Use of Latent Profile Analysis in Studies of Gifted Students

REFERENCES

• Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Csake (Eds.), Second international symposium on information theory ( pp. 267–281). Budapest, Hungary: Akademiai Kiado.
   Google Scholar
• Asendorpf, J. B. (2006). Typeness of personality profiles: A continuous person-centered approach to personality data. European Journal of Personality, 20, 83–106. doi:10.1002/per.575
   Web of Science ®Google Scholar
• Asendorpf, J. B. (2015). Person-centered approaches to personality. In M. L. Cooper & R. J. Larsen (Eds.), Handbook of personality and social psychology: Personality processes and individual differences ( pp. 403–424). Washington, DC: American Psychological Association. doi:10.1037/14343-018
   Google Scholar
• Bergman, L. R., Magnusson, D., & El-Khouri, B. M. (2003). Studying individual development in interindividual context: A person-oriented approach. In D. Magnusson (Series Ed.), Paths through life (Vol. 4). Mahwah, NJ: Erlbaum.
   Google Scholar
• Bozdogan, H. (1987). Model selection and Akaike’s information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52, 345–370. doi:10.1007/BF02294361
   Web of Science ®Google Scholar
• Brasseur-Hock, I., Hock, M. F., Kieffer, M. J., Biancarosa, G., & Deshler, D. D. (2011). Adolescent struggling readers in urban schools: Results of a latent class analysis. Learning and Individual Differences, 21, 438–452. doi:10.1016/j.lindif.2011.01.008
   Web of Science ®Google Scholar
• Chae, Y., & Gentry, M. (2011). Gifted and general high school students’ perceptions of learning and motivational constructs in Korea and the United States. High Ability Studies, 22, 103–118. doi:10.1080/13598139.2011.577275
   Web of Science ®Google Scholar
• Chan, D. W. (2009). Dimensionality and typology of perfectionism: The use of the Frost Multidimensional Perfectionism Scale with Chinese gifted students in Hong Kong. Gifted Child Quarterly, 53, 174–187. doi:10.1177/0016986209334963
   Web of Science ®Google Scholar
• Chan, D. W. (2012). Life satisfaction, happiness, and the growth mindset of healthy and unhealthy perfectionists among Hong Kong Chinese gifted students. Roeper Review, 34, 224–233. doi:10.1080/02783193.2012.715333
   Google Scholar
• Cho, S., Ahn, D., Han, S., & Park, H. (2008). Academic developmental patterns of the Korean gifted during the 18 years after identification. Personality and Individual Differences, 45, 784–789. doi:10.1016/j.paid.2008.08.007
   Web of Science ®Google Scholar
• Clark, S. L. (2010). Mixture modeling with behavioral data (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses database. (UMI No. 3405665)
   Google Scholar
• Cross, J. R., Cross, T. L., & Finch, H. (2010). Maximizing student potential versus building community: An exploration of right-wing authoritarianism, social dominance orientation, and preferred practice among supporters of gifted education. Roeper Review, 32, 235–248. doi:10.1080/02783193.2010.508155
   Google Scholar
• Cross, J. R., Kim, M., O’Reilly, C., Mammadov, S., & Cross, T. L. (2015). Social coping and self-concept among young gifted students in Ireland and the United States: A cross-cultural study. High Ability Studies, 26(1), 39–61. doi:10.1080.13598139.2015.1031881
   Web of Science ®Google Scholar
• Dayton, C. M. (1998). Latent class scaling analysis. Thousand Oaks, CA: Sage. doi:10.4135/9781412984720
   Google Scholar
• Dixon, F. A., Cross, T. L., & Adams, C. M. (2001). Psychological characteristics of academically gifted students in a residential setting: A cluster analysis. Psychology in the Schools, 38, 433–445. doi:10.1002/pits.1032
   Web of Science ®Google Scholar
• Dixon, F. A., Lapsley, D. K., & Hanchon, T. A. (2004). An empirical typology of perfectionism in gifted adolescents. Gifted Child Quarterly, 48, 95. doi:10.1177/001698620404800203
   Web of Science ®Google Scholar
• Eshghi, A., Haughton, D. M., Legrand, P., Skaletsky, M., & Woolford, S. W. (2011). Identifying groups: A comparison of methodologies. Journal of Data Science, 9, 271–291.
