Michael J. Rovine
Professor of Human Development and Family Studies
416 BBH Building
The Pennsylvania State University
University Park PA 16802
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. As part of this work, I am also interested in the relationships (including equivalences and differences) of models based on the general linear mixed model, including a comparison of repeated measures ANOVA and multilevel growth curve modeling.
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 completed an NSF funded study to develop and apply time series models to developmental data. We worked with a number of different models including multilevel ARMA, state space, and control models. Cynthia Stifter and I are continuing this work with a special emphasis on hidden Markov modeling which we have used to model mother-infant interactions. This work is currently being funded through a National Institutes of Health grant (Pamela Cole, PI). Along with Peter Molenaar and Carol Gold we are looking at the possibility of modeling and eventually controlling the incidence of symptoms and attacks in asthma sufferers. A brief description of each 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.
Application of Time Series Modeling Techniques for Optimal Control of Asthmatic Symptoms at the Person-Specific Level, Pilot Study. Michael J. Rovine (PI), Penn State University.
Asthma requires daily monitoring of lung function, which can be affected by variations in medication usage, in addition to a number of different behaviors and triggers. Problems associated with an inappropriate type or dosage level of medication are especially important. Despite our recognition of the variability in patients’ responses to various types of medications and dosage levels, little has been put into place that would monitor a patient’s responses to medication in a continuing way so that proper adjustments can be made for the duration of the use of that drug therapy. The determinants of reduced lung capacity and more severe attacks also vary by individual. Modeling the relationship between levels of stress, exercise, and exposure to environmental triggers such as smoke (first-hand and second-hand) and pollen and the resultant lung function represents another important challenge in this study.
Our approach seeks to optimize the effectiveness of ongoing medical treatment by using adaptive control techniques. These are time series modeling techniques that are used by engineers to ensure the best possible outcomes in dynamic processes. Such models can be accommodated to the needs of medicine and social sciences to increase the positive results of interventions. The basic idea is that first a criterion for determining a good outcome is established (e.g., minimum asthma symptoms with minimum medication dose). Repeated measurements are taken to monitor the outcome. As soon as the outcome deviates from the criterion, the level or content of the intervention is modified to counteract this deviation. At the next measurement occasion, the direction of the outcome is checked for improvement and, if necessary, subsequent modifications of the intervention are made. Unlike many other intervention strategies, the degree or content of the intervention can be adjusted on an individual basis.
In the first phase of this study we collected data to be the basis for modeling the relationships indicated above. We currently have intensive data on 15 participants including measures of expiratory lung capacity, incidence of attacks, and triggers along with a number of background variables.
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
2007- present: Professor of Human Development, Department of Human Development and Family Studies, College of Health and Human Development, Penn State University
2012- present: Adjunct Professor of Education, College of Education, University of Pennsylvania
2011-2012: Visiting Professor, Applied Psychology and Human Development, University of Pennsylvania
2004-2014: 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
1991-2007: Associate Professor of Human Development, Department of Human Development and Family Studies, College of Health and Human Development, Penn State University
1992-2004: Associate Director, Center for Developmental and Health Research Methodology, College of Health and Human Development, Penn State University
1993-1994: Acting Director, Center for Developmental and Health Research Methodology, College of Health and Human Development, Penn State University
1990-1993: Research Associate, Center for Developmental and Health Research Methodology, College of Health and Human Development, Penn State University
1984-1991: Assistant Professor of Human Development, Department of Human Development and Family Studies, College of Health and Human Development, Penn State University
Rovine, M., & Molenaar, P. (2014, in press). Person-specific approaches to the modeling of intra-individual variation in developmental psychopathology. In D. Cicchetti, (Ed.), Developmental Psychopathology.
Stifter, C., & Rovine, M. (2014, in press). Modeling dyadic processes using hidden Markov models: A time series approach to mother-infant interactions during infant immunization. Infant and Child Development.
Rovine, M., & Lo, L. (2014, in press). Person-specific individual approaches in developmental research. In Noel Card (Ed.), Monographs of the Society for Research in Child Development.
Liu, S., Rovine, M., & 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., & 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: Fordham University Press.
Rovine, M., & 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., & 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., & 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.