RESEARCH ARTICLE
Longitudinal Analysis of Academic Burnout in Korean Middle School Students Boyoung Kim, Minyoung Lee, Keunhwa Kim, Hyunju Choi & Sang Min Lee*† Department of Education, College of Education, Korea University, Seoul, Korea
Abstract The purpose of the study was to investigate the longitudinal relationships between the initial values and slopes of three dimensions of burnout syndrome (i.e. emotional exhaustion, cynicism and academic inefficacy). The study utilized four-wave longitudinal data from a total of 367 (81.6% response rate) middle school students in South Korea. Comprising a 6-month interval survey, the first survey was conducted in June 2010, the second in December 2010, the third in June 2011 and the fourth in December 2011. All participants were 13-year-olds at the first and second surveys, and 14-year-olds at the third and fourth surveys. The Maslach Burnout Inventory—Student Survey was used for each survey to assess the level of academic burnout. The longitudinal data were analysed using latent growth modelling. The results of the study indicated that high initial values (intercept) for emotional exhaustion were associated with a higher rate of increase (slope) in cynicism and academic inefficacy. On the other hand, high initial values for cynicism and academic inefficacy were associated with a lower rate of increase in the other dimensions. This longitudinal study should promote understanding of burned-out students and contribute to the literature by informing the design of prevention programmes for academic burnout. Copyright © 2014 John Wiley & Sons, Ltd. Received 14 January 2013; Revised 15 August 2013; Accepted 14 October 2013 Keywords academic burnout; longitudinal analysis; latent growth model *Correspondence Sang Min Lee, Department of Education, College of Education, Korea University, Anam-dong, Seongbuk-gu, Seoul, Korea. † E-mail: [email protected] Published online 23 January 2014 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/smi.2553
The pressure to perform successfully at school is a heavy burden for adolescents. Particularly in South Korea, there is a strong culture of expectation and aspiration to accomplish high academic achievement (Lee, Puig, Kim, Shin, Lee, & Lee, 2010). Most Korean adolescents give a great deal of time to their studies, and as a result, they suffer from extreme academic stress due to excessive competition as well as school work (Lee, Puig, Lea, & Lee, 2013). Hence, the academic stress accrued from elementary to high school is the primary factor for chronic academic burnout. The concept of ‘burnout’ was first introduced by Freudenberger (1974), who defined the term as ‘to fail, to wear out, or become exhausted by making excessive demands on energy, strength, or resources’ (p. 159). Research on burnout syndrome has been popularized in line with the development of the Maslach Burnout Inventory (MBI; Maslach & Jackson, 1981). This tool interprets burnout as a psychological syndrome that arises in response to chronic job stressors (Maslach, Schaufeli, & Leiter, 2001). Whereas early burnout research focused on human service professionals, recent burnout studies have been applied to other groups such as parents and students (Brouwers & Stress Health 31: 281–289 (2015) © 2014 John Wiley & Sons, Ltd.
Tomic, 2000; Butler & Charles, 1999; Lee et al., 2010; Tomic, Tomic, & Evers, 2004). Since students’ main activities at school can be considered as a form of work, ‘academic burnout’ exists as a response to students’ continuing difficulties in coping with multiple achievement pressures (Salmela-Aro, Kiuru, Leskinen, & Nurmi, 2009b). Students need to attend regular and structured classes and complete their assignments every day. This performance is also reflected on their school records. In addition, they also receive penalties and compensation such as flunking or a good grade. These class activities and assessment activities are comparable with the work of formal employees. That is, students experience burnout just as formal workers do when they must carry a heavy academic load (Lee, 2010). School is a context in which students work (Samela-Aro, Hannu, & Holopainen, 2009). The Maslach Burnout Inventory—Student Survey (MBI-SS; Schaufeli, Martínez, Pinto, Salanova, & Bakker, 2002), which was developed for measuring students’ academic burnout, construes academic burnout as a multidimensional construct consisting of emotional exhaustion, cynicism and inefficacy. Emotional exhaustion signifies the feeling of being 281
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overextended and depleted of resources, representing the basic individual stress dimension of burnout. Cynicism signifies cynical, distant attitudes and feelings about one’s tasks. Inefficacy signifies the tendency to take a pessimistic view of one’s achievement at work (Maslach & Schaufeli, 1993). Specifically, ‘exhausted’ students feel emotionally drained by their studies. Students who experience ‘cynicism’ have become less enthusiastic about their studies. Students who believe ‘I make an effective contribution to the classes’ experience less ‘inefficacy’. Over the past two decades, relationships among three dimensions of burnout syndrome have been of interest to researchers (e.g. Golembiewski, Munzenrider, & Stevenson, 1986; Lee & Ashforth, 1993a; Lee & Ashforth, 1993b; Leiter & Maslach, 1988; Parker & Salmela-Aro, 2011; Schaufeli & Enzmann, 1998; Taris, Le Blanc, Schaufeli, & Schreurs, 2005). The possibility of causal relationships among three dimensions of burnout has been examined. As a result, several developmental process models of burnout have been suggested. The best known burnout process model asserted that emotional exhaustion is the primary stage of burnout (Leiter & Maslach, 1988), which then engenders the development of cynicism as an ineffective coping strategy as well as the accumulation of feelings of incompetence (Leiter, 1989). In this model, high levels of emotional exhaustion would trigger high levels of cynicism, which would in turn lead to high levels of incompetence (Taris et al., 2005). Different from the initial developmental burnout process model by Leiter and Maslach (1988), Golembiewski (1989) suggested that the cynicism dimension is the initial component, which is then followed by feelings of inefficiency, with emotional exhaustion emerging in the more damaging phase of burnout. That is, cynicism, as a dysfunctional attempt to cope with job stress, leads workers to develop feelings of inefficacy. Moreover, they become emotionally exhausted because they feel that their heavy workloads are out of proportion to their capacity. There are some integrated models that are combined with previous developmental burnout process models (Lee & Ashforth, 1993b; Taris et al., 2005). Lee and Ashforth’s developmental burnout model is somewhat similar to the model of Leiter and Maslach in that emotional exhaustion triggers cynicism; however, it differs in that feelings of inefficacy develop independently from cynicism, meaning that emotional exhaustion directly affects inefficiency and does not go through cynicism. In addition, Taris’s model determined that emotional exhaustion affects feelings of inefficacy through cynicism, both directly and indirectly, combining the model of Leiter and Maslach with that of Lee and Ashforth. Because most studies (e.g. Golembiewski et al., 1986; Leiter & Maslach, 1988) were of cross-sectional nature, it is important to examine the relationship among three dimensions of burnout longitudinally. If we trace the changes of the three component of burnout in longitudinal data analysis, we can observe the relationship 282
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among them in terms of the intrapersonal aspect. Nevertheless, a notable paucity of research exists on the relationship among three dimensions of burnout from a longitudinal perspective. Furthermore, few research studies of the developmental model in academic burnout have been conducted (Galbraith & Merrill, 2012; Shin, 2012). Research on the initial values (first-wave data point) and slope (change rates) of each dimension provides information not only on the simple developmental processes but also on the particular direction required to develop future intervention. For example, if a high initial value for emotional exhaustion is found to trigger greater rate of change for cynicism and inefficacy, emotional exhaustion should be the primary focus of treatment because it is the cause of the intensified burnout. By reducing emotional exhaustion, the foreseeable latent consequences of burnout, namely cynicism and inefficacy symptoms, could be prevented. Thus, the purpose of this study is to investigate the growth and the relationships between the initial values and slopes of the three dimensions of academic burnout syndrome. Aims of the study The present study explores the longitudinal relationships among three dimensions of academic burnout and the growth trajectories in Korean middle school students. Specifically, we examined the effect of initial values of one dimension on change rate of other dimensions to derive implications of the developmental process of academic burnout. The following hypotheses were examined in this study. Firstly, we hypothesized that three dimensions of academic burnout would increase across school years. Lee (2010) conducted mean difference tests of latent variables (i.e. inefficacy, cynicism, antipathy and exhaustion) in order to assess academic burnout of Korean students. Following the results of Lee’s (2010) study, we hypothesized that academic burnout would increase in Korean middle school students over time. However, we do not yet hold a specific hypothesis in terms of the linear or nonlinear growth, which can represent individual change. Rather, we examined how three dimensions of academic burnout grew across school years of middle school students by testing whether the growth pattern would exhibit either a linear or non-linear growth trajectory in academic burnout, and we conducted latent growth model analysis (Duncan, Duncan, Stycker, Li, & Alpert, 1999; Muthén & Muthén, 2006). Secondly, intra-individual or intrapersonal differences would exist in three dimensions of academic burnout across the school years. Most previous studies have only focused on interpersonal differences of burnout because of cross-sectional samples. Intra-individual differences can be estimated by conducting latent growth modelling (LGM) with a longitudinal design. We established a research model using LGM to test Stress Health 31: 281–289 (2015) © 2014 John Wiley & Sons, Ltd.