   Google Scholar
• Everitt, B. S., Landau, S., & Leese, M. (2009). Cluster analysis (4th ed.). New York, NY: Wiley.
   Google Scholar
• Field, A. (2005). Discovering statistics using SPSS (2nd ed.). Thousand Oaks, CA: Sage.
   Google Scholar
• Gibson, W. A. (1959). Three multivariate models: Factor analysis, latent structure analysis and latent profile analysis. Psychometrika, 24, 229–252. doi:10.1007/BF02289845
   Web of Science ®Google Scholar
• Hagenaars, J. A., & McCutcheon, A. L. (2002). Applied latent class analysis. New York, NY: Cambridge University Press. doi:10.1017/CBO9780511499531
   Google Scholar
• Henson, J. M., Reise, S. P., & Kim, K. H. (2007). Detecting mixtures from structural model differences using latent variable mixture modeling: A comparison of relative model fit statistics. Structural Equation Modeling: A Multidisciplinary Journal, 14, 202–226. doi:10.1080/10705510709336744
   Web of Science ®Google Scholar
• IBM SPSS. (2012). IBM SPSS statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.
   Google Scholar
• Jain, A. K., & Dubes, R. C. (1988). Algorithms for clustering data. Upper Saddle River, NJ: Prentice Hall.
   Google Scholar
• John, O. P., Donahue, E. M., & Kentle, R. L. (1991). The big five inventory—Versions 4a and 54. Berkeley: University of California, Berkeley, Institute of Personality and Social Research.
   Google Scholar
• Jöreskog, K. G., & Sörbom, D. (1979). Advances in factor analysis and structural equation models. Cambridge, MA: Abt Books.
   Google Scholar
• Kerr, B., & McKay, R. (2013). Searching for tomorrow’s innovators: Profiling creative adolescents. Creativity Research Journal, 25, 21–32. doi:10.1080/10400419.2013.752180
   Web of Science ®Google Scholar
• Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2007). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling: A Multidisciplinary Journal, 14, 671–694. doi:10.1080/10705510701575602
   PubMed Web of Science ®Google Scholar
• Laursen, B., & Hoff, E. (2006). Person-centered and variable-centered approaches to longitudinal data. Merill-Palmer Quarterly, 52, 377–389. doi:10.1353/mpq.2006.0029
   Web of Science ®Google Scholar
• Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston, MA: Houghton Mifflin.
   Google Scholar
• Leon-Perez, J., Notelaers, G., Arenas, A., Munduate, L., & Medina, F. J. (2014). Identifying victims of workplace bullying by integrating traditional estimation approaches into a latent class cluster model. Journal of Interpersonal Violence, 29, 1155–1177. doi:10.1177/0886260513506280
   PubMed Web of Science ®Google Scholar
• Li, J. J., & Lee, S. S. (2012). Interaction of dopamine transporter (DAT1) genotype and maltreatment for ADHD: A latent class analysis. Journal of Child Psychology and Psychiatry, 53, 997–1005. doi:10.1111/jcpp.2012.53.issue-9
   PubMed Web of Science ®Google Scholar
• Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88, 767–778. doi:10.1093/biomet/88.3.767
   Web of Science ®Google Scholar
• Loken, E., & Molenaar, P. C. M. (2008). Categories or continua? The correspondence between mixture models and factor models. In G. R. Hancock & K. M. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 277–297). Charlotte, NC: Information Age Publishing.
   Google Scholar
• Lubke, G. H., & Muthén, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10, 21–39. doi:10.1037/1082-989x.10.1.21
   PubMed Web of Science ®Google Scholar
• Marcoulides, G. A., Gottfried, A. E., Gottfried, A. W., & Oliver, P. H. (2008). A latent transition analysis of academic intrinsic motivation from childhood through adolescence. Educational Research and Evaluation, 14, 411–427. doi:10.1080/13803610802337665
   Google Scholar
• Marsh, H. W., Lüdtke, O., Trautwein, U., & Morin, A. J. S. (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: A Multidisciplinary Journal, 16, 191–225. doi:10.1080/10705510902751010
   Web of Science ®Google Scholar
• McCutcheon, A. L. (1987). Latent class analysis. Newbury Park, CA: Sage.
