Psychosocial change across a drum corps season

Abstract

Drum corps is a marching arts (MA) activity that combines movement, music, and visual performance. Education in the MA emphasizes performance, endurance, and group cohesion. However, research on the psychosocial effects of participation in MA education is rare. In the current study, we measured resilience, self-efficacy, goal orientation, and flow, over the course of a season of drum corps, hypothesizing significant change in these constructs. A total of 74 young men (M_age = 19.16) from a world-class drum corps participated in an online longitudinal study involving five surveys with repeated measures across their 2017 competitive marching season. We found a statistically significant curvilinear change over time in general self-efficacy, marching self-efficacy, mastery goal orientation, and flow, but no pattern of change in ego goal orientation. The pattern of change was similar for all outcomes: participants first declined, and then rebounded up, but only some outcomes returned to baseline or higher levels. Findings may inform MA participants and educators about dynamic psychosocial change throughout a season for which they should be prepared, as well as future research on MA.

Keywords

marching arts marching music drum corps self-efficacy motivation flow resilience

The marching arts (MA) are exceptional among musical activities, as the members hold more performance responsibilities than stationary musicians. The MA not only require musical responsibilities such as intonation, musical phrasing, and acoustic timing, but also include additional visual design considerations, such as drill spots (placement on the field), steps sizes, and choreography that are beyond the scope of stationary music performances (Dagaz, 2012). This differs from other highly physical forms of musical performance such as musical theater performers, rock singers, or drumming, because it requires the playing of an instrument over the use of one’s voice or seated playing. Musical theater, fronting a band, or drumming all can require movement, choreography, or even some level of synchrony. However, marching and playing is definitionally foundational to the MA, and is a new coordination skill that must be learned and developed much later in one’s lifetime after learning their instrument primarily stationary.

MA include indoor color guard, indoor percussion, indoor winds, outdoor drum corps, and outdoor marching band. Aside from indoor color guard, activities housed within the MA are dedicated to ensemble music production in tandem with visual performance. MA circuits vary by the size of the organization (i.e., how many groups participate), geographic span (i.e., region from which groups are allowed to participate), scope (i.e., the classes of groups participating), and notoriety based on prestige. Available estimates from major (i.e., largest, most reputable, largest scoped) competitive MA circuits based in the United States report serving approximately 450,000 high school music students during the fall marching season and 33,000 students during the winter marching season in the United States alone (Bands of America [BOA], n.d.; Winter Guard International [WGI], n.d.). The largest circuit also provides workshops and broadcasts of marching events reaching approximately 7.2 million people between the ages of 13 and 22 years worldwide (About Drum Corps International [DCI], Marching Music’s Major LeagueTM, n.d.). However, the MA empirical literature is small, and has been almost completely ignored by the music and psychology research communities, despite involving these large numbers of music students in the United States.

Though some research has suggested change in MA participants over the course of the season, much more research is needed to draw informed conclusions. As such, this study examines psychosocial change (i.e., psychological changes that are innately related to time and the social experiences within a MA program). MA are interesting because they combine the skills needed for music (e.g., memorization, fine motor coordination, music literacy) and the skills needed for sport (e.g., stamina, gross motor coordination, high aerobic activity) and coordinate them over a large space, across many individuals. While there are several other high-energy musical performance types that may include musicians who move and perform music, those typically have a focal performer who holds all the responsibilities typical of a marching member and several other performers that focus on a single aspect (i.e., visual or musical) of the performance. Also, the moving/performing ensemble in these performances is typically much smaller than a marching band. While the size of a marching band may be small due to the size of the music program, they can span up to 400 performers synchronously navigating simultaneously their musical and visual responsibilities.

Considering the combination of skills needed in MA, it is likely the psychosocial effects of engaging in the MA cross the boundaries of music and sport, including commonly researched constructs such as motivation, self-efficacy, resilience, and flow. Researchers have found the psychosocial experiences of musicians and athletes are similar in some dimensions and significantly related to well-being and health (e.g., Côté-Leclerc et al., 2017; Fancourt et al., 2016; Miksza et al., 2016; Nathan et al., 2013). For example, professional musicians and athletes report comparable amounts of flow (Habe et al., 2019), which has been related to more life satisfaction in music students and more mental toughness in athletes (Fritz & Avsec, 2007; Jackman et al., 2016). MA, which is a combination music and sport, therefore holds potential to benefit participants psychosocially.