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statistically significant variances that mean intraindividual differences. Thirdly, we hypothesized that higher levels of emotional exhaustion would trigger higher change rates for cynicism and academic inefficacy. Emotional exhaustion is believed to be the primary stage of burnout among its three components (Lee & Ashforth, 1993b; Leiter & Maslach, 1988; Taris, Schreurs, & Schaufeli, 1999). That is, high levels of emotional exhaustion predict high levels of cynicism or inefficacy. If these results are valid in the academic burnout phenomenon, high levels of emotional exhaustion will lead to the development of high levels of cynicism or academic inefficacy. Therefore, we expected that higher initial levels of emotional exhaustion would be significantly associated with greater change rates for cynicism and academic inefficacy. As shown in Figure 1, it was expected that higher levels of ‘EE i’ would trigger higher levels of ‘CY s’ and ‘AI s’. Finally, we hypothesized that higher levels of cynicism would have negative relationships with change rates for emotional exhaustion and academic inefficacy. According to Faber (2000), teachers who had cynicism tend not to be exhausted and not to feel inefficacy. We hypothesized that cynicism of academic burnout would be similar to the result of Faber (2000) because academic burnout reveals similar features with job burnout. In addition to the previous study, we explore the longitudinal relationships of cynicism with the other two dimensions. As shown in Figure 1, it was expected that higher levels of ‘CY i’ would have negative relationships with ‘EE s’ and ‘AI s’.
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Method Participants and procedure The sample consists of South Korean adolescents (7th graders in urban communities), and the four waves of data for 2 years were collected at one semester intervals. Just before the final semester exam was considered as the proper moment for measurement of academic burnout because this time is expected to be the highest level of academic stress. This comprised a 6-month interval survey, which led to the first survey being conducted in June 2010, the second in December 2010, the third in June 2011 and the fourth in December 2011. Survey packets containing informed consent, demographic questions and one copy of MBI-SS were distributed to potential participant. Students voluntarily completed the survey instrument during class following a homeroom teacher’s instructions. To protect confidentiality, students used their school identification numbers, by which students were tracked across periods. At the first survey, 412 packets of the 450 were returned. Of these, 45 packets were incomplete surveys without responses to five or more items, which were excluded from the data analyses. After exclusions, 367 surveys (81.6% response rate) were included in the final sample. As for characteristics of the sample, 56% was female (N = 207). No significant differences on academic burnout level between male and female were observed. Participants’ age was not a concern because this was drawn from the same age group. That is, all
Figure 1. The research model Stress Health 31: 281–289 (2015) © 2014 John Wiley & Sons, Ltd.
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participants were 13-year-olds at the first and second measurements, and 14-year-olds at the third and fourth measurements. The participation rate from the second survey to fourth survey was favourable (98%, 92% and 97%, respectively). Tests for differences between completers and dropouts were conducted in terms of gender, parents’ education level and burnout values at Time 1. As a result, statistically significant differences were not found. In order to manage missing data, we tested whether a data set was missing completely at random (MCAR) or not. The result of Little’s (1998) MCAR test accepted the null hypothesis (χ 2 = 75.41, p = 0.37), which means a suspicious pattern or distribution in missing data was not detected. So, full information maximum likelihood estimation, which combines parameter estimation with missing data analysis (Parker & Salmela-Aro, 2011), was employed. Measure Maslach Burnout Inventory—Student Survey The Korean version of the MBI-SS (Shin, Puig, Lee, Lee, & Lee, 2011) was used to assess students’ level of academic burnout. The MBI-SS scale contains three dimensions: emotional exhaustion (five items), cynicism (four items) and academic efficacy (six items). Although there was criticism about the dimensions of MBI, which comprise exhaustion, depersonalization and cynicism (Kristensen, Borritz, Villadsen, & Christensen, 2005), Schaufeli and Taris (2005) asserted that burnout should be conceptualized as a work-related phenomenon of at least two dimensions (fatigue and withdrawal) that can be applied to exhaustion and depersonalization/cynicism. According to Shin et al. (2011), who conducted cultural validation of the Korean version of MBI-SS, a better model fit was shown in the three-factor model, which implies a multidimensional construct of burnout, than in the onefactor model, which regards burnout as a homogenous phenomenon. This result signifies that the academic burnout phenomenon can be interpreted well on the basis of the three-factor model, and it is in line with previous burnout studies for counsellors and police managers (Demerouti, Verbeke, & Bakker, 2005; Loo, 2004). Because Shin et al. (2011) used a sample composed of middle school and high school students for validation, the Korean version of the MBI-SS was expected to be appropriate for the present sample, composed of middle school students. For these reasons, we employed each factor of burnout as a latent variable in the present study. Respondents were asked to rate items on a scale from 1 (strongly disagree) to 5 (strongly agree). Higher scores indicate greater degree of burnout. The reliability coefficients for these dimensions were as follows: emotional exhaustion (e.g. I feel emotionally drained by my studies., αT1 = 0.83, αT2 = 0.87, αT3 = 0.87, αT4 = 0.89), cynicism (e.g. I have become less enthusiastic about my studies., αT1 = 0.82, αT2 = 0.83, αT3 = 0.83, αT4 = 0.85) 284
and academic efficacy (e.g. I believe that I make an effective contribution to the classes that I attend., αT1 = 0.83, αT2 = 0.84, αT3 = 0.83, αT4 = 0.85). As such, the coding for academic efficacy was reversed. Data analysis Descriptive statistics were used for basic understanding of burnout symptoms, and intercorrelations of the three dimensions were used to examine the relationships within burnout, using PASW 18.0 (IBM, Armonk, New York, US). Next, to assess the relationships among the three dimensions of burnout over time, the data were analysed using LGM (Duncan et al., 1999; Muthén & Muthén, 2006) as implemented in the AMOS 18.0 package (IBM, Armonk, New York, US). Full information maximum likelihood was used to correct for the presence of any missing data. Inspection of the univariate distributions of the variables revealed no substantive deviations from normality. As for multivariate normality, tests of multivariate kurtosis (Media, 1970) turned out that the present sample could deviate from multivariate normality (e.g. b2p = 27.59, p = 0.000). Nevertheless, the plot of ordered squared distances illustrated a linear pattern, and the value of Mahalanobis distance (D2) indicated that there was no multivariate outlier. Accordingly, LGM analysis was conducted. Latent growth modelling combines features of structural equation modelling, which incorporates latent variables, and hierarchical linear modelling (Raudenbush & Bryk, 2002), which allows random coefficients across individual developmental trajectories. LGM enables analyses of associations between change over time in the independent and dependent variables of interest (Simons-Morton, Chen, Abroms, & Haynie, 2004). In LGM, the basic measurement model has two latent constructs: one represents the intercept (initial status) for the construct, and the other represents the slope (rate of change) for the construct (Wills & Cleary, 1999). As such, LGM could present a general overview of the measurement of individual change in burnout over the four semesters period (i.e. intra-individual change) and differences in such change across all subjects (i.e. inter-individual change). The longitudinal research model is made up of three parts: emotional exhaustion, cynicism and academic inefficacy. To examine the growth pattern of each part (linear or non-linear), linearity of growth was separately analysed in three models. After this, to investigate the relationship among the three burnout components, the research model was analysed, and several types of model fit were provided: chi-square (χ 2) test, Tucker–Lewis index, comparative fit index (CFI) and root mean square error approximation (RMSEA).
