Archive for March, 2018

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.