The most studied MA activity is marching band (Carter, 2013; Dagaz, 2012; Matthews, 2017; Silveira & Hudson, 2015; Weren et al., 2016). Marching band combines movement, music, and visual performance either in parades or on a football field, usually connected to a sports team, military group, or educational institution (Dagaz, 2012). Marchers perform a show with precision and synchrony to the music they are playing, each other, and the shapes they make on the field (called drill) to maximize musical, visual, and aesthetic effect for the audience. Drum corps, while similar to marching bands, are independent. Drum corps are self-governed marching programs overseen by circuit organizations (e.g., Drum Corps International (DCI) or Drum Corps Associates) that facilitate competition and adjudication. Members of drum corps audition, and if accepted, pay to learn, compete, and perform a marching show designed for their ensemble. While drum corps and marching bands share fundamental qualities such as technique and rehearsal organization, instrumentation is distinct. A drum corps employs four sections: the horn line, composed of valved brass instruments; the drum line, composed of marching percussion instruments; the front ensemble, composed of non-marching percussion instruments; the color guard, composed of members dancing and spinning various pieces of equipment (e.g., flags, wooden rifles, mock sabers). Instead of marching only valved brass instruments, typical marching bands field woodwinds. The marching season comprises two components; a training season (e.g., band camp or spring training) and also a competitive and/or performance season. The group will rehearse and only focus on training for the training season. Once performance/competition season begins, the group rehearses less and gains performing as an additional obligation. In world-class drum corps, the corps rehearses together in a live-in location over the course of weeks, then begin touring the country rehearsing during the day, preforming during the evening, and traveling through the night.

The empirical literature on the psychological aspects of drum corps participation is virtually non-existent. Zdzinski (2004) surveyed drum corps alumni, collecting both quantitative and qualitative data examining which quality of life domains they attributed to their experiences in drum corps. The largest percentages of the alumni participants responded that drum corps impacted their work ethic, self-discipline, people skills, personal accountability, and self- confidence. Two additional studies examined drum corps in relation to personality and performance anxiety, finding that drum corps participants have average or lower performance anxiety compared with musicians from the same age group as well as slightly above average levels of Agreeableness, Conscientiousness, Extraversion, and Openness. Conscientiousness, Emotional Stability, and Extraversion were significant positive predictors of drum corps satisfaction, while performance anxiety was a significant negative predictor (Levy et al., 2011; Levy & Lounsbury, 2011). However, both of these studies are correlational, and do not provide a full picture of student engagement in a drum corps season.

While also small, there is more research on marching band. Previous sociological and psychological research on marching band has utilized qualitative and program evaluation methods, finding consistent qualitative evidence suggesting marching band activities foster members’ social and personal growth, specifically trust among teammates, feelings of acceptance, and self-confidence (Dagaz, 2012; Matthews, 2017). Given their close organizational and engagement similarities, marching band findings can help inform drum corps research.

Marching band experiences

To investigate the dynamic changes and experiences of a marching band season, Dagaz (2012) observed and conducted interviews at two Midwestern high school competitive marching bands over their 4-month season, focusing on physical and emotional demands of participation. Students spent an extraordinary amount of time working during the season: 90 hours of rehearsal in the 2-week band camp period, 3 hour nightly rehearsals, and 13 hour days of competition and game performances. Several themes emerged from participant interviews, including feelings of commitment, trust, acceptance, and self-confidence. The band’s need for every person on the field to be coordinated, synchronous, and cohesive to be successful was described as a mechanism through which participants developed feelings of acceptance and inclusion (Dagaz, 2012).

In a mixed-methods longitudinal study of the marching band season, Matthews (2017) asked focus groups consisting of band members about their participation, and also surveyed them about feelings of group cohesion and collective efficacy over time. Participants identified band-community connection, band as family (i.e., creation of bonds that exceed friendship in strength and quality), and acceptance (i.e., embracing diversity of identities) as central social themes, expressing only one source of strain on ensemble collective morale: time. Over three time points of survey data collection, participants reported a decrease from beginning to middle of the season, followed by an increase at the end of the season (Time 3) on both collective efficacy and task related group cohesion. Participants reported significantly higher group cohesion at the end of the season (Time 3) than at the beginning (Time 1).