Results Descriptive statistics and the intercorrelations for the three dimensions of academic burnout are presented in Table I. The means of emotional exhaustion were MT1 = 2.56 [standard deviation (SD) = 0.89], MT2 = 2.59 Stress Health 31: 281–289 (2015) © 2014 John Wiley & Sons, Ltd.
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(SD = 0.88), MT3 = 2.81 (SD = 0.90) and MT4 = 2.71 (SD = 0.90). Those of cynicism were MT1 = 2.43 (SD = 0.93), MT2 = 2.45 (SD = 0.91), MT3 = 2.54 (SD = 0.93) and MT4 = 2.68 (SD = 0.95), and those of academic inefficacy were MT1 = 2.87 (SD = 0.74), MT2 = 2.85 (SD = 0.74), MT3 = 2.88 (SD = 0.71) and MT4 = 2.92 (SD = 0.73). Skewness and kurtosis were within the cut-off points for normality for all scales for the four measured waves (Curran, West, & Finch, 1997). The range of intercorrelations among the three burnout dimensions varied from 0.28 to 0.68 over the study period, and all coefficients were significant (p < 0.001). Repeated-measures analysis of variance was conducted as a preliminary analysis to test whether changes of academic burnout over time were statistically significant. Figure 2 shows the levels of the three dimensions of academic burnout changing across the four time points. Repeated-measures analysis of variance of academic burnout scores showed significant time effects (F = 14.25, p < 0.001) and dimensions of academic burnout–time interaction (F = 3.48, p < 0.01). Therefore, LGM was conducted for more specific analyses on the basis of this result. To test whether the data could be best described by linear or non-linear growth, two models of each burnout dimensions were tested. In the linear model, factor loadings for the slope parameter were fixed to correspond to a linear time scale (0, 1, 2 and 3). In the non-linear model, the slope parameter was freely estimated. In order to better identify the model, at least two factor loadings on the slope parameter must be fixed to two different values (Meredith & Tisak, 1990). Therefore, the first two parameters
Figure 2. Repeated-measures analysis of variance of academic burnout scores
were fixed at 0 and 1, whereas the other two parameters were estimated freely. As a result, the linear model was more adequate for data interpretation than the non-linear model, since errors in variance were detected in the non-linear model but not in the linear model. This result indicates that the growth of the three dimensions of academic burnout had a linear tendency. Before we analysed the research model, the growth of each sub-component of burnout was examined. Table II shows the growth of the three variables. All intercept and slope means were significant except for the slope mean of academic inefficacy. These reveal that the mean level of burnout in this sample progressed in the measuring stages except for academic inefficacy. Table II also shows that all variances of intercepts and slopes were significant
Table I. Descriptive statistics and intercorrelations of variables across four waves Time 1
Time 2
Time 3
Time 4
Emotional Academic Emotional Academic Emotional Academic Emotional Academic exhaustion Cynicism inefficacy exhaustion Cynicism inefficacy exhaustion Cynicism inefficacy exhaustion Cynicism inefficacy EE_T1 CY_T1 AI_T1 EE_T2 CY_T2 AI_T2 EE_T3 CY_T3 AI_T3 EE_T4 CY_T4 AI_T4 Mean SD Skewness Kurtosis
1 0.56*** 0.43*** 0.62*** 0.45*** 0.39*** 0.46*** 0.39*** 0.35*** 0.40*** 0.47*** 0.35*** 2.56 0.89 0.25 0.20
1 0.62*** 0.49*** 0.63*** 0.56*** 0.34*** 0.59*** 0.46*** 0.24*** 0.50*** 0.48*** 2.43 0.93 0.36 0.24
1 0.40*** 0.51*** 0.67*** 0.24*** 0.43*** 0.55*** 0.22*** 0.37*** 0.57*** 2.87 0.74 0.07 0.09
1 0.61*** 0.46*** 0.47*** 0.48*** 0.39*** 0.46*** 0.49*** 0.41*** 2.59 0.88 0.08 0.31
1 0.57*** 0.35*** 0.62*** 0.46*** 0.26*** 0.54*** 0.50*** 2.45 0.91 0.22 0.49
1 0.28*** 0.48*** 0.64*** 0.29*** 0.47*** 0.62*** 2.85 0.74 0.00 0.13
1 0.55*** 0.43*** 0.62*** 0.48*** 0.40*** 2.81 0.90 0.02 0.42
1 0.55*** 0.41*** 0.67*** 0.54*** 2.54 0.93 0.33 0.20
1 0.34*** 0.49*** 0.68*** 2.88 0.71 0.32 0.46
1 0.55*** 0.38*** 2.71 0.90 0.05 0.41
1 0.58*** 2.68 0.95 0.23 0.27
2.92 0.73 0.01 0.57
EE: emotional exhaustion; CY: cynicism; AI: academic inefficacy; T: measurement time; SD: standard deviation. ***p < 0.001. Stress Health 31: 281–289 (2015) © 2014 John Wiley & Sons, Ltd.
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in the research model. This suggests a rationalization for including predictor variables in the model (Byrne & Crombie, 2003). The research model has a total of six latent constructs because academic burnout has three dimensions, each of which has two latent constructs (initial status and slope). This model hypothesized that an initial status in one dimension of academic burnout is related to change in the rate of the other two dimensions of burnout. To explore the relationship among the three components of the burnout symptoms, the research model was specified in terms of longitudinal conceptualization. In the linear model, the chi-square (χ 2 = 126.03, degrees of freedom = 45, p < 0.001) was significant. However, chi-square tends to be easily significant with large sample sizes (Brannick, 1995) even when all other indices of fit indices display a good fit. For this reason, other indices of fit were examined. CFI was 0.97, non-normed fit index (NNFI) was 0.95 and RMSEA was 0.07 (90% CI [0.05, 0.08]). Hu and Bentler (1999) suggested that for continuous data, NNFI (also known as Tucker–Lewis index) > .95, CFI 0.95 and RMSEA < 0.08. On the basis of Hu and Bentler’s criteria, as shown in Table II, the data met the necessary assumptions for analysis since the measurement model had a reasonable fit. Parameter estimates for the latent growth models of emotional exhaustion, cynicism and academic inefficacy are presented in Figure 3. The path from the emotional exhaustion intercept to the cynicism slope was significant (ß = 0.96, p < 0.001); it was also significant (ß = 0.98, p < 0.001) to the academic inefficacy slope. The path from the cynicism intercept to the emotional exhaustion slope was significant (ß = 0.52, p < 0.05) and likewise to the academic inefficacy slope (ß = 0.73, p < 0.01). The path from the academic inefficacy intercept to the emotional exhaustion slope was positive but non-significant (ß = 0.35, ns), whereas it was significant (ß = 0.75, p < 0.01) to the cynicism slope. These results demonstrate that a high initial value (intercept) for emotional exhaustion leads to a higher rate of increase (slope) in cynicism and academic inefficacy. On the other hand, high initial
Figure 3. Path coefficients from intercept to slope of measurement model
values for cynicism and academic inefficacy were associated with a lower rate of increase in the other dimensions.
