What are we working on?

Establishing best practices with mixture models

• Nylund-Gibson, K., Grimm, R., Masyn, K. (in press). Prediction from latent classes: A demonstration of different approaches to include distal outcomes in mixture models. Structural Equation Modeling, 26(6), 967-985.
• Nylund-Gibson, K., & Choi, A. Y. (2018). Ten frequently asked questions about latent class analysis. Translational Issues in Psychological Science., 4(4), 440- 461.
• 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: A Multidisciplinary Journal, 23(6), 782-797.
• Nylund-Gibson, K., Grimm, R., Quirk, M., & Furlong, M. (2014). A latent transition mixture model using the three-step specification. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 439-454.
  • Excel file for changing reference class in logit calculations.
• Grimm, R. (2015). Review of handbook of quantitative methods for educational research, edited by Timothy Teo. Structural Equation Modeling: A Multidisciplinary Journal. Advance online publication. doi: 10.1080/10705511.2014.955103 Nylund-Gibson, K., & Hart, S. (2014). Latent class analysis is prevention science. In Z. Sloboba, & H. Petras, (Eds.), Defining prevention science, (pp. 493-511). New York: Springer Science & Business Media. Nylund-Gibson, K, Graham, S., & Juvonen, J. (2010). An application of multilevel LCA to study peer victimization in middle school. Advances and Applications in Statistical Sciences, 3(2), 343-363
• Nylund, K., Asparouhov, T., & Muthén, B. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: An Interdisciplinary Journal, 14(4), 535-569. Nylund, K., Bellmore, A., Nishina, A., & Graham, S. (2007). Subtypes, severity, and structural stability of peer victimization: what does latent class analysis say? Child Development, 78(6), 1706-1722. doi: 10.1111/j.1467-8624.2007.01097.x Nylund, K (2007). Latent Transition Analysis: Modeling Extensions and an Application to Peer Victimization.

Three-step method with mixture models

• Nylund-Gibson, K., Grimm, R., Quirk, M., & Furlong, M. (2014). A latent transition mixture modeling using the three-step specification. Structural Equation Modeling, 21(3), 439-454.
• Nylund-Gibson, K., Grimm, R., Masyn, K. (2019). Prediction from latent classes: A demonstration of different approaches to include distal outcomes in mixture models. Structural Equation Modeling, 26(6), 967-985.

Latent Transition Analysis

• Moore, S. A., Dowdy, E., Nylund-Gibson, K., & Furlong, M. J. (2019). A latent transition analysis of the longitudinal stability of dual-factor mental health in adolescence. Journal of School Psychology, 73, 56-73.
• Liu, S. R., Kia-Keating, M, Nylund-Gibson, K. (in press). Patterns of family, school, and community promotive factors and health disparities among youth: Implications for prevention science. Prevention Science. Moore, S.A., Dowdy, E., Nylund-Gibson, K., Furlong, M. J. (2019). An Empirical Approach to Complete Mental Health Classification in Adolescents. School Mental Health, 11(3), 438-453.
• Nylund-Gibson, K., Grimm, R., Quirk, M., & Furlong, M. (2014). A latent transition mixture modeling using the three-step specification. Structural Equation Modeling, 21(3), 439-454.
• Quirk, M., Grimm, R., Furlong, M.J., & Nylund-Gibson, K., Swami, S. (2016). Latino children’s school readiness profiles and associations with longitudinal reading achievement trajectories. Journal of Educational Psychology, 108(6), 814-829.
• Ing, M., & Nylund-Gibson, K. (2013). Linking early science and mathematics attitudes to long-term science, technology, engineering, and mathematics career attainment: Latent class analysis with proximal and distal outcomes. Educational Research and Evaluation, 19(6), 510-524.
• Nylund, K (2007). Latent Transition Analysis: Modeling Extensions and an Application to Peer Victimization.

STEM Research

• Phelan, J., Ing, M., Nylund-Gibson, K., & Brown, R.S. (2017). Identifying Students’ Expectancy-Value Beliefs: A Latent Class Analysis Approach to Analyzing Middle School Students’ Science Self-Perception. Journal of STEM Education: Innovations & Research, 18(1), 11-15
• Dang, M., Nylund-Gibson, K. (2017). Connecting Math Attitudes with STEM Career Attainment: A Latent Class Analysis Approach. Teachers College Record, 119(6), 1-38.
• Ing, M., & Nylund-Gibson, K. (2013). Linking early science and mathematics attitudes to long-term science, technology, engineering, and mathematics career attainment: Latent class analysis with proximal and distal outcomes. Educational Research and Evaluation, 19(6), 510-524. [link]
• Nylund-Gibson, K., Ing, M., & Park, K. (2013). A latent class analysis of student science attitudes, perceived teacher support, and STEM career attainment. The International Journal of Engineering and Science, 2(12), 65-70.