In an analysis of how marching band motivation is affected by the network of members’ friendships, Weren et al. (2016) measured various types of motivation and friend nominations (where all participants name their friends, creating a modeled network of connectedness in the band). They found that while intrinsic motivation decreased between the beginning and end of the season, reciprocated friendships (i.e., friendship nominations where both participants nominated each other) within the band were found to be positively correlated with individual levels of social, intrinsic, and extrinsic motivation at both time points. Important to note, however, is that specific characteristics of bands such as racial climate and hazing culture (Carter, 2013; Silveira & Hudson, 2015), may affect participant outcomes and intersect with participant identities. For example, qualitative work at a historically Black college with Black, gay, male students found despite being providing community for their racial identity, marching band was less accepting of their sexuality (Carter, 2013). Other work has found that while a majority of participants (> 90%) report never being forced into hazing activities, those who have reveal a culture of students’ believing that hazing (e.g., being yelled at) is not serious enough to report, an apathy about hazing, and hazing as a tradition fostered by their school’s MA environment (Silveira & Hudson, 2015).

Considering the past work in the MA, both drum corps and marching band, there is general consistency across studies to suggest students change over the marching season. Matthews (2017) as well as Weren and colleagues (2016) were able to note change quantitatively. Dagaz (2012) and the qualitative component of Matthews’ (2017) study were able to note that students were growing. They also provide qualitative evidence suggesting the domains in which growth would occur. The present study attempts to extend and expand past findings by measuring the themes that have previously emerged in the literature; motivation, self-confidence (measured as self-efficacy), and commitment (measured as resilience) (Dagaz, 2012; Matthews, 2017; Weren et al., 2016). This study also attempts to measure participants’ engagement and enjoyment of the activity by measuring flow, which has been previously measured in both music and sports contexts (Habe et al., 2019).

Taken together, MA have been identified as related to students’ motivation, enjoyment, and feelings of inclusion within the school environment, although these experiences may be dependent on the specific marching context. These findings, however, did not investigate participant’s perceptions of their own psychosocial health, nor have they looked at participation across more than three time points. Little research has quantitatively investigated dynamics of social and personal growth through MA participation. In the current study, therefore, we extend previous research on MA to drum corps, and quantitatively assess constructs that have emerged in the qualitative literature by testing patterns of change over multiple (i.e., five) times during a drum corps season.

The current study

This study builds on and extends past research to understand the degree of psychosocial growth drum corps participants experienced over the course of the season. Our work is novel because it uses a five–time point repeated measures design including established quantitative self- report survey instruments to closely examine trajectories of personal perceived change in self- efficacy (marching and general), resilience, goal motivation (ego and mastery), and flow across a MA season. These outcomes are connected to important non-musical consequences such as motivation and school/work performance (Cerasoli et al., 2014); self-efficacy as protective against burnout (Shoji et al., 2016); resilience and better mental health indicators (Hu et al., 2015); flow and better performance, less performance anxiety, and greater well-being (Cohen & Bodner, 2019; Fritz & Avsec, 2007; Iusca, 2015).

Considering the current literature, it was unclear if the change would be experienced linearly (straight growth) or curvilinearly (curved growth) across the course of the season. We hypothesized that participants would grow over the course of the season and assessed for both linear and curved growth over time. By collecting data at program transitions, we could contextualize the change as related to transitions within the drum corps season.

Methods

We collected longitudinal survey data with members of the Madison Scouts Drum and Bugle Corps during their 2017 competitive season.