Discussion The main purpose of our study was to explore the possible relationships among the three dimensions of MBI by examining the initial status and the change rates of burnout symptoms. Firstly, using LGM analysis, we tested two models of each burnout dimension to examine whether the data fitted with a linear or non-linear growth model. We found that the linear model was the best fit for the data. This result revealed that dimensions of academic burnout were growing linearly during the middle school period. This result supported the first hypothesis, which stated that ‘the three dimensions of academic burnout would increase across school years’. According to Lee (2010), academic burnout symptoms of Korean students grew over time. She
Table II. Parameter estimates for latent growth models of emotional exhaustion, cynicism and academic inefficacy Emotional exhaustion
Intercept: mean Variance Slope: mean Variance Covariance intercept slope
Cynicism
B
SE
CR
12.88*** 14.36*** 0.31*** 1.63*** 2.36***
0.22 1.49 0.09 0.26 0.51
59.01 9.64 3.64 6.18 4.66
B 9.59*** 9.53*** 0.37*** 0.61*** 0.82***
Academic inefficacy
SE
CR
B
SE
CR
0.18 1.00 0.06 0.15 0.31
52.92 9.53 5.81 4.02 2.65
17.15*** 13.54*** 0.12*** 0.71*** 1.02***
0.21 1.38 0.07 0.19 0.40
80.12 9.80 1.72 3.68 2.56
T: measurement time; SE: standard error, CR: Critical ratio. ***p < 0.001, **p < 0.01, *p < 0.05.
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conducted mean difference tests of latent variables (i.e. inefficacy, cynicism, antipathy and emotional exhaustion) in order to assess growth of academic burnout. However, her conclusion was not deduced from a longitudinal research design. Salmela-Aro et al. (2009) found that early career burnout increases linearly in a university student sample. Similarly, a Korean middle school student sample also exhibited a linear growth of academic burnout. In addition, variances of the linear model for each dimension were statistically significant. The result supported the second hypothesis, which stated that ‘intra-individual or intrapersonal differences would exist in three dimensions of academic burnout across the school years’. Significant variances in the intercepts of the three dimensions of academic burnout indicated that substantial individual differences existed at baseline. This is that some students have higher scores on the three dimensions of academic burnout whereas others have lower scores at Time 1. Significant variances in slopes revealed intra-individual differences in the probability of progressing in academic burnout over time. This result indicated that each individual has different change slopes. It could be interpreted that three dimensions of academic burnout of some students increased by different rates compared with academic burnout dimensions of others over the same period. This is that some students burnt out over this time whereas others did not. Therefore, intraindividual or intrapersonal differences existed in the three dimensions of academic burnout in this study, and it was appropriate to conduct LGM for considering these intra-individual or intrapersonal differences. In the research model, higher initial levels of emotional exhaustion are significantly associated with greater change rates for cynicism and academic inefficacy. Thus, it can be suggested that emotional exhaustion is the starting point of academic burnout. Accordingly, the result that a higher level of emotional exhaustion triggers higher change rates for cynicism and academic inefficacy is consistent with the models of Leiter and Maslach (1988) and Lee and Ashforth (1993b); both of which proposed that emotional exhaustion is the starting point of burnout symptoms. Although these studies were conducted to examine career burnout, there is some evidence that academic burnout is similar to the professional burnout of service workers (Meier & Schmeck, 1985; Ramist, 1981). Our findings thus imply that academic burnout is similar to career burnout in that both start with emotional exhaustion. In other words, a high level of emotional exhaustion is related to high levels of cynicism and academic inefficacy. Additional evidence for emotional exhaustion being the starting point of burnout has been found in previous research (Taris et al., 1999). Taris et al. (1999) found that job demand was positively related with emotional exhaustion but not with the two other dimensions. Considering these results and the heavy Stress Health 31: 281–289 (2015) © 2014 John Wiley & Sons, Ltd.
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academic demands that are placed on Korean middle school students, emotional exhaustion could be expected to occur before the other burnout symptoms emerge. This assumption could be induced from our findings that the initial status of academic inefficacy did not have a significant impact on the change rate of emotional exhaustion, despite the fact that higher levels of initial emotional exhaustion significantly led to greater change rates for academic inefficacy. These findings suggest the possibility that emotional exhaustion, as an initial event, could contribute to the progress of academic inefficacy. Additionally, we found indications that higher initial levels of cynicism were associated with lower change rates for emotional exhaustion and academic inefficacy over time. This result indicates that students who had a high score on the cynicism dimension during the first semester period demonstrated a lower rate of increase for emotional exhaustion and academic inefficacy over the four semesters period. This suggests that when students were cynical about their studies, they were no longer exhausted but felt inefficient. Similarly, Cordes and Dougherty (1993) reviewed literatures related to job burnout and found that emotional exhaustion is the first stage and then cynicism is employed as a coping strategy. In other words, a high level of emotional exhaustion is more relative to the occurrence of cynicism than vice versa. This result is in line with the findings of Faber (2000), which proposes that teachers who are ‘underchallenged’ have cynical attitudes towards their students. These teachers will tend not to become emotionally exhausted and will also have a low level of reduced personal accomplishment because they did not have the initial energy or enthusiasm for their work, including enthusiasm about their students. Therefore, on the basis of previous studies and the finding of this study, it may be deduced that a high level of cynicism contributes to the slower growth of slopes for emotional exhaustion and academic inefficacy. The present study has several limitations. Firstly, all measures used in this study were self-reported questionnaires. Using this method may have led to overestimation or underestimation of burnout phenomena. Secondly, this study included only students living in South Korea. Education and academic ability levels may be different in other countries. Therefore, our conclusions are limited because results cannot be generalized to all middle school students around the world. Further research needs to determine whether or not significant similarities or differences exist in academic burnout for students in other nations and world regions. Lastly, the present study did not include the academic demands variable examining the relationship between emotional exhaustion and academic demands in Korean middle school students. Future research is necessary to examine whether overloaded academic demands placed on Korean middle school students trigger emotional exhaustion longitudinally. 287
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Despite these limitations, our study has several implications. Firstly, this study focused on the intrachanges of the three dimensions of academic burnout. Most existing studies have only focused on interpersonal differences. Secondly, this study to some extent provides evidence to support the developmental process models of Leiter and Maslach (1988) and Lee and Ashforth (1993b), in that it indicates that higher initial levels of exhaustion lead to higher change rates of cynicism. We can also conclude that higher initial levels of exhaustion trigger higher change rates for academic inefficacy. These results imply that emotional exhaustion is the key dimension that forms the starting point of academic burnout symptoms. By examining the relationships between emotional exhaustion and the other dimensions of academic burnout, this study highlights the importance of understanding and focusing on emotional exhaustion. In addition, LGM has statistical strengths to explore intra-individual or intrapersonal differences and the trajectory of growth of academic burnout. Middle school students experience psychological growth and change. LGM can assess the intra-individual differences as well as the change. We were also aware of few studies that had examined the growth and change of the three dimensions of academic burnout in middle school students. Middle
ances structure modeling. Journal of Organizational Behavior, 16, 201–213. Brouwers, A., & Tomic, W. (2000). A longitudinal study of teacher burnout and perceived self-efficacy in classroom management. Teaching and Teacher Education, 16, 239–254. Butler, S., & Charles, M. (1999). The past, the present, but never the future: Thematic representations of fostering disruption. Child and Family Social Work, 4, 9–19. Byrne, B. M., & Crombie, G. (2003). Modeling and testing change: an introduction to the latent growth curve model. Understanding Statistics, 2, 177–203. Cordes, C., & Dougherty, T. W. (1993). A review and integration of research on job burnout. Academy of Management Review, 18, 621–656. bustness of test statistics to normality and specificaconfirmatory
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a
multi-
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Longitudinal Analysis of Academic Burnout in Korean Middle School Students Boyoung Kim, Minyoung Lee, Keunhwa Kim, Hyunju Choi & Sang Min Lee*† Department of Education, College of Education, Korea University, Seoul, Korea
