A developmental model of job burnout dimensions among primary school teachers: Evidence from structural equation model and cross-lagged panel network model

doi:10.3724/SP.J.1041.2022.00371

Abstract

Abstract:

Emotional exhaustion, depersonalization, and reduced personal accomplishment of teacher burnout are relatively independent but also have mutual influences. Research into their developmental relationship is helpful in understanding the developmental process and identifying the early symptoms of job burnout. A total of 3837 primary school teachers took part in this two-wave longitudinal study with intervals of three years. The structural equation model and cross-lagged network model were used for analysis. The results showed that the optimal development model of teacher burnout was “emotional exhaustion and reduced personal accomplishment at T1 predict emotional exhaustion and reduced personal accomplishment at T2, respectively, and depersonalization at T1 predicts depersonalization and reduced personal accomplishment at T2”. There was no gender difference or teaching experience difference in the optional development model. The results emphasize the important role of depersonalization in the development of teacher burnout and have certain theoretical and practical significance for identifying the early symptoms of teacher burnout and for taking corresponding measures to effectively prevent further teacher burnout.

Key words: primary school teachers, job burnout, structural equation model, cross-lagged panel network model, development model

XIE Min, LI Feng, LUO Yuhan, KE Li, WANG Xia, WANG Yun. (2022). A developmental model of job burnout dimensions among primary school teachers: Evidence from structural equation model and cross-lagged panel network model. Acta Psychologica Sinica, 54(4), 371-384.