The 2017 Madison Scouts

The Madison Scouts Drum and Bugle Corps are a world class (top competitive tier) drum corps that belongs to the DCI circuit. At the time of data collection, the Madison Scouts only included men aged 15–22 years old, but has recently opened up to include women in the corps. In their 2017 season, the Madison Scouts were just coming back from their failure to make DCI World Championship Finals in 2016. To make finals, a corps must place in the top 12 of all corps in the circuit (DCI Bylaws, Policies, Procedures, n.d.). Their 2016 show, Judas, was the first time that the Madison Scouts had not made DCI World Championship finals since 2009 (Drum Corps International: Official DCI Scores, n.d.). It is likely that, despite the program’s emphasis on student growth and development over competitive success, that the membership of students felt some pressure to make finals that year. Over the course of the 2017 season, the Madison Scouts and the Mandarins scored very close to each other, sometimes the Mandarins beating the Madison Scouts in competition, and others the Madison Scouts beating the Mandarins. Going into DCI World Championship Preliminary Competition, the Madison Scouts were placing above the Mandarins relatively consistently, but the average difference in scores between the Madison Scouts and the Mandarins when they performed at the same show was .89. It was unclear if the Mandarins would beat the Madison Scout during preliminary or semi-finals competition.

It is common for the musical/visual production produced by drum corps to be edited and changed incrementally throughout the course of the season. As such, a distinctive difficulty score for the package is difficult to determine for the overall season. If interested in overall scores, placements, or the content difficulty of the visual or musical book, please see the competitive score sheets at https://www.dci.org/scores.

Participants

Supported by the Madison Scouts Drum and Bugle Corps, we recruited 74 members of the program via a recruitment presentation at the April pre-season rehearsal. Reflecting the composition of the program, all participants were men aged 15–22 years (M = 19.18, SD = 1.29). Institutional review board approval was given by Pace University, and any members under the age of 18 years obtained parental consent and provided self-assent. The sample included 50 (67.6%) White participants, eight (10.8%) multiracial participants, seven (9.5%) Asian/Pacific Islander participants, five (6.8%) Latinx/Hispanic participants, three (4.1%) African American/Black participants, and one (2.7%) participant who did not report their race/ethnicity. The participants varied in level of education given the age range: 39 (52.7%) had some college experience, 24 (32.4 %) had a high school diploma or GED, four (5.4%) had some high school education, four (5.4%) had a 2-year college degree, two (2.7%) had a 4-year college degree, and one (1.4%) did not report education level. Of the participants, 49 (67.1%) were new to the Madison Scouts and 23 (31.5%) were returning members. Of the returning members, they had 1–3 years prior experience with the Scouts (M = 1.41, SD = 0.59). The sample included 45 (61.6%) members from the horn line, 16 (21.9%) members from the color guard, eight (11%) members from the drumline, and four (5.5%) members from the front percussion ensemble. The average number of total years MA experience was 5.39 (SD = 1.68).

Procedure

We collected survey data from members across five time points, using Qualtrics, allowing us to model program relevant trajectories of change for each of the outcomes over the entire course of a single marching season. The Madison Scouts administrative staff posted the first Qualtrics survey link on the member Facebook group after the pre-season rehearsal officially ended. Each participants’ survey was linked together by a unique code created by participants based on a set of instructions for anonymous participation. The first survey was longer (approximately 10 min) as it included participant demographic questions in addition to the main psychosocial measures. Follow-up surveys occurred approximately during the end of first week of spring training, end spring training/beginning of tour, halfway through tour, and at the end of the tour and program. This Madison Scouts marching season began May 26, 2017 and ended August 13, 2017.

Measures

Demographics

Participants reported age, race/ethnicity, gender, sexual identity, education level, and section of the Corps (i.e., Color Guard, Horn Line, Drum Line, or Front Ensemble). Participants also reported prior experience with MA and prior experience with the Madison Scouts.

Personal self-efficacy

The 10-item Personal Efficacy Beliefs Scale (Riggs & Knight, 1994) assesses people’s beliefs about themselves and their ability to perform their job (e.g., “I have confidence in my ability to do my job”). Instructions explained that “job” could be applied to any type of work in their day-to-day life aside from marching (e.g., work, school, chores). All items were responded to on a 6-point scale from 1 (strongly disagree) to 6 (strongly agree). The measure has demonstrated good internal reliability in previous research (α = .86; Riggs & Knight, 1994). In this study, the scale demonstrated good internal reliability at each time point (α = .82, .80, 84, .86, and .88). Mean scores were calculated from participant responses to each item in the measure, with higher scores indicating greater personal self-efficacy.