Abstract The purpose of the study was to investigate the longitudinal relationships between the initial values and slopes of three dimensions of burnout syndrome (i.e. emotional exhaustion, cynicism and academic inefficacy). The study utilized four-wave longitudinal data from a total of 367 (81.6% response rate) middle school students in South Korea. Comprising a 6-month interval survey, the first survey was conducted in June 2010, the second in December 2010, the third in June 2011 and the fourth in December 2011. All participants were 13-year-olds at the first and second surveys, and 14-year-olds at the third and fourth surveys. The Maslach Burnout Inventory—Student Survey was used for each survey to assess the level of academic burnout. The longitudinal data were analysed using latent growth modelling. The results of the study indicated that high initial values (intercept) for emotional exhaustion were associated with a higher rate of increase (slope) in cynicism and academic inefficacy. On the other hand, high initial values for cynicism and academic inefficacy were associated with a lower rate of increase in the other dimensions. This longitudinal study should promote understanding of burned-out students and contribute to the literature by informing the design of prevention programmes for academic burnout. Copyright © 2014 John Wiley & Sons, Ltd. Received 14 January 2013; Revised 15 August 2013; Accepted 14 October 2013 Keywords academic burnout; longitudinal analysis; latent growth model *Correspondence Sang Min Lee, Department of Education, College of Education, Korea University, Anam-dong, Seongbuk-gu, Seoul, Korea. † E-mail: [email protected] Published online 23 January 2014 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/smi.2553
The pressure to perform successfully at school is a heavy burden for adolescents. Particularly in South Korea, there is a strong culture of expectation and aspiration to accomplish high academic achievement (Lee, Puig, Kim, Shin, Lee, & Lee, 2010). Most Korean adolescents give a great deal of time to their studies, and as a result, they suffer from extreme academic stress due to excessive competition as well as school work (Lee, Puig, Lea, & Lee, 2013). Hence, the academic stress accrued from elementary to high school is the primary factor for chronic academic burnout. The concept of ‘burnout’ was first introduced by Freudenberger (1974), who defined the term as ‘to fail, to wear out, or become exhausted by making excessive demands on energy, strength, or resources’ (p. 159). Research on burnout syndrome has been popularized in line with the development of the Maslach Burnout Inventory (MBI; Maslach & Jackson, 1981). This tool interprets burnout as a psychological syndrome that arises in response to chronic job stressors (Maslach, Schaufeli, & Leiter, 2001). Whereas early burnout research focused on human service professionals, recent burnout studies have been applied to other groups such as parents and students (Brouwers & Stress Health 31: 281–289 (2015) © 2014 John Wiley & Sons, Ltd.
Tomic, 2000; Butler & Charles, 1999; Lee et al., 2010; Tomic, Tomic, & Evers, 2004). Since students’ main activities at school can be considered as a form of work, ‘academic burnout’ exists as a response to students’ continuing difficulties in coping with multiple achievement pressures (Salmela-Aro, Kiuru, Leskinen, & Nurmi, 2009b). Students need to attend regular and structured classes and complete their assignments every day. This performance is also reflected on their school records. In addition, they also receive penalties and compensation such as flunking or a good grade. These class activities and assessment activities are comparable with the work of formal employees. That is, students experience burnout just as formal workers do when they must carry a heavy academic load (Lee, 2010). School is a context in which students work (Samela-Aro, Hannu, & Holopainen, 2009). The Maslach Burnout Inventory—Student Survey (MBI-SS; Schaufeli, Martínez, Pinto, Salanova, & Bakker, 2002), which was developed for measuring students’ academic burnout, construes academic burnout as a multidimensional construct consisting of emotional exhaustion, cynicism and inefficacy. Emotional exhaustion signifies the feeling of being 281
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overextended and depleted of resources, representing the basic individual stress dimension of burnout. Cynicism signifies cynical, distant attitudes and feelings about one’s tasks. Inefficacy signifies the tendency to take a pessimistic view of one’s achievement at work (Maslach & Schaufeli, 1993). Specifically, ‘exhausted’ students feel emotionally drained by their studies. Students who experience ‘cynicism’ have become less enthusiastic about their studies. Students who believe ‘I make an effective contribution to the classes’ experience less ‘inefficacy’. Over the past two decades, relationships among three dimensions of burnout syndrome have been of interest to researchers (e.g. Golembiewski, Munzenrider, & Stevenson, 1986; Lee & Ashforth, 1993a; Lee & Ashforth, 1993b; Leiter & Maslach, 1988; Parker & Salmela-Aro, 2011; Schaufeli & Enzmann, 1998; Taris, Le Blanc, Schaufeli, & Schreurs, 2005). The possibility of causal relationships among three dimensions of burnout has been examined. As a result, several developmental process models of burnout have been suggested. The best known burnout process model asserted that emotional exhaustion is the primary stage of burnout (Leiter & Maslach, 1988), which then engenders the development of cynicism as an ineffective coping strategy as well as the accumulation of feelings of incompetence (Leiter, 1989). In this model, high levels of emotional exhaustion would trigger high levels of cynicism, which would in turn lead to high levels of incompetence (Taris et al., 2005). Different from the initial developmental burnout process model by Leiter and Maslach (1988), Golembiewski (1989) suggested that the cynicism dimension is the initial component, which is then followed by feelings of inefficiency, with emotional exhaustion emerging in the more damaging phase of burnout. That is, cynicism, as a dysfunctional attempt to cope with job stress, leads workers to develop feelings of inefficacy. Moreover, they become emotionally exhausted because they feel that their heavy workloads are out of proportion to their capacity. There are some integrated models that are combined with previous developmental burnout process models (Lee & Ashforth, 1993b; Taris et al., 2005). Lee and Ashforth’s developmental burnout model is somewhat similar to the model of Leiter and Maslach in that emotional exhaustion triggers cynicism; however, it differs in that feelings of inefficacy develop independently from cynicism, meaning that emotional exhaustion directly affects inefficiency and does not go through cynicism. In addition, Taris’s model determined that emotional exhaustion affects feelings of inefficacy through cynicism, both directly and indirectly, combining the model of Leiter and Maslach with that of Lee and Ashforth. Because most studies (e.g. Golembiewski et al., 1986; Leiter & Maslach, 1988) were of cross-sectional nature, it is important to examine the relationship among three dimensions of burnout longitudinally. If we trace the changes of the three component of burnout in longitudinal data analysis, we can observe the relationship 282
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among them in terms of the intrapersonal aspect. Nevertheless, a notable paucity of research exists on the relationship among three dimensions of burnout from a longitudinal perspective. Furthermore, few research studies of the developmental model in academic burnout have been conducted (Galbraith & Merrill, 2012; Shin, 2012). Research on the initial values (first-wave data point) and slope (change rates) of each dimension provides information not only on the simple developmental processes but also on the particular direction required to develop future intervention. For example, if a high initial value for emotional exhaustion is found to trigger greater rate of change for cynicism and inefficacy, emotional exhaustion should be the primary focus of treatment because it is the cause of the intensified burnout. By reducing emotional exhaustion, the foreseeable latent consequences of burnout, namely cynicism and inefficacy symptoms, could be prevented. Thus, the purpose of this study is to investigate the growth and the relationships between the initial values and slopes of the three dimensions of academic burnout syndrome. Aims of the study The present study explores the longitudinal relationships among three dimensions of academic burnout and the growth trajectories in Korean middle school students. Specifically, we examined the effect of initial values of one dimension on change rate of other dimensions to derive implications of the developmental process of academic burnout. The following hypotheses were examined in this study. Firstly, we hypothesized that three dimensions of academic burnout would increase across school years. Lee (2010) conducted mean difference tests of latent variables (i.e. inefficacy, cynicism, antipathy and exhaustion) in order to assess academic burnout of Korean students. Following the results of Lee’s (2010) study, we hypothesized that academic burnout would increase in Korean middle school students over time. However, we do not yet hold a specific hypothesis in terms of the linear or nonlinear growth, which can represent individual change. Rather, we examined how three dimensions of academic burnout grew across school years of middle school students by testing whether the growth pattern would exhibit either a linear or non-linear growth trajectory in academic burnout, and we conducted latent growth model analysis (Duncan, Duncan, Stycker, Li, & Alpert, 1999; Muthén & Muthén, 2006). Secondly, intra-individual or intrapersonal differences would exist in three dimensions of academic burnout across the school years. Most previous studies have only focused on interpersonal differences of burnout because of cross-sectional samples. Intra-individual differences can be estimated by conducting latent growth modelling (LGM) with a longitudinal design. We established a research model using LGM to test Stress Health 31: 281–289 (2015) © 2014 John Wiley & Sons, Ltd.
