptibility to anger, however, had a significant correlation with emotional convergence within cohorts (r = −.49, p = .02; Table 2). This finding was congruent with emotional contagion theory’s premise that susceptibility to emotional contagion contributes to the convergence of affect (Hatfield et al., 1994).
Correlations Between Susceptibility to Emotional Contagion, Emotional Expressivity, and Affective Convergence
Note. AD = average deviation.
p < .05.
Discussion
These results suggest that student cohorts form a positive group affective tone. Positive affect, as conceptualized by Watson et al. (1988), represents a range of affective states. High levels of positive affect represent emotional states such as enthusiastic, inspired, active, and alert. Low levels of positive affect, described by Watson et al. (1988) as a state of lethargy and sadness, are the same affective states experienced at a distinctly lower level. Evidence for the development of a group affective tone suggests that cohorts develop congruent affective identities clustered around the unique emotional characteristics of positive affect. Positive affect as conceptualized by Watson et al. has 10 distinct emotional characteristics, and the formation of positive affective tone suggests that each cohort converges around a distinctive pattern of higher and lower levels of these 10 affective attributes.
Evidence for the development of a positive group affective tone may help explain why cohorts develop distinct affective identities. Student cohorts that are supportive, familial, and collaborative (Bista & Cox, 2014; Greenlee & Karanxha, 2010) may have developed a high positive group affective tone. Likewise, cohorts that have maladaptive attitudes and behaviors such as intentional avoidance of active participation (Jaffee, 2007) may have developed a group affective tone around low levels of positive affect.
The finding that cohorts develop a positive affective tone has implications for the understanding of cohort dynamics. The literature supports the conclusion that cohorts have unique cultures or identities (Lei et al., 2011; Lewis et al., 2010), although the research on why this occurs is limited. The evidence that cohorts develop a group affective tone suggests that cohort identities may be the result of unique affective clusters of high or low positive affect. Cohorts that are functional and supportive of the academic experience may have high positive affective tone, whereas cohorts that are negative or maladaptive may have low positive affective tone. This is significant because past research has shown that group affective tone forms independently of either favorable or adverse events occurring proximal to the group (Totterdell, 2000; Totterdell et al., 1998). The independence of group affective tone from proximal events suggests that the unique affective identity of a cohort may be a phenomenon in itself and not a by-product of the positive or negative experiences that have occurred within the cohort. Group affective tone’s independence from external events suggests that interventions used by faculty or program administrators to influence a cohort’s culture may need to directly address the cohort’s affect instead of creating proximal events in the hopes of influencing a positive shift within the group.
Whether affective interventions could be designed on the basis of a cohort’s unique affective identity remains to be seen. For example, would a cohort with low levels of certain aspects of positive affect (such as low levels of alertness and excitement) respond better to a different approach than a cohort demonstrating a different pattern of low affective tone (such as low levels of pride and enthusiasm)? Cohorts with a low level of alertness and excitement, for example, may receive greater benefit from a focus on classroom engagement strategies, such as the use of interactive quiz software or competitions, than cohorts who have a high level of alertness and excitement yet lower levels in other areas.
Susceptibility to emotional contagion also correlated positively with affective convergence of cohort members. Specifically, a cohort’s affect converged when that cohort had a higher level of susceptibility to the emotion of anger. It is unclear why susceptibility to this particular emotion influenced group convergence and other emotions did not, although different possible explanations exist. Anger has been shown to be a powerful emotion in regard to emotional contagion (Lewis, 2000). It is possible that being susceptible to anger is a stronger driver of convergence than susceptibility to less valent emotions. Also, Kelly et al. (2016) found that anger contagion occurred during both high and low cognitive load conditions, whereas happiness contagion only occurred during low cognitive load conditions. This is relevant because higher education would represent a context of high cognitive load.
The implication of this finding is that anger within a cohort could have significantly more influence on the formation of a cohort’s affective identity than more positive emotions. Faculty or administrators who attempt to manage an angry cohort by being purposefully positive or happy may not experience success because anger will be subsumed to a greater degree than competing positive emotions. This finding suggests that the cohort member or members who are emitting angry emotions (i.e., the source of the anger) may need to be managed directly to limit their influence on the rest of the cohort.
The lack of correlation between emotional expressivity and affective convergence may be explained in part by the secondary nature of the trait. Whereas susceptibility to emotional contagion directly leads to an affective shift (subsuming the emotions of others), emotionally expressive cohort members still need another member to be susceptible to that emotion for emotional convergence to occur. Prior research has shown that susceptibility to emotional contagion and transmission of emotional contagion are both active in the process of emotional contagion. Cheng et al. (2012) found that transmission of emotional contagion and susceptibility to emotional contagion were both necessary for emotional contagion to occur, although the current study was not able to demonstrate this relationship.
This study had limitations. First, although the number of respondents was quite high, the usable number of cohorts was smaller (N = 24). A larger number of cohorts in the analysis might have increased the sensitivity of the correlational analysis. Moreover, the study used a representative population from a cohort, as opposed to the entire population of the cohort. Representative populations have been used in research on the formation of group affective tone (Chi et al., 2013; Cole et al., 2008; Mason & Griffin, 2005), but surveying the entire cohort would have been ideal. Finally, cohort membership was limited to the student members of the cohort and did not take into account faculty member influence on group affect. Faculty members and their position of relative power may have influenced the formation of cohort affect, and this effect warrants future consideration.
Implications for Occupational Therapy Education
The results of this study have the following implications for occupational therapy education:
Cohorts develop unique group-level affective identities that are independent of external events and nonlinear in their formation (anger is more influential than happiness in group affective convergence). Educators may need to manage the source of anger directly and promptly instead of attempting to counteract its effect with either a countervailing positive affect or by creating proximal events.
Students could be made aware of the affective nature of cohorts at the beginning of the cohort experience. A student orientation that includes education on the affective nature of cohorts and how group-level emotions influence the student experience may provide students with a degree of meta-awareness of how their expressed emotions affect group identity.
Conclusion
The results of this study suggest that cohort affective identities may be influenced by the process of emotional contagion; specifically, the influence of cohort members’ susceptibility to the emotion of anger may be an active contributor to the formation of a cohort’s unique group affective tone. Further exploration of this phenomenon is warranted to better understand the development of group-level affect within cohorts and its implications for the educational process and student experience.