Marching self-efficacy

The 10-item Personal Efficacy Beliefs Scale (Riggs & Knight, 1994) items were adapted to ask participants about how they feel about their ability to “march” their show (e.g., “I have confidence in my ability to march my show”). All items were responded to on a 6-point scale from one (strongly disagree) to six (strongly agree). This version of the scale demonstrated good internal reliability at each time point in this study (α = .79, .74, .84, .80, and .84). Mean scores were calculated from participant responses to each item in the measure, with higher scores indicating greater marching self-efficacy.

Resilience

The four-item Brief Resilient Coping Scale (Sinclair & Wallston, 2004) assesses one’s ability to cope with negative life stress (e.g., “I actively look for ways to replace the losses I encounter in life”). All items were responded to on a 5-point scale from 1 (does not describe me at all) to 5 (describes me very well). The measure has demonstrated good internal reliability in past research (α = .69; Sinclair & Wallston, 2004). However, in the current study the scale did not have good reliability while including all the questions (α = .48, .52, .64, .38, and .58). This is likely due to the small number of questions in the measure, as the number of items can greatly impact the stability of a measure’s reliability (Vaske et al., 2017). To improve reliability, one item (i.e., “I actively look for ways to replace the losses I encounter in life”) was removed from analyses. After removal, reliability was adequate at most time points (α = .63, .59, .73, .47, and .80), but not ideal. Mean scores were calculated out of the three remaining questions, and higher scores indicate greater resilience. Due to low reliability in resilience, we do not report further analyses regarding resilience in this article. However, to be transparent about all analyses conducted in the study, we report the results for participant resilience in Supplementary Materials online.

Flow

The 26-item Activity Flow State Scale (Payne et al., 2011) assesses the experience of flow. The scale includes nine subscales assessing domains that make up flow, and it can also be treated as a single measure of overall flow (e.g., “I felt just the right amount of challenge”). All items were responded to on a 5-point scale from 1 (strongly disagree) to 5 (strongly agree). Previous research has found the scale to have good internal reliability (α = .70–.90; Payne et al., 2011). In the current study, the scale had good reliability across all time points (α = .88, .84, .87, .92, and .92). Mean scores with all items were calculated, and higher scores indicate greater overall flow.

Motivation

The 12-item Achievement Goal Scale for Youth Sports (Cumming et al., 2008) assesses goal orientation while participating in a sports activity (e.g., “My goal is to learn new skills and get as good as possible”). The items are divided into two subscales representing two different goal orientations: mastery (e.g., “My goal is to learn new skills and as good as possible”) and ego (e.g., “To me, success means being better than others.”). All items were responded to on a 5-point scale from 1 (not true at all) to 5 (very true). Previous research has found good internal reliability for both mastery orientation (α = .78) and ego orientation (α = .88; Cumming et al., 2008), as was found again in the current study (mastery: α = .60, .80, .86, .89, and .90; ego: α = .90, .90, .92, .79, and .74). For this study, mean scores for each of the subscale items were calculated, and higher scores indicate greater mastery or ego orientation.

Results

Table 1 includes number of respondents, means, and standard deviations for each outcome, by time point.

Table 1.

Number of Respondents, Means, and Standard Deviation of Outcomes by Time Point.

Time point	Marching self-efficacy			General self-efficacy			Mastery orientation			Flow
Time point	n	M	SD	n	M	SD	n	M	SD	n	M	SD
1	73	4.16	0.68	72	4.38	0.71	74	4.67	0.38	73	3.60	0.48
2	44	3.99	0.67	44	4.08	0.73	46	4.57	0.44	45	3.5	0.42
3	39	4.08	0.68	39	4.16	0.69	40	4.50	0.53	39	3.58	0.45
4	28	4.31	0.71	28	4.01	0.75	28	4.39	0.66	28	3.54	0.52
5	24	4.88	0.79	24	4.54	0.72	24	4.55	0.50	24	3.93	0.59

Trajectories of change

To assess trajectories of change for each outcome (general self-efficacy, marching self- efficacy, flow, mastery, and goal-oriented motivation), five hierarchical linear modeling analyses were run in SAS PROC MIXED. A variable for time was created for analyses, coded as the average number of days since beginning the MA season that participants completed at each time point, with baseline coded as 0. Time point two was coded as 11.84 days, three as 28.61 days, four as 66.37 days, and five as 89.27 days. The time variable was also squared to be able to test for both linear and curvilinear change over time. The time (assessing if participants’ change was linear) and time squared (assessing if the participants’ change was curved) variables were entered into analyses as simultaneous predictors for each of the outcomes.