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statistically significant variances that mean intraindividual differences. Thirdly, we hypothesized that higher levels of emotional exhaustion would trigger higher change rates for cynicism and academic inefficacy. Emotional exhaustion is believed to be the primary stage of burnout among its three components (Lee & Ashforth, 1993b; Leiter & Maslach, 1988; Taris, Schreurs, & Schaufeli, 1999). That is, high levels of emotional exhaustion predict high levels of cynicism or inefficacy. If these results are valid in the academic burnout phenomenon, high levels of emotional exhaustion will lead to the development of high levels of cynicism or academic inefficacy. Therefore, we expected that higher initial levels of emotional exhaustion would be significantly associated with greater change rates for cynicism and academic inefficacy. As shown in Figure 1, it was expected that higher levels of ‘EE i’ would trigger higher levels of ‘CY s’ and ‘AI s’. Finally, we hypothesized that higher levels of cynicism would have negative relationships with change rates for emotional exhaustion and academic inefficacy. According to Faber (2000), teachers who had cynicism tend not to be exhausted and not to feel inefficacy. We hypothesized that cynicism of academic burnout would be similar to the result of Faber (2000) because academic burnout reveals similar features with job burnout. In addition to the previous study, we explore the longitudinal relationships of cynicism with the other two dimensions. As shown in Figure 1, it was expected that higher levels of ‘CY i’ would have negative relationships with ‘EE s’ and ‘AI s’.
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Method Participants and procedure The sample consists of South Korean adolescents (7th graders in urban communities), and the four waves of data for 2 years were collected at one semester intervals. Just before the final semester exam was considered as the proper moment for measurement of academic burnout because this time is expected to be the highest level of academic stress. This comprised a 6-month interval survey, which led to the first survey being conducted in June 2010, the second in December 2010, the third in June 2011 and the fourth in December 2011. Survey packets containing informed consent, demographic questions and one copy of MBI-SS were distributed to potential participant. Students voluntarily completed the survey instrument during class following a homeroom teacher’s instructions. To protect confidentiality, students used their school identification numbers, by which students were tracked across periods. At the first survey, 412 packets of the 450 were returned. Of these, 45 packets were incomplete surveys without responses to five or more items, which were excluded from the data analyses. After exclusions, 367 surveys (81.6% response rate) were included in the final sample. As for characteristics of the sample, 56% was female (N = 207). No significant differences on academic burnout level between male and female were observed. Participants’ age was not a concern because this was drawn from the same age group. That is, all
Figure 1. The research model Stress Health 31: 281–289 (2015) © 2014 John Wiley & Sons, Ltd.
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participants were 13-year-olds at the first and second measurements, and 14-year-olds at the third and fourth measurements. The participation rate from the second survey to fourth survey was favourable (98%, 92% and 97%, respectively). Tests for differences between completers and dropouts were conducted in terms of gender, parents’ education level and burnout values at Time 1. As a result, statistically significant differences were not found. In order to manage missing data, we tested whether a data set was missing completely at random (MCAR) or not. The result of Little’s (1998) MCAR test accepted the null hypothesis (χ 2 = 75.41, p = 0.37), which means a suspicious pattern or distribution in missing data was not detected. So, full information maximum likelihood estimation, which combines parameter estimation with missing data analysis (Parker & Salmela-Aro, 2011), was employed. Measure Maslach Burnout Inventory—Student Survey The Korean version of the MBI-SS (Shin, Puig, Lee, Lee, & Lee, 2011) was used to assess students’ level of academic burnout. The MBI-SS scale contains three dimensions: emotional exhaustion (five items), cynicism (four items) and academic efficacy (six items). Although there was criticism about the dimensions of MBI, which comprise exhaustion, depersonalization and cynicism (Kristensen, Borritz, Villadsen, & Christensen, 2005), Schaufeli and Taris (2005) asserted that burnout should be conceptualized as a work-related phenomenon of at least two dimensions (fatigue and withdrawal) that can be applied to exhaustion and depersonalization/cynicism. According to Shin et al. (2011), who conducted cultural validation of the Korean version of MBI-SS, a better model fit was shown in the three-factor model, which implies a multidimensional construct of burnout, than in the onefactor model, which regards burnout as a homogenous phenomenon. This result signifies that the academic burnout phenomenon can be interpreted well on the basis of the three-factor model, and it is in line with previous burnout studies for counsellors and police managers (Demerouti, Verbeke, & Bakker, 2005; Loo, 2004). Because Shin et al. (2011) used a sample composed of middle school and high school students for validation, the Korean version of the MBI-SS was expected to be appropriate for the present sample, composed of middle school students. For these reasons, we employed each factor of burnout as a latent variable in the present study. Respondents were asked to rate items on a scale from 1 (strongly disagree) to 5 (strongly agree). Higher scores indicate greater degree of burnout. The reliability coefficients for these dimensions were as follows: emotional exhaustion (e.g. I feel emotionally drained by my studies., αT1 = 0.83, αT2 = 0.87, αT3 = 0.87, αT4 = 0.89), cynicism (e.g. I have become less enthusiastic about my studies., αT1 = 0.82, αT2 = 0.83, αT3 = 0.83, αT4 = 0.85) 284
and academic efficacy (e.g. I believe that I make an effective contribution to the classes that I attend., αT1 = 0.83, αT2 = 0.84, αT3 = 0.83, αT4 = 0.85). As such, the coding for academic efficacy was reversed. Data analysis Descriptive statistics were used for basic understanding of burnout symptoms, and intercorrelations of the three dimensions were used to examine the relationships within burnout, using PASW 18.0 (IBM, Armonk, New York, US). Next, to assess the relationships among the three dimensions of burnout over time, the data were analysed using LGM (Duncan et al., 1999; Muthén & Muthén, 2006) as implemented in the AMOS 18.0 package (IBM, Armonk, New York, US). Full information maximum likelihood was used to correct for the presence of any missing data. Inspection of the univariate distributions of the variables revealed no substantive deviations from normality. As for multivariate normality, tests of multivariate kurtosis (Media, 1970) turned out that the present sample could deviate from multivariate normality (e.g. b2p = 27.59, p = 0.000). Nevertheless, the plot of ordered squared distances illustrated a linear pattern, and the value of Mahalanobis distance (D2) indicated that there was no multivariate outlier. Accordingly, LGM analysis was conducted. Latent growth modelling combines features of structural equation modelling, which incorporates latent variables, and hierarchical linear modelling (Raudenbush & Bryk, 2002), which allows random coefficients across individual developmental trajectories. LGM enables analyses of associations between change over time in the independent and dependent variables of interest (Simons-Morton, Chen, Abroms, & Haynie, 2004). In LGM, the basic measurement model has two latent constructs: one represents the intercept (initial status) for the construct, and the other represents the slope (rate of change) for the construct (Wills & Cleary, 1999). As such, LGM could present a general overview of the measurement of individual change in burnout over the four semesters period (i.e. intra-individual change) and differences in such change across all subjects (i.e. inter-individual change). The longitudinal research model is made up of three parts: emotional exhaustion, cynicism and academic inefficacy. To examine the growth pattern of each part (linear or non-linear), linearity of growth was separately analysed in three models. After this, to investigate the relationship among the three burnout components, the research model was analysed, and several types of model fit were provided: chi-square (χ 2) test, Tucker–Lewis index, comparative fit index (CFI) and root mean square error approximation (RMSEA).
