Michael J. Rovine
Professor of Human Development and Family Studies
416 BBH Building
The Pennsylvania State University
University Park PA 16802
B.S., 1971, Mathematics, University of Pennsylvania
M.S., 1979, Ed. Psychology, The Pennsylvania State University
Ph.D., 1982, Ed. Psychology, The Pennsylvania State University
My current interests are in areas related to statistical modeling. In the area of structural equations modeling, I am looking at ways to estimate a number of different multilevel models as SEM. One model of particular interest is a multilevel autoregressive model that could have important implications for those collecting relatively intensive time series data. I am also working on a general model, the nonstationary autoregressive moving average model that can be used to describe essentially any latent variable model. This general model has important implications for model comparison and testing.
Another interest of mine relates to the history of statistics, in particular the contributions of the philosopher C.S. Peirce to the development of statistical methodology. My interest in this area was sparked by my attempts to develop variations of the correlation coefficient that could be used to describe effect sizes in uncontrolled studies. Looking to see whether similar work had been done in the past, I discovered an interesting history of correlation and regression that predated the better known work of Pearson and Galton.
My main focus, however, is on a more idiographic approach to the description of developmental phenomena. Along with Erik Loken (HDFS), David Nembhard (Engineering), Cynthia Stifter (HDFS) and Peter Molenaar (HDFS), I am beginning an NSF funded study to develop and apply time series models to developmental data. We will be working with a number of different models including multilevel ARMA, state space, and control models. A brief abstract for the study follows.
Multilevel ARMA and Dynamic Models for the Longitudinal Study of Human Interactions. Michael J. Rovine (PI), Penn State University.
Time series methods are used to model phenomena as varied as patterns in the weather, fluctuations in the stock market, changes in populations, quality of industrial products, patterns of sleep, physiological characteristics such as heart rate, blood pressure, and brain wave activity, and the flight of the space shuttle. The use of these methods, which are so common in the areas of engineering and econometrics and which seem so naturally suited for application in the study of human interactions have been surprisingly overlooked in the developmental sciences. In this proposal we will adapt, extend, and, where necessary, develop new methods that will be particularly well-suited to developmental research and the study of human interactions. We intend to implement these models in ways that will make them easily accessible to developmental researchers studying human interaction. We will demonstrate the utility of these methods by analyzing data from the Infant and Child Temperament Study related to infant's self regulation of emotion, and parent-infant interaction related to the parents ability to soothe a distressed child.
2004-present: Director, Health and Human Development Methodology Consulting Center
2004-2005: Visiting Professor, Applied Psychology and Human Development, University of Pennsylvania
1997-1998: Visiting Professor, Department of Developmental Psychology, University of Amsterdam
1991-Present: Associate Professor of Human Development, Department of Human Development and Family Studies, College of Health and Human Development, The Pennsylvania State University
1992-2004: Associate Director, Center for Developmental and Health Research Methodology, College of Health and Human Development, The Pennsylvania State University
1993-1994: Acting Director, Center for Developmental and Health Research Methodology, College of Health and Human Development, The Pennsylvania University
1990-1993: Research Associate, Center for Developmental and Health Research Methodology, College of Health and Human Development, The Pennsylvania State University
1984-1991: Assistant Professor of Human Development, Department of Human Development and Family Studies, College of Health and Human Development, The Pennsylvania State University
1982-1983: Post-doctoral Research Associate, Infant and Family Development Project, The Pennsylvania State University
1981: Instructor, Department of Educational Psychology, College of Education, The Pennsylvania State University--Altoona Campus
Environmental psychology; structural modeling with both continuous and discrete variables; analyzing longitudinal data.
Rovine, M., & Lo, L. (2011, in press). Issues and perspectives in person specific time series models. In B. Laursen, T. Little, & N. Card (Eds.), Handbook of Developmental Research Methods. New York, NY: Guilford.
Rovine, M., & Liu, S. (2011, in press). Structural equations modeling approaches to longitudinal data. In R. Jones, J. Newsom, & S. Hofer (Eds.), Best practices for data analysis of longitudinal studies of aging. New York, NY: Psychology Press.
Rovine, M. J., & Anderson, D. R. (2011). 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: Fordham University 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: 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. (2003). Estimating analysis of variance models as structural equation models. In B. Pugesek, A. Tomer, & A. von Eye (Eds.), Structural equation modeling: Applications in ecological and evolutionary biology research (235-280). New York: Cambridge.
Rovine, M. J., & Molenaar, P. C. M. (2000). A structural modeling approach to the random coefficients model. Multivariate Behavioral Research, 35(1), 51-58.
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.
Michael Rovine vitae