Both time (B = −0.0109, SE = 0.00395, t = −2.75, p = .006) and time squared (B = 0.0002, SE = 0.00005, t = 3.39, p = .0009) were significant predictors of general self-efficacy, suggesting both linear and curvilinear trajectories of change. The average change in general self-efficacy score between baseline and time two was −.11 (approximately −.009 per day). The average change in general self-efficacy score between time two and time three was −.08 (approximately −.005 per day). The average change in general self-efficacy score between time three and time four was .14 (approximately .004 per day). The average change in general self-efficacy score between time four and end of season was .29 (approximately .013 per day). As can be seen in Figure 1, general self- efficacy, on average, decreased from baseline to time three (end of spring training/start of tour). After time three, general self-efficacy increased throughout the rest of the season, ending higher than where it began at baseline.

Figure 1.

Curvilinear Pattern of Change in General Self-Efficacy over Time. Scale Ranged from 1 (Strongly Disagree) to 6 (Strongly Agree). The y-Axis Is Magnified Here to Make Trajectories Easier to Visualize.

Time squared (B = 0.0001, SE = 0.00005, t = 2.94, p = .004) was a significant predictor of marching self-efficacy, suggesting a curvilinear trajectory of change, while time was not associated (B = 0.0043 SE = 0.0043, t = −1.01, p = .313). The average change in marching self-efficacy score between baseline and time two was −.03 (approximately −.003 per day). The average change in marching self-efficacy score between time two and time three was .02 (approximately .001 per day). The average change in marching self-efficacy score between time three and time four was .34 (approximately .009 per day). The average change in marching self-efficacy score between time four and end of season was .40 (approximately .017 per day). As can be seen in Figure 2, marching self-efficacy first decreased from baseline to time two (a week into spring training), and then began increasing between time two and time three. From time three onward, marching self-efficacy increased into the end of the season, ending higher than baseline.

Figure 2.

Depiction of Significant Curvilinear Pattern of Change Found in Marching Self-Efficacy over Time. Scale Ranged from 1 (Strongly Disagree) to 6 (Strongly Agree). The y-Axis Is Magnified Here to Make Trajectories Easier to Visualize.

Time (B = −0.0063, SE = 0.00326, t = −1.92, p = .057) marginally predicted, and time squared (B = 0.0001, SE = 0.00004, t = 2.98, p = .003) significantly predicted flow, suggesting a curvilinear trajectory of change. The average change in flow score between baseline and time two was −.06 (approximately −.005 per day). The average change in flow score between time two and time three was −.03 (approximately −.002 per day). The average change in flow score between time three and time four was .15 (approximately .004 per day). The average change in flow score between time four and end of season was .24 (approximately .011 per day). As can be seen in Figure 3, flow, on average, decreased from baseline to time three. After time three, general flow increased throughout the rest of the season, ending higher than where it began at baseline.

Figure 3.

Curvilinear Pattern of Change in Flow over Time. Scale Ranged from 1 (Strongly Disagree) to 5 (Strongly Agree). The y-Axis Is Magnified Here to Make Trajectories Easier to Visualize.

Both time (B = −0.0099, SE = 0.00301, t = −3.27, p = .001) and time squared (B = 0.0001, SE = 0.00003, t = 2.92, p = .004) were significant predictors of mastery goal orientation, suggesting both linear and curvilinear trajectories of change. The average change in mastery goal orientation score between baseline and time two was −.10 (approximately −.009 per day). The average change in mastery goal orientation score between time two and time three was −.10 (approximately −.006 per day). The average change in mastery goal orientation score between time three and time four was .01 (approximately .0004 per day). The average change in mastery goal orientation score between time four and end of season was .13 (approximately .006 per day). As can be seen in Figure 4, mastery goal orientation decreased from baseline into time four (middle of tour). After time four, mastery goal orientation increased into time five.