Results Descriptive statistics and the intercorrelations for the three dimensions of academic burnout are presented in Table I. The means of emotional exhaustion were MT1 = 2.56 [standard deviation (SD) = 0.89], MT2 = 2.59 Stress Health 31: 281–289 (2015) © 2014 John Wiley & Sons, Ltd.
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(SD = 0.88), MT3 = 2.81 (SD = 0.90) and MT4 = 2.71 (SD = 0.90). Those of cynicism were MT1 = 2.43 (SD = 0.93), MT2 = 2.45 (SD = 0.91), MT3 = 2.54 (SD = 0.93) and MT4 = 2.68 (SD = 0.95), and those of academic inefficacy were MT1 = 2.87 (SD = 0.74), MT2 = 2.85 (SD = 0.74), MT3 = 2.88 (SD = 0.71) and MT4 = 2.92 (SD = 0.73). Skewness and kurtosis were within the cut-off points for normality for all scales for the four measured waves (Curran, West, & Finch, 1997). The range of intercorrelations among the three burnout dimensions varied from 0.28 to 0.68 over the study period, and all coefficients were significant (p < 0.001). Repeated-measures analysis of variance was conducted as a preliminary analysis to test whether changes of academic burnout over time were statistically significant. Figure 2 shows the levels of the three dimensions of academic burnout changing across the four time points. Repeated-measures analysis of variance of academic burnout scores showed significant time effects (F = 14.25, p < 0.001) and dimensions of academic burnout–time interaction (F = 3.48, p < 0.01). Therefore, LGM was conducted for more specific analyses on the basis of this result. To test whether the data could be best described by linear or non-linear growth, two models of each burnout dimensions were tested. In the linear model, factor loadings for the slope parameter were fixed to correspond to a linear time scale (0, 1, 2 and 3). In the non-linear model, the slope parameter was freely estimated. In order to better identify the model, at least two factor loadings on the slope parameter must be fixed to two different values (Meredith & Tisak, 1990). Therefore, the first two parameters
Figure 2. Repeated-measures analysis of variance of academic burnout scores
were fixed at 0 and 1, whereas the other two parameters were estimated freely. As a result, the linear model was more adequate for data interpretation than the non-linear model, since errors in variance were detected in the non-linear model but not in the linear model. This result indicates that the growth of the three dimensions of academic burnout had a linear tendency. Before we analysed the research model, the growth of each sub-component of burnout was examined. Table II shows the growth of the three variables. All intercept and slope means were significant except for the slope mean of academic inefficacy. These reveal that the mean level of burnout in this sample progressed in the measuring stages except for academic inefficacy. Table II also shows that all variances of intercepts and slopes were significant
Table I. Descriptive statistics and intercorrelations of variables across four waves Time 1
Time 2
Time 3
Time 4
Emotional Academic Emotional Academic Emotional Academic Emotional Academic exhaustion Cynicism inefficacy exhaustion Cynicism inefficacy exhaustion Cynicism inefficacy exhaustion Cynicism inefficacy EE_T1 CY_T1 AI_T1 EE_T2 CY_T2 AI_T2 EE_T3 CY_T3 AI_T3 EE_T4 CY_T4 AI_T4 Mean SD Skewness Kurtosis
1 0.56*** 0.43*** 0.62*** 0.45*** 0.39*** 0.46*** 0.39*** 0.35*** 0.40*** 0.47*** 0.35*** 2.56 0.89 0.25 0.20
1 0.62*** 0.49*** 0.63*** 0.56*** 0.34*** 0.59*** 0.46*** 0.24*** 0.50*** 0.48*** 2.43 0.93 0.36 0.24
1 0.40*** 0.51*** 0.67*** 0.24*** 0.43*** 0.55*** 0.22*** 0.37*** 0.57*** 2.87 0.74 0.07 0.09
1 0.61*** 0.46*** 0.47*** 0.48*** 0.39*** 0.46*** 0.49*** 0.41*** 2.59 0.88 0.08 0.31
1 0.57*** 0.35*** 0.62*** 0.46*** 0.26*** 0.54*** 0.50*** 2.45 0.91 0.22 0.49
1 0.28*** 0.48*** 0.64*** 0.29*** 0.47*** 0.62*** 2.85 0.74 0.00 0.13
1 0.55*** 0.43*** 0.62*** 0.48*** 0.40*** 2.81 0.90 0.02 0.42
1 0.55*** 0.41*** 0.67*** 0.54*** 2.54 0.93 0.33 0.20
1 0.34*** 0.49*** 0.68*** 2.88 0.71 0.32 0.46
1 0.55*** 0.38*** 2.71 0.90 0.05 0.41
1 0.58*** 2.68 0.95 0.23 0.27
2.92 0.73 0.01 0.57
EE: emotional exhaustion; CY: cynicism; AI: academic inefficacy; T: measurement time; SD: standard deviation. ***p < 0.001. Stress Health 31: 281–289 (2015) © 2014 John Wiley & Sons, Ltd.
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in the research model. This suggests a rationalization for including predictor variables in the model (Byrne & Crombie, 2003). The research model has a total of six latent constructs because academic burnout has three dimensions, each of which has two latent constructs (initial status and slope). This model hypothesized that an initial status in one dimension of academic burnout is related to change in the rate of the other two dimensions of burnout. To explore the relationship among the three components of the burnout symptoms, the research model was specified in terms of longitudinal conceptualization. In the linear model, the chi-square (χ 2 = 126.03, degrees of freedom = 45, p < 0.001) was significant. However, chi-square tends to be easily significant with large sample sizes (Brannick, 1995) even when all other indices of fit indices display a good fit. For this reason, other indices of fit were examined. CFI was 0.97, non-normed fit index (NNFI) was 0.95 and RMSEA was 0.07 (90% CI [0.05, 0.08]). Hu and Bentler (1999) suggested that for continuous data, NNFI (also known as Tucker–Lewis index) > .95, CFI 0.95 and RMSEA < 0.08. On the basis of Hu and Bentler’s criteria, as shown in Table II, the data met the necessary assumptions for analysis since the measurement model had a reasonable fit. Parameter estimates for the latent growth models of emotional exhaustion, cynicism and academic inefficacy are presented in Figure 3. The path from the emotional exhaustion intercept to the cynicism slope was significant (ß = 0.96, p < 0.001); it was also significant (ß = 0.98, p < 0.001) to the academic inefficacy slope. The path from the cynicism intercept to the emotional exhaustion slope was significant (ß = 0.52, p < 0.05) and likewise to the academic inefficacy slope (ß = 0.73, p < 0.01). The path from the academic inefficacy intercept to the emotional exhaustion slope was positive but non-significant (ß = 0.35, ns), whereas it was significant (ß = 0.75, p < 0.01) to the cynicism slope. These results demonstrate that a high initial value (intercept) for emotional exhaustion leads to a higher rate of increase (slope) in cynicism and academic inefficacy. On the other hand, high initial
Figure 3. Path coefficients from intercept to slope of measurement model
values for cynicism and academic inefficacy were associated with a lower rate of increase in the other dimensions.
