Michael Rovine

Biography

Dr. Michael J. Rovine is a Senior Fellow in Quantitative Methods at the University of Pennsylvania Graduate School of Education. He is a developmental methodologist and quantitative psychologist. He is also a member of the Quantitative Development Group at The Pennsylvania State University.

Dr. Rovine is the former director of Penn State’s Methodology Consulting Center. He has been affiliated with Penn State’s Social Science Research Institute and was involved in many of their interdisciplinary research initiatives.

Ph.D. (Educational Psychology) The Pennsylvania State University, 1982
M.S. (Educational Psychology) The Pennsylvania State University, 1979
B.S. (Mathematics) University of Pennsylvania, 1971

Multivariate statistics
Multilevel modeling
Longitudinal analysis
Structural equations modeling
Time series modeling

Statistics, Measurement, Assessment, and Research Technology, M.S.Ed.

Quantitative Methods, M.Phil.Ed.

Quantitative Methods, Ph.D.

Research Interests and Current Projects

Dr. Rovine’s research is in areas related to statistical modeling. Using a structural equations modeling (SEM) approach, he looks at ways to estimate a number of different multilevel models as SEM. One model is a multilevel autoregressive model that could have important implications for those collecting intensive time series or ecological momentary assessment (EMA) data. He has also been involved in the development of the SEM version of the nonstationary autoregressive moving average model (NARMA), which can be used to describe essentially any latent variable model. Dr. Rovine is also interested in the relationship between different models based on the general linear mixed model, including a comparison of repeated measures ANOVA and multilevel growth curve modeling.

Dr. Rovine’s main focus is on idiographic approaches to the description of developmental phenomena. He recently completed a study funded by the National Science Foundation to develop and apply time series models to developmental data. Working with developmental psychologists and engineers on a number of different models including multilevel ARMA, state space, and optimal control models, Dr. Rovine is continuing this work with a special emphasis on hidden Markov modeling which he has used to model mother-infant interactions. He is currently involved in a National Institutes of Health (NIH) grant to look at the development of self-regulation in infants. Along with a colleague at Penn State, Dr. Rovine is looking at the possibility of modeling and eventually controlling the incidence of symptoms and attacks in asthma sufferers and blood glucose level in diabetes patients.

He is also interested in the history of statistics, particularly the contributions of the philosopher C. S. Peirce to the development of statistical methodology. Dr. Rovine first encountered Peirce’s work while developing variations of the correlation coefficient that could be used to describe effect sizes in uncontrolled studies. Looking for antecedents, he discovered a similar coefficient presented by Peirce in 1884 along with an interesting history of correlation and regression that predated the better known work of Pearson and Galton.

Rovine, M. J., Weiss, E. M., & McDermott, P. A. (in press). It’s all regression: Fundamentals of developmental data analysis. In D. M. Teti, H. H. Cleveland, & K. Rulison (Eds.), Handbook of research methods in developmental science.

Gerstner, C., McDermott, P. A., Rovine, M. J., & Weiss, E. M. (in press). Contextual specificity in classroom adjustment: Latent profiles of primary school behavior problems in Trinidad and Tobago. International Journal of School and Educational Psychology.

Rovine, M. J., & McDermott, P. A. (2023). Touchstones of equivalence and the Houdini transformation. Multivariate Behavioral Research. Advance online publication. https://doi.org/10.1080/00273171.2023.2205390

Weiss, E. M., Gerstner, C. C., McDermott, P. A., & Rovine, M. J. (2023). Latent trajectories of learning- and teacher-context behavior problems across the primary school transition. Journal of Applied Developmental Psychology, 86, Article 101538. https://doi.org/10.1016/j.appdev.2023.101538

Heid, A. R., Abbott, K. M., Rovine, M. J., Eshraghi, K., Madrigal, C., Crumbie, V., & Van Haitsma, K. (2023). The stability of nursing home residents’ ratings of importance of recreation preferences in daily care over one year. Journal of Applied Gerontology, 41(8), 1942–1951. https://doi.org/10.1177/07334648221089239

McDermott, P. A., Rovine, M. J., Gerstner, C., Weiss, E. M., Watkins, M. W. (2022). Latent profile analysis of classroom behavior problems in an American national sample of prekindergarten children. Social Development, 31(4), 1059–1078. https://doi.org/10.1111/sode.12606

