Modeling unobserved heterogeneity using person-centered approaches: Latent profiles of preservice teachers' emotional awareness

Yıl 2023, Cilt: 10 Sayı: 1, 129 - 144, 20.03.2023

Esra Sözer Boz , Derya Akbaş , Nilüfer Kahraman

https://doi.org/10.21449/ijate.1148460

Öz

Latent Class and Latent Profile Models are widely used in psychological assessment settings, especially when individual differences are suspected to be related to unobserved class memberships, such as different personality types. This paper provides an easy-to-follow introduction and application of the methodology to the data collected as part of more extensive educational research investigating social-emotional competency profiles of preservice teachers (n=184) who responded to an Emotional Awareness Questionnaire. Suspected that there would be two or more latent emotional awareness sub-groups in the sample, a series of latent profile models was estimated. The results suggested three distinct emotional awareness profiles; namely, introverted, extroverted, and less sensitive to others' emotions, with proportions of 9%, 56%, and 35%, respectively. Subsequent analyses showed that preservice teachers with higher levels of emotionality, sociability, and well-being were more likely to be in the extroverted profile. The findings suggest that nearly half of the teachers in the sample could be expected to possess the most professionally desirable teacher profile. Nonetheless, it was noted that if timely diagnostic and tailored training or intervention programs were available, at least some of the preservice teachers in the less sensitive to others' profiles, and most of the preservice teachers in the introverted profile could be helped to self-observe the way which they tend to identify and regulate their emotions.