Discussion The main purpose of our study was to explore the possible relationships among the three dimensions of MBI by examining the initial status and the change rates of burnout symptoms. Firstly, using LGM analysis, we tested two models of each burnout dimension to examine whether the data fitted with a linear or non-linear growth model. We found that the linear model was the best fit for the data. This result revealed that dimensions of academic burnout were growing linearly during the middle school period. This result supported the first hypothesis, which stated that ‘the three dimensions of academic burnout would increase across school years’. According to Lee (2010), academic burnout symptoms of Korean students grew over time. She
Table II. Parameter estimates for latent growth models of emotional exhaustion, cynicism and academic inefficacy Emotional exhaustion
Intercept: mean Variance Slope: mean Variance Covariance intercept slope
Cynicism
B
SE
CR
12.88*** 14.36*** 0.31*** 1.63*** 2.36***
0.22 1.49 0.09 0.26 0.51
59.01 9.64 3.64 6.18 4.66
B 9.59*** 9.53*** 0.37*** 0.61*** 0.82***
Academic inefficacy
SE
CR
B
SE
CR
0.18 1.00 0.06 0.15 0.31
52.92 9.53 5.81 4.02 2.65
17.15*** 13.54*** 0.12*** 0.71*** 1.02***
0.21 1.38 0.07 0.19 0.40
80.12 9.80 1.72 3.68 2.56
T: measurement time; SE: standard error, CR: Critical ratio. ***p < 0.001, **p < 0.01, *p < 0.05.
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conducted mean difference tests of latent variables (i.e. inefficacy, cynicism, antipathy and emotional exhaustion) in order to assess growth of academic burnout. However, her conclusion was not deduced from a longitudinal research design. Salmela-Aro et al. (2009) found that early career burnout increases linearly in a university student sample. Similarly, a Korean middle school student sample also exhibited a linear growth of academic burnout. In addition, variances of the linear model for each dimension were statistically significant. The result supported the second hypothesis, which stated that ‘intra-individual or intrapersonal differences would exist in three dimensions of academic burnout across the school years’. Significant variances in the intercepts of the three dimensions of academic burnout indicated that substantial individual differences existed at baseline. This is that some students have higher scores on the three dimensions of academic burnout whereas others have lower scores at Time 1. Significant variances in slopes revealed intra-individual differences in the probability of progressing in academic burnout over time. This result indicated that each individual has different change slopes. It could be interpreted that three dimensions of academic burnout of some students increased by different rates compared with academic burnout dimensions of others over the same period. This is that some students burnt out over this time whereas others did not. Therefore, intraindividual or intrapersonal differences existed in the three dimensions of academic burnout in this study, and it was appropriate to conduct LGM for considering these intra-individual or intrapersonal differences. In the research model, higher initial levels of emotional exhaustion are significantly associated with greater change rates for cynicism and academic inefficacy. Thus, it can be suggested that emotional exhaustion is the starting point of academic burnout. Accordingly, the result that a higher level of emotional exhaustion triggers higher change rates for cynicism and academic inefficacy is consistent with the models of Leiter and Maslach (1988) and Lee and Ashforth (1993b); both of which proposed that emotional exhaustion is the starting point of burnout symptoms. Although these studies were conducted to examine career burnout, there is some evidence that academic burnout is similar to the professional burnout of service workers (Meier & Schmeck, 1985; Ramist, 1981). Our findings thus imply that academic burnout is similar to career burnout in that both start with emotional exhaustion. In other words, a high level of emotional exhaustion is related to high levels of cynicism and academic inefficacy. Additional evidence for emotional exhaustion being the starting point of burnout has been found in previous research (Taris et al., 1999). Taris et al. (1999) found that job demand was positively related with emotional exhaustion but not with the two other dimensions. Considering these results and the heavy Stress Health 31: 281–289 (2015) © 2014 John Wiley & Sons, Ltd.
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academic demands that are placed on Korean middle school students, emotional exhaustion could be expected to occur before the other burnout symptoms emerge. This assumption could be induced from our findings that the initial status of academic inefficacy did not have a significant impact on the change rate of emotional exhaustion, despite the fact that higher levels of initial emotional exhaustion significantly led to greater change rates for academic inefficacy. These findings suggest the possibility that emotional exhaustion, as an initial event, could contribute to the progress of academic inefficacy. Additionally, we found indications that higher initial levels of cynicism were associated with lower change rates for emotional exhaustion and academic inefficacy over time. This result indicates that students who had a high score on the cynicism dimension during the first semester period demonstrated a lower rate of increase for emotional exhaustion and academic inefficacy over the four semesters period. This suggests that when students were cynical about their studies, they were no longer exhausted but felt inefficient. Similarly, Cordes and Dougherty (1993) reviewed literatures related to job burnout and found that emotional exhaustion is the first stage and then cynicism is employed as a coping strategy. In other words, a high level of emotional exhaustion is more relative to the occurrence of cynicism than vice versa. This result is in line with the findings of Faber (2000), which proposes that teachers who are ‘underchallenged’ have cynical attitudes towards their students. These teachers will tend not to become emotionally exhausted and will also have a low level of reduced personal accomplishment because they did not have the initial energy or enthusiasm for their work, including enthusiasm about their students. Therefore, on the basis of previous studies and the finding of this study, it may be deduced that a high level of cynicism contributes to the slower growth of slopes for emotional exhaustion and academic inefficacy. The present study has several limitations. Firstly, all measures used in this study were self-reported questionnaires. Using this method may have led to overestimation or underestimation of burnout phenomena. Secondly, this study included only students living in South Korea. Education and academic ability levels may be different in other countries. Therefore, our conclusions are limited because results cannot be generalized to all middle school students around the world. Further research needs to determine whether or not significant similarities or differences exist in academic burnout for students in other nations and world regions. Lastly, the present study did not include the academic demands variable examining the relationship between emotional exhaustion and academic demands in Korean middle school students. Future research is necessary to examine whether overloaded academic demands placed on Korean middle school students trigger emotional exhaustion longitudinally. 287
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Despite these limitations, our study has several implications. Firstly, this study focused on the intrachanges of the three dimensions of academic burnout. Most existing studies have only focused on interpersonal differences. Secondly, this study to some extent provides evidence to support the developmental process models of Leiter and Maslach (1988) and Lee and Ashforth (1993b), in that it indicates that higher initial levels of exhaustion lead to higher change rates of cynicism. We can also conclude that higher initial levels of exhaustion trigger higher change rates for academic inefficacy. These results imply that emotional exhaustion is the key dimension that forms the starting point of academic burnout symptoms. By examining the relationships between emotional exhaustion and the other dimensions of academic burnout, this study highlights the importance of understanding and focusing on emotional exhaustion. In addition, LGM has statistical strengths to explore intra-individual or intrapersonal differences and the trajectory of growth of academic burnout. Middle school students experience psychological growth and change. LGM can assess the intra-individual differences as well as the change. We were also aware of few studies that had examined the growth and change of the three dimensions of academic burnout in middle school students. Middle
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