McDermott, P. A., Rovine, M. J., Weiss, E. M., Gladstone, J. N., Fatima, S. F., & Reyes, R. S. (2022). Latent change and co-occurrence of overactive and underactive behavior problems in American early education, School Psychology Review. Advance online publication. https://doi.org/10.1080/2372966X.2021.2000842

Weiss, E. M., McDermott, P. A., Rovine, M. J., & Oh, J. (2022). Latent growth trajectories of peer context problems across preschool, kindergarten and 1st grade. Early Education and Development, 33(6), 939–957. https://doi.org/10.1080/10409289.2021.1928445

Reyes, R. S., McDermott, P. A., Watkins, M. W., Rovine, M. J., & Chao, J. L. (2020). Forecasting accuracy of earliest assessment versus transitional change in early education classroom problem behavior among children at risk. School Psychology Review, 49(1), 47–59. https://doi.org/10.1080/2372966X.2020.1717372

Heid, A. R., Abbott, K. M., Kleban, M., Rovine, M. J., & Van Haitsma, K. S. (2020). The impact of nursing home residents’ characteristics on ratings of importance of autonomy preferences in daily care over time. Aging and Mental Health, 24(8), 1334–1341. https://doi.org/10.1080/13607863.2019.1584875

Chao, J. L., McDermott, P. A., Watkins, M. W., Rovine, M. J., Drogalis, A. R., Worrell, F. C., & Hall, T. E. (2019). Standardization of parental assessments of child adjustment in Trinidad and Tobago using exploratory multidimensional IRT. Caribbean Journal of Psychology, 10(2), 127–164.

McDermott, P. A., Rovine, M. J., Watkins, M. W., Rikoon, S. H., Irwin, C. W., Reyes, R., & Chao, J. L. (2019). Emergent growth patterns of early education self-control problems among children from underresourced American families. Early Childhood Research Quarterly, 48, 1–13. https://doi.org/10.1016/j.ecresq.2018.08.010

Rovine, M. J., & McDermott, P. A. (2018). Latent growth curve and repeated measures ANOVA contrasts: What the models are telling you. Multivariate Behavioral Research, 53(1), 90–101. https://doi.org/10.1080/00273171.2017.1387511

Abbott, K., Heid, A. R., Kleban, M., Rovine, M. J., & Van Haitsma, K. S. (2018). The change in nursing home residents’ preferences over time. Journal of the American Medical Directors Association, 19(12), 1092–1098. https://doi.org/10.1016/j.jamda.2018.08.004

McDermott, P. A., Rovine, M. J., Reyes, R. S., Chao, J. L., Scruggs, R., Buek, K., & Fantuzzo, J. W. (2018). Trajectories of early learning behaviors among children at risk: A growth mixture modeling approach. Psychology in the Schools, 55(10), 1205–1223. https://doi.org/10.1002/pits.22145

McDermott, P. A., Rovine, M. J., Buek, K., Reyes, R. S., Chao, J., & Watkins, M. (2018). Initial assessment versus gradual change in early childhood behavior problems—Which better foretells the future? Psychology in the Schools, 55(9), 1071–1085. https://doi.org/10.1002/pits.22150e

McDermott, P. A., Rovine, M. J., Watkins, M. W., Chao, J. L., Irwin, C. W., & Reyes, R. (2017). Latent national subpopulations of early education classroom disengagement of children from under-resourced families. Journal of School Psychology, 65, 69–82. https://doi.org/10.1016/j.jsp.2017.07.002

Rovine, M. J., & Molenaar, P. C. M. (2016). Person-specific approaches to the modeling of intra-individual variation in developmental psychopathology. In D. Cicchetti (Ed.), Developmental psychopathology (pp. 238–273). Wiley.