Anahtar Kelimeler

Latent profile, Heterogeneity, Preservice teachers, Emotional awareness

Proje Numarası

04/2019-01

Kaynakça

• Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52(3), 317 332. https://doi.org/10.1007/BF02294359
• Ashkanasy, N.M. & Dasborough, M.T. (2003) Emotional awareness and emotional intelligence in leadership teaching. Journal of Education for Business, 79(1), 18-22. https://doi.org/10.1080/08832320309599082
• Asparouhov, T., & Muthen, B. (2014). Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 21, 329-341. https://doi.org/10.1080/10705511.2014.915181
• Bauer, J. (2022). A primer to Latent Profile and Latent Class Analysis. In M. Goller, E. Kyndt, S. Paloniemi & C. Damşa (Eds.), Methods for researching professional learning and development: Challenges, applications, and empirical illustrations (pp. 243-268). Springer Cham. https://doi.org/10.1007/978-3-031-08518-5
• Bauer, D.J., & Curran, P.J. (2004). The integration of continuous and discrete latent variable models: Potential problems and promising opportunities. Psychological Methods, 9(1), 3-29. https://doi.org/10.1037/1082-989X.9.1.3
• Berlin, K.S., Parra, G.R., & Williams, N.A. (2014). An introduction to latent variable mixture modeling (part 2): longitudinal latent class growth analysis and growth mixture models. Journal of Pediatric Psychology, 39(2), 188-203. https://doi.org/10.1093/jpepsy/jst085
• Bondjers, K., Willebrand, M., & Arnberg, F.K. (2018). Similarity in symptom patterns of posttraumatic stress among disaster-survivors: a three-step latent profile analysis. European Journal of Psychotraumatology, 9(1). https://doi.org/10.1080/20008198.2018.1546083
• Bouckenooghe, D., Clercq, D.D., & Raja, U. (2019). A person-centered, latent profile analysis of psychological capital. Australian Journal of Management, 44(1), 91-108. https://doi.org/10.1177/0312896218775153
• Celeux, G., & Soromenho, G. (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13(2), 195 212. https://doi.org/10.1007/BF01246098
• Deniz, M.E., Özer, E., & Işık, E. (2013). Trait Emotional Intelligence Questionnaire–Short Form: Validity and reliability studies. Education and Science, 38(169), 407-419.
• Ferguson, S.L., & Hull, D.M. (2019). Exploring science career interest: Latent profile analysis of high school occupational preferences for science. Journal of Career Development, 46(5), 583-598. https://doi.org/10.1177/0894845318783873
• Ferguson, S.L., Moore, E.W., & Hull, D.M. (2020). Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers. International Journal of Behavioral Development, 44(5), 458-468. https://doi.org/10.1177/0165025419881721
• Gibson, W.A. (1959). Three multivariate models: Factor analysis, latent structure analysis, and latent profile analysis. Psychometrika, 24, 229-252. https://doi.org/10.1007/BF02289845
• Gottman, J. & Declaire, J. (1997). Raising an emotionally intelligent child: The heart of parenting. Simon & Schuster.
• Grunschel, C., Patrzek, J., & Fries, S. (2013). Exploring different types of academic delayers: A latent profile analysis. Learning and Individual Differences, 23, 225-233. https://doi.org/10.1016/j.lindif.2012.09.014
• Harvey, S. & Evans, I.M. (2003). Understanding the emotional environment of the classroom. In D. Fraser & R. Openshaw (Eds.), Informing our practice (pp. 182-195). Kanuka Grove.
• Hickendorff, M., Edelsbrunner, P.A., Schneider, M., Trezise, K., & & McMullen, J. (2018). Informative tools for characterizing individual differences in learning: Latent class, latent profile, and latent transition analysis. Learning and Individual Differences, 66, 4-15. https://doi.org/10.1016/j.lindif.2017.11.001
• Hill, A.L., Degnan, K.A., Calkins, S.D., & Keane, S.P. (2006). Profiles of externalizing behavior problems for boys and girls across preschool: The roles of emotion regulation and inattention. Developmental Psychology, 42(5), 913 928. https://doi.org/10.1037/0012-1649.42.5.913
• Jennings, P.A., & Greenberg, M.T. (2009). The prosocial classroom: Teacher social and emotional competence in relation to student and classroom outcomes. Review of Educational Research, 79(1), 491-525. http://dx.doi.org/10.3102/0034654308325693
• Jung, T., & Wickrama, K.A. (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2(1), 302-317. https://doi.org/10.1111/j.1751-9004.2007.00054.x
• Kim, S., & Lee, Y. (2021). Examining the profiles of school violence and their association with individual and relational covariates among South Korean children. Child Abuse & Neglect, 118. https://doi.org/10.1016/j.chiabu.2021.105155
• Kökçam, B., Arslan, C., & Traş, Z. (2022). Do psychological resilience and emotional intelligence vary among stress profiles in university students? A latent profile analysis. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.788506
• Lanza, S.T., & Rhoades, B.L. (2013). Latent class analysis: An alternative perspective on subgroup analysis in prevention and treatment. Prevention Science, 14, 157-168. https://doi.org/10.1007/s11121-011-0201-1
• Lanza, S.T., Flaherty, B.P., & Collins, L.M. (2003). Latent class and latent transition analysis. In J.A. Schinka, & W.A. Velicer (Eds.), Handbook of psychology: Research methods in psychology (pp. 663-685). Wiley.
• Lehmann, R.J., Neumann, C.S., Hare, R.D., Biedermann, J., Dahle, K.P., & Mokros, A. (2019). A latent profile analysis of violent offenders based on PCL-R factor scores: Criminogenic needs and recidivism risk. Frontiers in Psychiatry, 10, 627. https://doi.org/10.3389/fpsyt.2019.00627
• Lo, Y., Mendell, N.R., & Rubin, D.B. (2001). Testing the number of components in a normal mixture. Biometrika, 88(3), 767-778. https://doi.org/10.1093/biomet/88.3.767
• Lubke, G.H., & Muthen, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10(1), 21-39. https://doi.org/10.1037/1082-989X.10.1.21
• Marsh, H.W., Ludtke, O., Trautwein, U., & Morin, A.J. (2009). Classical latent profile analysis of academic self-concept dimensions: Synergy of person-and variable-centered approaches to theoretical models of self-concept. Structural Equation Modeling, 16, 191-225. https://doi.org/0.1080/10705510902751010
