Michael Rovine

Senior Fellow

Human Development and Quantitative Methods Division

Graduate School of Education
University of Pennsylvania

mrovine@upenn.edu

Professional 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.

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.
 

Selected Publications

McDermott, P. A., Rovine, M. J., Watkins, M. W., Chao, J. L., Irwin, C. W., & Reyes, R. (in press). Latent national subpopulations of early education classroom disengagement of children from under-resourced families. Journal of School Psychology.

Lo, L., Molenaar, P., & Rovine, M. (2017). Determining the number of factors in P-technique factor analysis. Applied Developmental Science. DOI: 10.1080/10888691.2016.1173549

Liu, Y., Almeida, D., Rovine, M., & Zarit, S. (2017). Care transitions and adult day services moderate the longitudinal links between stress biomarkers and family caregivers’ functional health. Gerontology. DOI: 10.1159/000475557

Rovine, M., & Lo, L. (2017). Person-specific individual approaches in developmental research. In N. Card (Ed.), Monographs of the Society for Research in Child Development. DOI: 10.1111/mono.12300

Zarit, S., Bangerter, L., Liu, Y., & Rovine, M. (2017). Exploring the benefits of respite services to family caregivers: Methodological issues and current findings. Aging and Mental Health. DOI: 10.1080/13607863.2015.1128881.

Meeks, S., Van Haitsma, K., Mast, B., Arnold, S., Streim, J., Sephton, S., Smith, P., Kleban, M., & Rovine, M. J. (2016). Psychological and social resources relate to biomarkers of allostasis in newly admitted nursing home residents. Aging and Mental Health. DOI: 10.1080/13607863.2015.1072796 

Padilla, J., McHale, S., Rovine, M., Updegraff, K., & Umana-Taylor, A. (2016). Parent-youth differences in family values from adolescence into young adulthood: Developmental course and links with parent-youth conflict. Journal of Youth and Adolescence. DOI: 10.1007/s10964-016-0518-y

Liu, Y., Almeida, D., Rovine, M., & Zarit, S. (2016). Modeling daily cortisol rhythms of family caregivers of individuals with dementia: Daily stressors and adult day services (ADS) use. Journal of Gerontology: Psychological Sciences. DOI: 10.1093/geronb/gbw140

Lee, J., Zarit, S., Rovine, M., Birditt, K., & Fingerman, K. (2016). The interdependence of relationships with adult children and spouses. Family Relations, 65(2), 342-353. DOI: 10.1111/fare.12188

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). New York: 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.

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, 259-267. 

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.       

Liu, S., Rovine, M. J., Almeida, D., & Klein, L. (2013). Synchrony of diurnal Cortisol patterns in couples. Journal of Family Psychology, 27, 579–588.

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, 97–115.

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.

Liu, S., Rovine, M. J., & Molenaar, P. (2012). Using fit indices to select a covariance model for longitudinal data. Structural Equation Modeling, 19(4), 633–650.

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). New York, NY: 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). New York, NY: 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). New York, NY: 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). New York, NY: 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). Washington, D.C.: APA 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). New York, NY: 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.

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

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). Washington, D.C.: APA 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.

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, 42–46.