Figures/Tables 12

References 45

[1]	Bakker, A. B., Schaufeli, W. B., Sixma, H. J., Bosveld, W., & Van Dierendonck, D. (2000). Patient demands, lack of reciprocity, and burnout: A five-year longitudinal study among general practitioners. Journal of Organizational Behavior, 21(4), 425-441. doi: 10.1002/(SICI)1099-1379(200006)21:4<>1.0.CO;2-# URL
[2]	Bi, Z. Z., & Huang, X. T. (2005). The role of burnout in the relationship between achievement motivation and turnover intention. Journal of Psychological Science, 28(1), 28-31.
[3]	Borsboom, D., & Cramer, A. O. J. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9(1), 91-121. doi: 10.1146/clinpsy.2013.9.issue-1 URL
[4]	Brenner, J. (2020). Examining the stage progression of employee burnout (Order No. 28148693). Available from ProQuest Dissertations & Theses Global; Publicly Available Content Database. ( 2451849425).
[5]	Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
[6]	Cramer, A. O. J., van Borkulo, C. D., Giltay, E. J., van der Maas, H. L. J., Kendler, K. S., Scheffer, M., & Borsboom, D. (2016). Major depression as a complex dynamic system. PLoS One, 11(12), 1-20.
[7]	Cramer, A. O. J., van der Sluis, S., Noordhof, A., Wichers, M., Geschwind, N., Aggen, S. H., ... Borsboom, D. (2012). Dimensions of normal personality as networks in search of equilibrium: You can’t like parties if you don’t like people. European Journal of Personality, 26(4), 414-431. doi: 10.1002/per.1866 URL
[8]	Deboeck, P. R., & Preacher, K. J. (2016). No need to be discrete: A method for continuous time mediation analysis. Structural Equation Modeling, 23(1), 61-75. doi: 10.1080/10705511.2014.973960 URL
[9]	de Vos, J. A., Brouwers, A., Schoot, T., Pat-El, R., Verboon, P., & Näring, G. (2016). Early career burnout among Dutch nurses: A process captured in a Rasch model. Burnout Research, 3(3), 55-62. doi: 10.1016/j.burn.2016.06.001 URL
[10]	Diestel, S., & Schmidt, K.-H. (2010). Direct and interaction effects among the dimensions of the Maslach Burnout Inventory: Results from two German longitudinal samples. International Journal of Stress Management, 17(2), 159-180. doi: 10.1037/a0018967 URL
[11]	Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48(4), 1-18.
[12]	Fried, E. I., Epskamp, S., Nesse R. M, Tuerlinckx, F., & Borsboom, D. (2016). What are “good” depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis. Journal of Affective Disorders, 189, 314-320. doi: 10.1016/j.jad.2015.09.005 pmid: 26458184
[13]	Fried, E. I., van Borkulo, C. D., Cramer, A. O. J., Boschloo, L., Schoevers, R. A., & Borsboom, D. (2017). Mental disorders as networks of problems: A review of recent insights. Social Psychiatry and Psychiatric Epidemiology, 52(1), 1-10. doi: 10.1007/s00127-016-1319-z pmid: 27921134
[14]	Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33, 1-22. pmid: 20808728
[15]	Gan, T., & Gan, Y. (2014). Sequential development among dimensions of job burnout and engagement among IT employees. Stress and Health, 30(2), 122-133. doi: 10.1002/smi.v30.2 URL
[16]	Golembiewski, R. T., Munzenrider, R., & Carter, D. (1983). Phases of progressive burnout and their work site covariants: Critical issues in OD research and praxis. The Journal of Applied Behavioral Science, 19(4), 461-481. doi: 10.1177/002188638301900408 URL
[17]	Herman, K. C., Hickmon-Rosa, J., & Reinke, W. M. (2018). Empirically derived profiles of teacher stress, burnout, self-Efficacy, and coping and associated student outcomes. Journal of Positive Behavior Interventions, 20(2), 90-100. doi: 10.1177/1098300717732066 URL
[18]	Hou, K. T., Wen, Z. L., & Cheng, Z. J. (2004). Structural equation model and its applications. Beijing: Educational Science Publishing House.
[19]	Huang, J., Bao, X. H., You, X. Q., & Zhou, L. M. (2010). The mediation effects of personal resources on relationship between the job demand-resource model and job burnout. Journal of Psychological Science, 33(4), 963-965.
[20]	Huang, J., You, X.Q., Wang, Y. S., & Bao, X. H. (2015). A Longitudinal Analysis of the Developmental Process of Job Burnout. Journal of Psychological Science, 38(4), 911-915.
[21]	Huyghebaert, T., Fouquereau, E., Gillet, N., Beltou, N., & Tellier, F. (2018). Effects of workload on teachers’ functioning: A moderated mediation model including sleeping problems and overcommitment. Stress and Health: Journal of the International Society for the Investigation of Stress, 34(5), 601-611. doi: 10.1002/smi.v34.5 URL
[22]	Lazarus, R. S. (1966). Psychological stress and the coping process. New York: McGraw-Hill.
[23]	Lee, R. T., & Ashforth, B. E. (1993). A longitudinal study of burnout among supervisors and managers: Comparisons between the Leiter and Maslach (1988) and Golembiewski et al. (1986) Models. Organizational Behavior and Human Decision Processes, 54(3), 369-398. doi: 10.1006/obhd.1993.1016 URL
