Archive for the ‘Publications’ Category

Dynamics of Change and Change in Dynamics

Friday, March 23rd, 2018

Boker, S. M., Staples, A., & Hu, Y. (2016) Dynamics of Change and Change in Dynamics. Journal for Person-Oriented Research, 2:1–2, 34–55.

A framework is presented for building and testing models of dynamic regulation by categorizing sources of differences between theories of dynamics. A distinction is made between the dynamics of change, i.e., how a system self–regulates on a short time scale, and change in dynamics, i.e., how those dynamics may themselves change over a longer time scale. In order to clarify the categories, models are first built to estimate individual differences in equilibrium value and equilibrium change. Next, models are presented in which there are individual differences in parameters of dynamics such as frequency of fluctuations, damping of fluctuations, and amplitude of fluctuations. Finally, models for within–person change in dynamics over time are proposed. Simulations demonstrating feasibility of these models are presented and OpenMx scripts for fitting these models have been made available in a downloadable archive along with scripts to simulate data so that a researcher may test selected models’ feasibility within a chosen experimental design.

The article accepted for publication can be downloaded as a PDF.

A Conversation between Theory, Methods, and Data

Friday, March 23rd, 2018

Boker, S. M. & Martin, M. (in press) A Conversation between Theory, Methods, and Data. Multivariate Behavioral Research. DOI: 10.1080/00273171.2018.1437017

The ten year anniversary of the COGITO Study provides an opportunity to revisit the ideas behind the Cattell data box. Three dimensions of the persons × variables × time data box are discussed in the context of three categories of researchers each wanting to answer their own categorically different question. The example of the well-known speed-accuracy tradeoff is used to illustrate why these are three different categories of statistical question. The 200 persons by 100 variables by 100 occasions of measurement COGITO data cube presents a challenge to integrate theories and methods across the dimensions of the data box. A conceptual model is presented for the speed-accuracy tradeoff example that could account for cross-sectional between persons effects, short term dynamics, and long term learning effects. Thus, two fundamental differences between the time axis and the other two axes of the data box include ordering and time scaling. In addition, nonstationarity in human systems is a pervasive problem along the time dimension of the data box. To illustrate, the difference in nonstationarity between dancing and conversation is discussed in the context of the interaction between theory, methods, and data. An information theoretic argument is presented that the theory-methods-data interaction is better understood when viewed as a conversation than as a dance. Entropy changes in the development of a theory-methods-data conversation provide one metric for evaluating scientific progress.

The article accepted for publication can be downloaded as a PDF.

Adaptive Equilibrium Regulation: Modeling Individual Dynamics on Multiple Timescales

Friday, March 23rd, 2018

McKee, K. L., Neale, M. C., Rappaport, L. M., & Boker, S. M. (in press) Adaptive Equilibrium Regulation: Modeling Individual Dynamics on Multiple Timescales. Structural Equation Modeling

Damped Linear Oscillators estimated by 2nd-order Latent Differential Equation (LDE) have assumed a constant equilibrium and one oscillatory component. Lower-frequency oscillations may come from seasonal background processes, which non-randomly contribute to deviation from equilibrium at each occasion and confound estimation of dynamics over shorter timescales. Boker (2015) proposed a model of individual change on multiple timescales, but implementation, simulation, and applications to data have not been demonstrated. This study implemented a generalization of the proposed model; examined robustness to varied timescale ratios, measurement error, and occasions-per-person in simulated data; and tested for dynamics at multiple timescales in experience sampling affect data. Results show small standard errors and low bias to dynamic estimates at timescale ratios greater than 3:1. Below 3:1, estimate error was sensitive to noise and total occasions; rates of non-convergence increased. For affect data, model comparisons showed statistically significant dynamics at both timescales for both participants.

The article accepted for publication can be downloaded as a PDF.

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE)

Friday, March 23rd, 2018

Boker, S. M., Brick, T. R., Pritikin, J. N., Wang, Y., Oertzen, T. v., Brown, D., Lach, J., Estabrook, R., Hunter, M. D., Maes, H. H., & Neale, M. C. (2015) Maintained Individual Data Distributed Likelihood Estimation. Multivariate Behavioral Research, 50:6, 706-720. DOI: 10.1080/00273171.2015.1094387. PMCID 26717128.

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE) is a novel paradigm for research in the behavioral, social, and health sciences. The MIDDLE approach is based on the seemingly-impossible idea that data can be privately maintained by participants and never revealed to researchers, while still enabling statistical models to be fit and scientific hypotheses tested. MIDDLE rests on the assumption that participant data should belong to, be controlled by, and remain in the possession of the participants themselves. Distributed likelihood estimation refers to fitting statistical models by sending an objective function and vector of parameters to each participants’ personal device (e.g., smartphone, tablet, computer), where the likelihood of that individual’s data is calculated locally. Only the likelihood value is returned to the central optimizer. The optimizer aggregates likelihood values from responding participants and chooses new vectors of parameters until the model converges. A MIDDLE study provides significantly greater privacy for participants, automatic management of opt- in and opt-out consent, lower cost for the researcher and funding institute, and faster determination of results. Furthermore, if a participant opts into several studies simultaneously and opts into data sharing, these studies automatically have access to individual-level longitudinal data linked across all studies.

The final draft of the article accepted for publication can be downloaded as a PDF.

Adaptive Equilibrium Regulation: A Balancing Act in Two Timescales

Friday, February 20th, 2015

Boker, S.M.; (2015) Adaptive Equilibrium Regulation: A Balancing Act in Two Timescales. Journal of Person-Oriented Research, 1(1-2), 99-109

An equilibrium involves a balancing of forces. Just as one maintains upright posture in standing or walking, many self-regulatory and interpersonal behaviors can be framed as a balancing act between an ever changing environment and within-person processes. The emerging balance between person and environment, the equilibria, are dynamic and adaptive in response to development and learning. A distinction is made between equilibrium achieved solely due to a short timescale balancing of forces and a longer timescale preferred equilibrium which we define as a state towards which the system slowly adapts. Together, these are developed into a framework that this article calls Adaptive Equilibrium Regulation (AER), which separates a regulatory process into two timescales: a faster regulation that automatically balances forces and a slower timescale adaptation process that reconfigures the fast regulation so as to move the system towards its preferred equilibrium when an environmental force persists over the longer timescale. This way of thinking leads to novel models for the interplay between multiple timescales of behavior, learning, and development.

The article accepted for publication can be downloaded as a PDF.

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.