Figure 4.

Curvilinear Pattern of Change in Mastery Goal Orientation over Time. Scale Ranged from 1 (Not True at All) to 5 (Very True). The y-Axis Is Magnified Here to Make Trajectories Easier to Visualize.

Neither time (B = 0.0040, SE = 0.00522, t = 0.76, p = .446 nor time squared (B = 0.00001, SE = 0.00006, t = 0.28, p = .781) were significant predictors of ego goal orientation, suggesting no consistent pattern of change across participants.

Discussion

We found curvilinear changes across the MA season in general self-efficacy, marching self-efficacy, flow, and mastery orientation. The general trend for all of these variables was that participants psychosocially declined during spring training from where they began preseason. Similar to Matthews (2017), the variables examined followed curvilinear trajectories. After spring training, once tour began, participants rebounded, showing an improvement throughout tour into the end of the season. For general self-efficacy, there was a relatively sharper decline from baseline to the end of spring training with a steady substantial increase through tour into the end of the season, concluding at levels above baseline. For marching self-efficacy and flow, there was only a slight decrease during spring training, and after spring training, the growth in marching self-efficacy and flow was significant, concluding at levels above baseline. Finally, for mastery orientation, there was a sharper decline from baseline through spring training, then a leveling out from the end of spring training until half way through tour. After that, mastery orientation increased, but did not conclude above baseline levels.

The time points for data collection were designed to occur around major transitions in the drum corps season, and therefore the results may reflect some of the changes for participants across these training and performance/competition periods. The second and third time points were collected during the corps’ spring training, which is the drum corps equivalent of band camp. Spring training is when the corps learns the entirety of their show, are exclusively rehearsing, and are the most isolated from their outside community, making it the most challenging point of the season. Echoing the findings of Dagaz’s (2012) study on band camp, the dip in the studied variables across these time points was likely due to the stress and challenge of spring training. The second time point, collected during the first week of spring training, was chosen to be close to when MA participants tend to experience the “wall.” Within the MA community, the wall is a popular colloquialism to refer to a moment or period of time that members go through in which they feel like they are overwhelmed and cannot continue. Though prominently discussed in the community, the wall has not been studied in the literature. The expectation from the coaching staff is that as members break through their walls, they grow stronger from that point forward. Our study provides some evidence of the existence of the wall and that it may not be isolated to the first week of spring training.

Following spring training, the corps begins touring the country, competing, and performing. At the end of spring training, members have overcome what is considered by the community to be the most difficult part of the season. The corps still rehearses daily, but for less time than during spring training. At this point, the schedule consists of not only rehearsals, but also competitive performances and exhibitions for the public. The corps also then allows members free days or blocks of time to do laundry and enjoy free time in public spaces. The fourth time point captures the middle of the tour season and the fifth captures the end of the tour season. The lessening of rehearsal time, the empowerment of surviving spring training, members’ reconnection with society, and their enjoyment of positive feedback about their shows could individually or collectively explain the rebound from spring training through tour, but this study was unable to definitively assess such nuances.

Ultimately, for marching self-efficacy, general self-efficacy, and flow, participants scored higher on average at the final time point compared with baseline. These results were expected given previous literature finding participants had qualitatively reported improvement in confidence and overall enjoyment (Dagaz, 2012; Matthews, 2017). However, mastery orientation, on average, was near baseline at the final time point, contrary to results of those same previous studies (Dagaz, 2012; Matthews, 2017). This could potentially be due to the difficulty of the whole program. Members could be strained and challenged throughout more than they are able to bounce back from within the course of the season, thus resulting in their lower self-perception about their motivation in the time frame assessed. Mastery orientation also never rebounded back to baseline levels, consistent with past research that found significant decreases in intrinsic motivation by the end of a collegiate marching band season compared with the beginning (Weren et al., 2016). However, our study found that this pattern was curvilinear, suggesting some rebound from the initial decline.

Limitations and future directions

This study was not experimental, and thus causality cannot be determined. However, the study’s longitudinal design and program-relevant survey schedule identified how various psychosocial variables change across a MA season, particularly around meaningful periods of transition within the program. The five time point design replicated, extended, and added specificity to previous findings showing curvilinear change (Matthews, 2017). Although trajectories across the outcomes followed similar overall patterns, unique dynamics of specific outcome trajectories were identified through the number of time points included.