[24]	Leiter, M. P., & Maslach, C. (1988). The impact of interpersonal environment on burnout and organizational commitment. Journal of Organizational Behavior, 9(4), 297-308. doi: 10.1002/(ISSN)1099-1379 URL
[25]	Letina, S., Blanken, T. F., Deserno, M. K., & Borsboom, D. (2019). Expanding network analysis tools in psychological networks:Minimal spanning trees, participation coefficients, and motif analysis applied to a network of 26 psychological attributes. Complexity (New York, N.Y.), 2019, 1-27.
[26]	Maslach, C. (2003). Job burnout: New directions in research and intervention. Current Directions in Psychological Science, 12(5), 189-192. doi: 10.1111/1467-8721.01258 URL
[27]	Maslach, C. & Leiter, M. P. (2017). Understanding burnout: New models. In C. L. Cooper, & J. C. Quick (Eds.), The handbook of stress and health: A guide to research and practice (pp. 36-56, Chapter xxii). Wiley Blackwell.
[28]	Maslach, C., & Schaufeli, W. B. (1993). Historical and conceptual development of burnout. In W. B. Schaufeli, C. Maslach, & T. Marek (Eds.), Professional burnout: Recent developments in theory and research (pp.1-16). Washington, DC: Taylor & Francis.
[29]	McManus, I. C., Winder, B. C., & Gordon, D. (2002). The causal links between stress and burnout in a longitudinal study of UK doctors. The Lancet, 359, 2089-2090. doi: 10.1016/S0140-6736(02)08915-8 URL
[30]	Nguyen, H. T. T., Kitaoka, K., Sukigara, M., & Thai, A. L. (2018). Burnout Study of clinical nurses in Vietnam: Development of job burnout model based on Leiter and Maslach's theory. Asian Nursing Research, 12(1), 42-49. doi: S1976-1317(17)30279-7 pmid: 29463486
[31]	Nieminen, P., Lehtiniemi, H., Vhkangas, K., Huusko, A., & Rautio, A. (2013). Standardised regression coefficient as an effect size index in summarising findings in epidemiological studies. Epidemiology Biostatistics & Public Health, 10(4), 1-15.
[32]	Peterson, R. A., & Brown, S. P. (2005). On the use of beta coefficients in meta-analysis. Journal of Applied Psychology, 90(1), 175-181. pmid: 15641898
[33]	Rhemtulla, M., van Bork, R., & Cramer, A. O. J. (2019). Cross-laggedged network models. Multivariate Behavioral Research. Retrived from https://osf.io/r24q6/
[34]	Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1-36.
[35]	Savicki, V., & Cooley, E. J. (1994). Burnout in child protective service workers: A longitudinal study. Journal of Organizational Behavior, 15(7), 655-666. doi: 10.1002/(ISSN)1099-1379 URL
[36]	Schmittmann, V. D., Cramer, A. O. J., Waldorp, L. J., Epskamp, S., Kievit, R. A., & Borsboom, D. (2013). Deconstructing the construct: A network perspective on psychological phenomena. New Ideas in Psychology, 31(1), 43-53. doi: 10.1016/j.newideapsych.2011.02.007 URL
[37]	Taris, T. W., Le Blanc, P. M., Schaufeli, W. B., & Schreurs, P. J. (2005). Are there causal relationships between the dimensions of the Maslach Burnout Inventory? A review and two longitudinal tests. Work and Stress, 19(3), 238-255. doi: 10.1080/02678370500270453 URL
[38]	Toppinen-Tanner, S., Kalimo, R., & Mutanen, P. (2002). The process of burnout in white-collar and blue-collar jobs: Eight-year prospective study of exhaustion. Journal of Organizational Behavior, 23(5), 555-570. doi: 10.1002/(ISSN)1099-1379 URL
[39]	van Dierendonck, D., Schaufeli, W. B., & Buunk, B. P. (2001). Toward a process model of burnout: Results from a secondary analysis. European Journal of Work and Organizational Psychology, 10(1), 41-52 doi: 10.1080/13594320042000025 URL
[40]	Voelkle, M. C., Oud, J. H. L., Davidov, E., & Schmidt, P. (2012). An SEM approach to continuous time modeling of panel data: Relating authoritarianism and anomia. Psychological Methods, 17(2), 176-192. doi: 10.1037/a0027543 pmid: 22486576
[41]	Wang, F. (2006). Job burnout in university teachers: The causes and the internal relationship of the three dimensions (Unpublished doctoral dissertation). Beijing Normal University.
[42]	Wang, F., & Xu, Y. (2004). Job burnout in elementary and high school teachers: Characteristics and relationship with social support. Acta Psychologica Sinica, 36(5), 568-574.
[43]	Wu, X. C., Qi, Y. J., & Zang, W. W. (2019). Overall features and influencing factors of primary and secondary school teachers’ job burnout in China. Journal of South China Normal University (Social Science Edition), (1), 37-42.
[44]	Wu, X. C., Zeng, L. J., Qin, X. G., & Zheng, Q. (2003). The current situation and related factors of Chinese teacher’s burnout. Studies of Psychology and Behavior, 1(4), 262-267.
[45]	Zang, W. W. (2007). The research on the relationship among occupational stress, job burnout and coping style of elementary and high school teachers (Unpublished master’s thesis). Beijing Normal University.

