Archive for the ‘Publications’ Category

A Multilevel Multivariate Analysis of Academic Success in College based on NCAA Student-Athletes

Thursday, May 23rd, 2013

McArdle, J. J., Paskus, T. S. & Boker, S. M. (2013) A Multilevel Multivariate Analysis of Academic Success in College based on NCAA Student-Athletes. Multivariate Behavioral Research, 48:1, 57–95.

This is an application of contemporary multilevel regression modeling to the prediction of academic performances of 1st-year college students. At a first level of analysis, the data come from N > 16,000 students who were college freshman in 1994–1995 and who were also participants in high-level college athletics. At a second level of analysis, the student data were related to the different characteristics of the C = 267 colleges in Division I of the NCAA. The analyses presented here initially focus on the prediction of freshman GPA from a variety of high school academic variables. The models used are standard multilevel regression models, but we examine nonlinear prediction within these multilevel models, and additional outcome variables are considered. The multilevel results show that (a) high school grades are the best available predictors of freshman college grades, (b) the ACT and SAT test scores are the next best predictors available, (c) the number of high school core units taken does not add to this prediction but does predict credits attained, (d) college graduation rate has a second-level effect of a small negative outcome on the average grades, and (e) nonlinear models indicate stronger effects for students at higher levels of the academic variables. These results show that standard multilevel models are practically useful for standard validation studies. Some difficulties were found with more advanced uses and interpretations of these techniques, and these problems lead to suggestions for further research.

The manuscript of this article accepted for publication can be requested as a pdf file from Steve Boker.

Selection, Optimization, Compensation, and Equilibrium Dynamics

Thursday, May 23rd, 2013

Boker, S. M. (2013) Selection, Optimization, Compensation, and Equilibrium Dynamics. The Journal of Gerontopsychology and Geriatric Psychiatry, 26:1, 61-73.

One of the major theoretic frameworks through which human development is studied is a process-oriented model involving selection, optimization, and compensation. These three processes each provide accounts for methods by which gains are maximized and losses minimized throughout the lifespan, and in particular during later life. These processes can be cast within the framework of dynamical systems theory and then modeled using differential equations. The current article will review basic tenets of selection, optimization, and compensation whilst introducing language and concepts from dynamical systems. Four categories of interindividual differences and intraindividual variability in dynamics are then described and discussed in the context of selection, optimization, and compensation.

The manuscript of this article accepted for publication can be downloaded as a PDF. This article may not exactly replicate the final version published in The Journal of Gerontopsychology and Geriatric Psychiatry. It is not the copy of record.

The Interactive Effects of Estrogen and Progesterone on Changes in Binge Eating Across the Menstrual Cycle

Thursday, May 23rd, 2013

Klump, K. L., Keel, P. K., Racine, S., Burt, S. A., Sisk, C. L., Neale, M., Boker, S. M. & Hu, J. Y. (2013) The Interactive effects of Estrogen and Progesterone on Changes in Binge Eating Across the Menstrual Cycle. Journal of Abnormal Psychology, 122:1, 131–137.

Studies suggest that within-person changes in estrogen and progesterone predict changes in binge eating across the menstrual cycle. However, samples have been extremely small (maximum N = 9), and analyses have not examined the interactive effects of hormones that are critical for changes in food intake in animals. The aims of the current study were to examine ovarian hormone interactions in the prediction of within-subject changes in emotional eating in the largest sample of women to date (N = 196). Participants provided daily ratings of emotional eating and saliva samples for hormone measurement for 45 consecutive days. Results confirmed that changes in ovarian hormones predict changes in emotional eating across the menstrual cycle, with a significant estradiol x progesterone interaction. Emotional eating scores were highest during the midluteal phase, when progesterone peaks and estradiol demonstrates a secondary peak. Findings extend previous work by highlighting significant interactions between estrogen and progesterone that explain midluteal increases in emotional eating. Future work should explore mechanisms (e.g., gene–hormone interactions) that contribute to both within- and between- subjects differences in emotional eating.