Notably, there was a large amount of attrition during the study. The study began with 74 consenting participants and concluded with 24 participants in the sample. This was likely due to participant fatigue compounded with fatigue from constant rehearsal and performing over the course of the season. The participants were not allocated time to take the survey by the marching program, the survey had to be taken during their personal free time (i.e., food breaks, rest time, time in transit), of which they did not have much. Despite announcements and reminders, participants may have forgotten to take the survey within the one-day window of time the research staff gave them to participate using their personal devices. To account for some of the missing data, our procedures used restricted maximum likelihood estimation, which is particularly useful in accounting for missing data and smaller sample sizes in analyses (Corbeil & Searle, 1976).

Because we recruited participants from only one program with specific member requirements (e.g., all male, ages 15–22 years, only brass, percussion, and color guard), an important limitation is potential lack of generalizability. The study was also limited due to the lack of data collected on specific resources and day-to-day environmental challenges of the program. While this study assesses psychosocial change in relationship to the universal general structure of a drum corps season (i.e., spring training/tour), more information pertaining to the day-to-day environmental context would have been informative in better explaining these dynamics. This program is organized similarly to other MA organizations; however, every program has individual differences. We suggest not only should procedures be expanded to include more program-specific contextual information, but they should also be replicated in different MA programs at different levels with different populations (e.g., mixed-gender drum corps, predominately White institution collegiate marching bands, marching bands from historically Black universities and colleges, high school marching bands, indoor percussion, and indoor color guard) to provide a full picture of effects and possible benefits of MA participation over time.

Finally, this study used self-report survey responses. Although the survey was conducted anonymously to try and decrease social desirability concerns, the participants as members of the corps still knew the program administration’s goals and expectations for developing the members’ efficacy, leadership skills, and goal orientation, which could have led to social desirability or other participant biases in responding.

Conclusions and implications

Despite limitations, this study’s findings have implications for MA educators and administrators. It should be emphasized that these data suggest that spring training is detrimental to the psychosocial health of MA participants. MA educators should be aware that the stress of spring training is taking a toll on members throughout all of spring training and tour. To maximize the benefits seen in tour, MA educators will need to consider ways in relieving some of the burden of spring training, while still effectively facilitating the learning of the show. Multiple studies have found decreases in motivation across MA seasons. Losses in motivation can potentially have detrimental effects on the productivity and atmosphere of a program, such as to rehearsal quality, performer effort, openness to staff critique, and cohesion between performers. As such, we suggest MA educators should actively address and facilitate members’ motivation throughout the season.

The MA literature is small, and many questions remain for future investigation. Which aspects of the program specifically influence the observed curvilinear changes over time needs to be examined more directly. Future work should distinguish the roles of performing, rehearsal time, and free time in psychological health of participants, perhaps using metrics for time spent in rehearsal and free time allotted as predictors. Differences in program competitive success, participants’ accounts of overcoming obstacles, participants’ perception of audience reaction, and personal show performance could also be explored in relation to outcomes in future research. Matthews (2017) and Weren and colleagues (2016) found relationships between group characteristics and motivation in marching college students, which also suggests the important role of the group and collective climate among members. Dagaz (2012) showed that individual feelings of inclusion and perceived responsibility to the group is foundational to the success of the program and collective well-being and thus warrants exploration through measurement of inclusion and participant beliefs about their role in the ensemble. Future researchers may also want to test other potential benefits of MA and directly compare benefits of sport, non-marching music performance, and marching programs. Overall, the MA are a uniquely rich domain in which to study questions important to both music education and sport participation.

Supplemental Material

sj-pdf-1-pom-10.1177_03057356221097781 – Supplemental material for Psychosocial change across a drum corps season

Supplemental material, sj-pdf-1-pom-10.1177_03057356221097781 for Psychosocial change across a drum corps season by DaSean Lucas Young, Lisa Rosenthal and Thalia R Goldstein in Psychology of Music

Footnotes

Funding

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

ORCID iD

DaSean Lucas Young

Supplemental material

Supplemental material for this article is available online.

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