Dimension	M	SD	2	3	4	5	6
1 Emotional exhaustion T1	2.67	0.98	0.62^**	0.04^*	0.47^**	0.29^**	0.09^**
2 Depersonalization T1	1.79	0.66		0.16^**	0.31^**	0.40^**	0.20^**
3 Reduced personal accomplishment T1	2.50	0.99			0.04^*	0.12^**	0.25^**
4 Emotional exhaustion T2	2.47	0.93				0.67^**	0.14^**
5 Depersonalization T2	1.78	0.72					0.31^**
6 Reduced personal accomplishment T2	2.22	0.84

Dimension	M	SD	2	3	4	5	6
1 Emotional exhaustion T1	2.67	0.98	0.62^**	0.04^*	0.47^**	0.29^**	0.09^**
2 Depersonalization T1	1.79	0.66		0.16^**	0.31^**	0.40^**	0.20^**
3 Reduced personal accomplishment T1	2.50	0.99			0.04^*	0.12^**	0.25^**
4 Emotional exhaustion T2	2.47	0.93				0.67^**	0.14^**
5 Depersonalization T2	1.78	0.72					0.31^**
6 Reduced personal accomplishment T2	2.22	0.84

Model	χ²	df	RMSEA	SRMR	CFI	TLI	AIC	aBIC
Basic model	160.63	6	0.08	0.05	0.96	0.93	24076.50	24113.40
Golembiewski (1983) model	49.45	4	0.05	0.03	0.99	0.97	23969.32	24012.36
Leiter & Maslach (1988) model	25.35	4	0.04	0.02	0.99	0.98	23945.22	23988.27
Lee & Ashforth (1993) model	99.28	4	0.08	0.03	0.98	0.93	24019.15	24062.19
van Dierendonck (2001) model	145.37	4	0.09	0.05	0.97	0.90	24065.24	24108.29
Taris et al. (2005) Model	22.97	3	0.04	0.02	0.99	0.98	23944.83	23990.96
Adjusted Model 1	0.61	2	0.00	0.00	1	1	23924.48	23973.68
Adjusted Model 2	51.24	5	0.05	0.03	0.99	0.97	23969.11	24009.08

Model	χ²	df	RMSEA	SRMR	CFI	TLI	AIC	aBIC
Basic model	160.63	6	0.08	0.05	0.96	0.93	24076.50	24113.40
Golembiewski (1983) model	49.45	4	0.05	0.03	0.99	0.97	23969.32	24012.36
Leiter & Maslach (1988) model	25.35	4	0.04	0.02	0.99	0.98	23945.22	23988.27
Lee & Ashforth (1993) model	99.28	4	0.08	0.03	0.98	0.93	24019.15	24062.19
van Dierendonck (2001) model	145.37	4	0.09	0.05	0.97	0.90	24065.24	24108.29
Taris et al. (2005) Model	22.97	3	0.04	0.02	0.99	0.98	23944.83	23990.96
Adjusted Model 1	0.61	2	0.00	0.00	1	1	23924.48	23973.68
Adjusted Model 2	51.24	5	0.05	0.03	0.99	0.97	23969.11	24009.08

Model	Path	b	SE	t	β	r
Golembiewski (1983) model	ee1→ee2	0.40	0.01	36.82	0.44	0.49
	pa1→ee2	0.01	0.01	1.40	0.01	0.06
	dp1→dp2	0.41	0.01	29.00	0.38	0.43
	dp1→pa2	0.21	0.02	10.45	0.16	0.21
	pa1→pa2	0.18	0.01	13.77	0.21	0.26
Leiter & Maslach (1988) model	ee1→ee2	0.44	0.01	34.00	0.44	0.49
	ee1→dp2	0.06	0.01	5.17	0.06	0.11
	dp1→dp2	0.36	0.0	24.33	0.36	0.41
	dp1→pa2	0.21	0.02	10.65	0.21	0.26
	pa1→pa2	0.18	0.01	13.61	0.18	0.23
Taris et al. (2005) Mode l	ee1→ee2	0.42	0.02	25.00	0.45	0.50
	dp1→ee2	0.04	0.03	1.50	0.03	0.08
	ee1→dp2	0.05	0.01	4.15	0.07	0.12
	dp1→dp2	0.38	0.02	19.25	0.35	0.40
	dp1→pa2	0.21	0.02	10.75	0.17	0.22
	pa1→pa2	0.18	0.01	13.69	0.21	0.26
Adjusted Model 1	ee1→ee2	0.44	0.01	34.08	0.47	0.52
	pa1→ee2	0.05	0.01	3.92	0.05	0.10
	ee1→dp2	0.07	0.01	5.75	0.09	0.14
	dp1→dp2	0.35	0.02	22.06	0.33	0.38
	pa1→dp2	0.05	0.01	4.82	0.07	0.12
	dp1→pa2	0.21	0.02	10.55	0.16	0.21
	pa1→pa2	0.19	0.01	14.77	0.22	0.28
Adjusted Model 2	ee1→ee2	0.40	0.01	36.73	0.43	0.48
	dp1→dp2	0.40	0.01	31.08	0.38	0.43
	dp1→pa2	0.21	0.02	10.45	0.16	0.21
	pa1→pa2	0.18	0.01	13.85	0.21	0.26