The full text of this article can be downloaded from APA Psycnet as a PDF.

Latent Differential Equations with Moderators: Simulation and Application

Wednesday, January 16th, 2013

Hu, Y., Boker, S. M., Neale, M. C. & Klump, K. (in press) Latent Differential Equations with Moderators: Simulation and Application. Psychological Methods

Latent Differential Equations (LDE) is an approach using differential equations to analyze time series data. Due to its recent development, some technique issues critical to performing an LDE model remain. This article provides solutions to some of these issues, and recommends a step-by-step procedure demonstrated on a set of empirical data, which models the interaction between ovarian hormone cycles and emotional eating. Results indicated that emotional eating is self-regulated. For instance, when people have more emotional eating behavior than normal, they will subsequently tend to decrease their emotional eating behavior. In addition, a sudden increase will produce a stronger tendency to decrease than a slow increase. We also found that emotional eating is coupled with the cycle of the ovarian hormone estradiol, and the peak of emotional eating occurs after the peak of estradiol. Self-reported average level of negative affect moderates the frequency of eating regulation and the coupling strength between eating and estradiol. Thus, people with a higher average level of negative affect tend to fluctuate faster in emotional eating, and their eating behavior is more strongly coupled with the hormone estradiol. Permutation tests on these empirical data supported the reliability of using LDE models to detect self-regulation and a coupling effect between two regulatory behaviors.

The full text of this article can be dowloaded from APA Psycnet as a PDF.

A Differential Equations Model for the Ovarian Hormone Cycle

Monday, December 17th, 2012

Boker, S. M., Neale, M. C. & Klump, K. L. (in press) A Differential Equations Model for the Ovarian Hormone Cycle. In Handbook of Relational Developmental Systems: Emerging Methods and Concepts, P. C. Molenaar, R. Lerner, & K. Newell (Eds). New York: John Wiley & Sons

Dynamical systems models of behavior and regulation have become increasingly popular due to the promise that within-person mechanisms can be modeled and explained. However, it can be difficult to construct differential equation models of regulatory dynamics which test specific theoretically interesting mechanisms. The current chapter uses the example of ovarian hormone regulation and develops a model step by step in order for the model to be able to capture features of observed hormone levels as well as to link parameters of the model to biological mechanisms. Ovarian hormones regulate the monthly female reproductive cycle and have been implicated as having effects on affective states and eating behavior. The three major hormones in this system are estrogen, progesterone, and lutenizing hormone. These hormones are coupled together as a regulatory system. Estrogen level is associated with the release of lutenizing hormone by the hypothalamus. Lutenizing hormone triggers ovulation and the transformation of the dominant follicle into the corpus luteus which in turn produces progesterone. A differential equations model is developed that is biologically plausible and produces nonlinear cycling similar to that seen in a large ongoing daily-measure study of ovarian hormones and eating behavior.

The manuscript of this article accepted for publication can be requested as a pdf file from the first author: Steve Boker.

On the Equilibrium Dynamics of Meaning

Monday, December 17th, 2012

Boker, S. M. & Martin, M. (in press) On the Equilibrium Dynamics of Meaning. In Current Issues in the Theory and Application of Latent Variable Models, M. Edwards & R. MacCallum, (Eds). New York: Taylor & Francis.

Meaning is at the heart of what we do in latent variable modeling. A latent construct is a way to aggregate and focus meaning into quantifiable constructs. Structural models, and in particular factor models, are a way to use the considerable power of product moment matrices to focus meaning in such a way that it aggregates across participants (or within participant across time) in the hope that the meaning that the psychologist had in mind is the meaning that emerges in the latent variable indicated by the participants’ responses. But, what is meaning? And how is it attached to words or utterances? Philosophers of language have written about this problem, and so we review some recent arguments in epistemology in order to build the central thesis of this paper: If one takes context into account, intraindividual meanings are likely to have intrinsic dynamics that tend towards stable equilibria. We then discuss the implications from a lifespan psychological perspective for the meaning of an example variable: quality of life. Finally, some we discuss some ideas about what might be necessary in order to specify a factor analysis of sufficiency rather than a factor analysis of aggregation.