   Google Scholar
• McLachlan, G., & Peel, D. (2000). Finite mixture models. New York, NY: Wiley.
   Google Scholar
• Miller, A. L., Lambert, A. D., & Speirs Neumeister, K. L. (2012). Parenting style, perfectionism, and creativity in high-ability and high-achieving young adults. Journal for the Education of the Gifted, 35, 344–365. doi:10.1177/0162353212459257
   Google Scholar
• Miller, A. L., & Speirs Neumeister, K. L. (2012). Multiple variables for predicting creativity in high ability adults. Advanced Development Journal, 13, 84–102.
   Google Scholar
• Muthén, B. O. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (Ed.), Handbook of quantitative methodology for the social sciences ( pp. 345–368). Newbury Park, CA: Sage. doi:10.4135/9781412986311.n19
   Google Scholar
• Muthén, B. O., & Muthén, L. K. (2000). Integrating person-centered and variable centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882–891. doi:10.1111/j.1530-0277.2000.tb02070.x
   PubMed Web of Science ®Google Scholar
• Muthén, B. O., & Muthén, L. K. (2007). Mplus: Statistical analysis with latent variables (Version 4.2.1). Los Angeles, CA: Muthén and Muthén, Inc.
   Google Scholar
• Muthén, L. K., & Muthén, B. O. (2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén.
   Google Scholar
• Nylund, K. L., Asparouhov, T., & Muthen, 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, 535–569. doi:10.1080/10705510701575396
   Web of Science ®Google Scholar
• Parker, W. D. (1997). An empirical typology of perfectionism in academically talented children. American Educational Research Journal, 34, 545–562. doi:10.3102/00028312034003545
   Web of Science ®Google Scholar
• Pastor, D. A., & Gagné, P. (2013). Mean and covariance structure mixture models. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (2nd ed., pp. 343–393). Greenwich, CT: Information Age.
   Google Scholar
• Poncheri, R. M., & Ward, S. J. (2008, April). Personality remix: Integrating variable-centered and person-centered approaches to personality. Paper presented at the 23rd annual meeting of the Society for Industrial and Organizational Psychology, San Francisco, CA.
   Google Scholar
• Rinn, A. N., Mendaglio, S., Rudasill, K. M., & McQueen, K. S. (2010). Examining the relationship between the overexcitabilities and self-concepts of gifted adolescents via multivariate cluster analysis. Gifted Child Quarterly, 54, 3–17. doi:10.1177/0016986209352682
   Web of Science ®Google Scholar
• Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464. doi:10.1214/aos/1176344136
   Web of Science ®Google Scholar
• Sclove, S. L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52, 333–343. doi:10.1007/BF02294360
   Web of Science ®Google Scholar
• Tein, J., Coxe, S., & Cham, H. (2013). Statistical power to detect the correct number of classes in latent profile analysis. Structural Equation Modeling: A Multidisciplinary Journal, 20, 640–657. doi:10.1080/10705511.2013.824781
   PubMed Web of Science ®Google Scholar
• Tofighi, D., & Enders, C. K. (2008). Identifying the correct number of classes in growth mixture models. In G. R. Hancock & K. M. Samuelsen (Eds.), Advances in latent variable mixture models ( pp. 317–341). Greenwich, CT: Information Age.
   Google Scholar
• Ullman, J. B. (2006). Structural equation modeling: Reviewing the basics and moving forward. Journal of Personality Assessment, 87(1), 35–50. doi:10.1207/s15327752jpa8701_03
   PubMed Web of Science ®Google Scholar
• Vermunt, J. K., & Magidson, J. (2002). Latent class cluster analysis. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied latent class analysis (1st ed., pp. 89–106). Cambridge, England: Cambridge University Press. doi:10.1017/CBO9780511499531.004
   Google Scholar
• Wong, Y. J., & Maffini, C. S. (2011). Predictors of Asian American adolescents’ suicide attempts: A latent class regression analysis. Journal of Youth and Adolescence, 40, 1453–1464. doi:10.1007/s10964-011-9701-3
   PubMed Web of Science ®Google Scholar
• Yang, C. (2006). Evaluating latent class analysis models in qualitative phenotype identification. Computational Statistics & Data Analysis, 50, 1090–1104. doi:10.1016/j.csda.2004.11.004
   Web of Science ®Google Scholar