• Masyn, K.E. (2013). Latent class analysis and finite mixture modeling. In T.L. (Eds.), The Oxford handbook of quantitative methods (pp. 551-611). Oxford University.
• McCarthy, D. (2021). Adding social emotional awareness to teacher education field experiences. The Teacher Educator. https://doi.org/10.1080/08878730.2021.1890291
• Merz, E.L., & Roesch, S.C. (2011). A latent profile analysis of the Five Factor Model of personality: Modeling trait interactions. Personality and Individual Differences, 51(8), 915-919. https://doi.org/10.1016/j.paid.2011.07.022
• Muthén, B. (2007). Latent variable hybrids: Overview of old and new methods. In G.R. Hancock & K.M. Samuelsen (Eds.), Advances in latent variable mixture modeling (pp. 1-24). Information Age.
• Muthén, B., & Muthén, L.K. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24(6), 882 891. https://doi.org/10.1111/j.15300277.2000.tb02070.x
• Muthén, L.K., & Muthén, B.O. (1998-2017). Mplus user’s guide (8th Edition). Muthén & Muthén.
• Nylund, K.L., Asparouhov, T., & Muthén, B.O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535-569. https://doi.org/10.1080/10705510701575396
• Nylund-Gibson, K., & Masyn, K.E. (2016). Covariates and mixture modeling: Results of a simulation study exploring the impact of misspecified effects on class enumeration. Structural Equation Modeling, 23, 782 797. https://doi.org/10.1080/10705511.2016.1221313
• Oberski, D.L. (2016). Mixture models: latent profile and latent class analysis. In J. Robertson, & M. Kaptein (Eds.), Modern statistical methods for HCI (pp. 275-287). Springer.
• Petrides, K.V., & Furnham, A. (2000). Gender differences in measured and self-estimated trait emotional intelligence. Sex Roles, 42(5), 449 461. https://doi.org/10.1023/A:1007006523133
• Peugh, J., & Fan, X. (2013). Modeling unobserved heterogeneity using latent profile analysis: A Monte Carlo simulation. Structural Equation Modeling: A Multidisciplinary Journal, 20(4), 616-639. https://doi.org/10.1080/10705511.2013.824780
• Rieffe, C., Oosterveld, P., Miers, A.C., Terwogt, M.M., & & Ly, V. (2008). Emotion awareness and internalising symptoms in children and adolescents: The Emotion Awareness Questionnaire revised. Personality and Individual Differences, 45(8), 756-761. https://doi.org/10.1016/j.paid.2008.08.001
• Roesch, S.C., Villodas, M., & Villodas, F. (2010). Latent class/profile analysis in maltreatment research: A commentary on Nooner et al., Pears et al., and looking beyond. Child Abuse & Neglect, 34(3), 155-160. https://doi.org/10.1016/j.chiabu.2010.01.003
• Saritepeci, M., Yildiz-Durak, H., & Atman-Uslu, N. (2022). A Latent Profile Analysis for the Study of Multiple Screen Addiction, Mobile Social Gaming Addiction, General Mattering, and Family Sense of Belonging in University Students. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-022-00816-y
• Schwarz, G. (1978). Estimating the dimension of a model. The Annuals of Statistics, 6(2), 461-464. https://doi.org/10.1214/aos/1176344136
• Snow, R.E. (1986). Individual differences and the design of educational programs. American Psychologist, 41, 1029-1039. https://doi.org/10.1037/0003-066X.41.10
• Stanley, L., Kellermans, F.W., & Zellweger, T.M. (2017). Latent profile analysis: Understanding family firm profiles. Family Business Review, 30(1), 84-102. https://doi.org/10.1177/0894486516677426
• Steinley, D., & Brusco, M.J. (2011). Evaluating mixture modeling for clustering: Recommendations and cautions. Psychological Methods, 16, 63 79. https://doi.org/10.1037/a0022673
• Sterba, S.K. (2013). Understanding linkages among mixture models. Multivariate Behavioral Research, 48, 775-815. https://doi.org/10.1080/00273171.2013.827564
• Tein, J.Y., Coxe, S., & Cham, H. (2013). Statistical power to detect the correct number of classes in latent profile analysis. Structural Equation Modeling, 20, 640-657. https://doi.org/10.1080/10705511.2013.824781
• Ulloa, M., Evans, I., & Jones, L. (2016). The effects of emotional awareness training on teachers' ability to manage the emotions of preschool children: An experimental study. Escritos de Psicología, 9(1), 1-14. https://doi.org 10.5231/psy.writ.2015.1711
• Vermunt, J.K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18(4), 450–469. https://doi.org/10.1093/pan/mpq025
• Wade, T.D., Crosby, R.D., & Martin, N.G. (2006). Use of latent profile analysis to identify eating disorder phenotypes in an adult Australian twin cohort. Arch Gen Psychiatry, 63(12),1377–1384. https://doi.org/10.1001/archpsyc.63.12.1377
• Wang, Y., Su, Q., & Wen, Z. (2019). Exploring latent profiles of empathy among chinese preschool teachers: A person-centered approach. Journal of Psychoeducational Assessment, 37(6), 706-717. https://doi.org/10.1177/0734282918786653
• Wei, M., Mallinckrodt, B., Arterberry, B.J., Liu, S., & Wang, K.T. (2021). Latent profile analysis of interpersonal problems: attachment, basic psychological need frustration, and psychological outcomes. Journal of Counseling Psychology, 68(4), 467-488. https://doi.org/10.1037/cou0000551
• Whittaker, T.A., & Miller, J.E. (2021). Exploring the enumeration accuracy of cross-validation indices in latent class analysis. Structural Equation Modeling: A Multidisciplinary Journal, 28(3), 376-390. https://doi.org/10.1080/10705511.2020.1802280
• Williams, K.E., Nicholson, J.M., Walker, S., & Berthelsen, D. (2016). Early childhood profiles of sleep problems and self-regulation predict later school adjustment. British Journal of Educational Psychology, 86(2), 331-350. https://doi.org/10.1111/bjep.12109
• Wurpts, I.C., & Geiser, C. (2014). Is adding more indicators to a latent class analysis beneficial or detrimental? Results of a Monte-Carlo study. Frontiers in Psychology, 5, 920. http://dx.doi.org/10.3389/psyg.2014.00920
• Yalçın, İ., Can, N., Mançe Çalışır, Ö., Yalçın, S., & Çolak, B. (2022). Latent profile analysis of COVID-19 fear, depression, anxiety, stress, mindfulness, and resilience. Current Psychology, 41, 459-469. https://doi.org/10.1007/s12144-021-01667-x