Stifter, C., & Rovine, M. J. (2015). Modeling dyadic processes using hidden Markov models: A time series approach to mother-infant interactions during infant immunization. Infant and Child Development, 24(3), 298–321. https://doi.org/10.1002/icd.1907

McDermott, P. A., Watkins, M. W., Rovine, M. J., & Rikoon, S. H. (2014). Informing context and change in young children's sociobehavioral development: The national Adjustment Scales for Early Transition in Schooling (ASETS). Early Childhood Research Quarterly, 29(3), 259–267. https://doi.org/10.1016/j.ecresq.2014.02.004

Wang, Q., Molenaar, P., Harsh, S., Freeman, K., Xie, J., Gold, C., Rovine, M. J., & Ulbrecht, J. (2014). Personalized state-space modeling of glucose dynamics for Type 1 diabetes using continuously monitored glucose, insulin dose and meal intake: An extended Kalman filter approach. Journal of Diabetes Science and Technology, 8(2), 331–345. https://doi.org/10.1177/1932296814524080

Liu, S., Rovine, M. J., Almeida, D., & Klein, L. (2013). Synchrony of diurnal Cortisol patterns in couples. Journal of Family Psychology, 27(4), 579–588. https://doi.org/10.1037%2Fa0033735

McDermott, P. A., Watkins, M., Rovine, M. J., & Rikoon, S. (2013). Assessing changes in socioemotional adjustment across early school transitions: New national scales for children at risk. Journal of School Psychology, 51(1), 97–115. https://doi.org/10.1016/j.jsp.2012.10.002

Liu, S., Rovine, M. J., & Molenaar, P. (2012). Selecting a linear mixed model for longitudinal data: Repeated measures ANOVA, covariance pattern models and growth curve approaches. Psychological Methods, 17(1), 15–30. https://doi.org/10.1037/a0026971

Liu, S., Rovine, M. J., & Molenaar, P. (2012). Using fit indexes to select a covariance model for longitudinal data. Structural Equation Modeling, 19(4), 633–650. https://doi.org/10.1080/10705511.2012.726918

Rovine, M. J., & Anderson, D. R. (2012). Peirce’s coefficient of the science of the method: An early form of the correlation coefficient. In D. R. Anderson & C. Hausman (Eds.), A conversation on Peirce (pp. 246–274). Fordham University Press.

Anderson, D. R., & Rovine, M. J. (2012). Peirce and Pearson: The aims of inquiry. In D. R. Anderson & C. Hausman (Eds.), A conversation on Peirce (pp. 238–273). Fordham University Press.

Rovine, M. J., & Lo, L. (2012). Issues and perspectives in person specific time series models. In B. Laursen, T. Little, & N. Card (Eds.), Handbook of developmental research methods (pp. 313–332). Guilford.

Rovine, M. J., & Liu, S. (2011). Structural equations modeling approaches to longitudinal data. In J. Newsom, R. Jones, & S. Hofer (Eds.), Longitudinal data analysis (pp. 243–270). Psychology Press.

Rovine, M. J., Sinclair, K. O., & Stifter, C. A. (2010). Modeling mother-infant interactions using hidden Markov models. In K. Newell & P. C. M. Molenaar (Eds.), Individual pathways of change in learning and development (pp. 51–67). American Psychological Association Press.

Rovine, M. J., & Walls, T. A. (2006). A multilevel autoregressive model to describe interindividual differences in the stability of a process. In J. L. Schafer & T. A. Walls (Eds.), Models for intensive longitudinal data (pp. 124–147). Oxford.

Rovine, M. J., & Molenaar, P. C. M. (2005). Relating factor models for longitudinal data to quasi-simplex and NARMA models. Multivariate Behavioral Research, 40(1), 83–115. https://doi.org/10.1207/s15327906mbr4001_4

Rovine, M. J., & Anderson, D. R. (2004). Peirce and Bowditch: An American contribution to correlation and regression. American Statistician, 59(3), 232–236. https://doi.org/10.1198/000313004X964

Rovine, M. J., & Molenaar, P. C. M. (2001). A structural equations modeling approach to the general linear mixed model. In L. Collins & A. Sayer (Eds.), New methods for the analysis of change (pp. 65–96). American Psychological Association Press.

Rovine, M. J., & Molenaar, P. C. M. (2000). A structural modeling approach to the random coefficients model. Multivariate Behavioral Research, 35(1), 51–88. https://doi.org/10.1207/S15327906MBR3501_3

Rovine, M. J., & von Eye, A. (1997). A 14th way to look at a correlation coefficient: Correlation as the proportion of matches. American Statistician, 51(1), 42–46. https://doi.org/10.1080/00031305.1997.10473586

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