The manuscript of this article accepted for publication can be requested as a pdf file from the first author: Steve Boker.

Dynamical Systems and Differential Equation Models of Change

Monday, December 17th, 2012

Boker, S. M. (2012) Dynamical Systems and Differential Equation Models of Change. In APA Handbook of Research Methods in Psychology, Volume 3, H. Cooper, A. Panter, P. Camic, R. Gonzalez, D. Long, & K. Sher, (Eds). Washington, DC: American Psychological Association, pp 323-333.

This chapter provides a brief introduction to dynamical systems modeling from the standpoint of latent differential equations.

The manuscript of this article accepted for publication can be requested as a pdf file from the author: Steve Boker.

Correlational Methods for Analysis of Dance Movements

Monday, December 17th, 2012

Brick, T. R. & Boker, S. M. (2011) Correlational Methods for Analysis of Dance Movements. Dance Research, Special Electronic Issue: Dance and Neuroscience: New Partnerships, 29:2, 283–304

We propose to present three methods for the exploration of symmetry and synchrony in motion-capture data as is it applied to dance and illustrate these with examples from a study of free-form dance. First, Generalized Local Linear Approximation (GLLA) provides a transformation from the positional data returned by such a system into a representation including approximations of velocity and acceleration. Using these newly transformed data, time-lagged autocorrelation can provide insight into the structure of temporal symmetry within a single person’s movements during the dance. Windowed cross-correlation can show other kinds of symmetry between individuals or between an individual and an outside stimuli, such as a rhythm or musical work. Combined, these techniques provide powerful tools for the examination of the structure of symmetry and synchrony in dance.

The manuscript of this article accepted for publication can be requested as a pdf file from the first author: Timothy Brick at the Max Planck Institute for Human Development.

OpenMx: An Open Source Extended Structural Equation Modeling Framework

Monday, December 17th, 2012

Boker, S. M., Neale, M., Maes, H., Wilde, M., Spiegel, M., Brick, T., Spies, J., Estabrook, R., Kenny, S., Bates, T., Mehta, P., & Fox, J. (2011) OpenMx: An Open Source Extended Structural Equation Modeling Framework. Psychometrika, 76:2, 306-317. NIHMS ID: 427396

OpenMx is free, full-featured, open source, structural equation modeling (SEM) software. OpenMx runs within the R statistical programming environment on Windows, Mac OS-X, and Linux computers. The rationale for developing OpenMx is discussed along with the philosophy behind the user interface. The OpenMx data structures are introduced — these novel structures define the user interface framework and provide new opportunities for model specification. Two short example scripts for the specification and fitting of a confirmatory factor model are next presented. We end with an abbreviated list of modeling applications available in OpenMx 1.0 and a discussion of directions for future development.

The manuscript of this article accepted for publication can be downloaded as a PDF. This article may not exactly replicate the final version published in Psychometrika. It is not the copy of record.

Time Delay Embedding Increases Estimation Precision of Models of Intraindividual Variability

Monday, December 17th, 2012

Oertzen, T. v. & Boker, S. (2010) Time Delay Embedding Increases Estimation Precision of Models of Intraindividual Variability. Psychometrika, 75:1, 158-175. NIHMS ID: 427398

This paper investigates the precision of parameters estimated from local samples of time dependent functions. We find that time delay embedding, i.e., structuring data prior to analysis by constructing a data matrix of overlapping samples, increases the precision of parameter estimates and in turn statistical power compared to standard independent rows of panel data. We show that the reason for this effect is that the sign of estimation bias depends on the position of a misplaced data point if there is no a priori knowledge about initial conditions of the time dependent function. Hence, we reason that the advantage of time delayed embedding is likely to hold true for a wide variety of functions. We support these conclusions both by mathematical analysis and two simulations.

The manuscript of this article accepted for publication can be downloaded as a PDF. This article may not exactly replicate the final version published in Psychometrika. It is not the copy of record.