Posts Tagged ‘Structural Equation Modeling’

OpenMx 2.0: Extended Structural Equation and Statistical Modeling

Thursday, November 6th, 2014

Neale, M.C., Hunter, M.D., Pritikin, J.N., Zahery, M., Brick, T.R., Kirkpatrick, R., Estabrook, R., Bates, T.C., Maes, H.H., & Boker, S.M.; (in press) OpenMx 2.0: Extended Structural Equation and Statistical Modeling. Psychometrika

The new software package OpenMx 2.0 for structural equation and other statistical modeling is introduced and its features are described. OpenMx is evolving in a modular direction and now allows a mix-and-match computational approach that separates model expectations from fit functions and optimizers. Major backend architectural improvements include a move to swappable open-source optimizers such as the newly-written CSOLNP. Entire new methodologies such as Item Factor analysis (IRT) and State-space modeling have been implemented. New model expectation functions including support for the expression of models in LISREL syntax and a simplified multigroup expectation function are available. Ease-of-use improvements include helper functions to standardize model parameters and compute their Jacobian-based standard errors, access to model components through standard R $ mechanisms, and improved tab completion from within the R Graphical User Interface.

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.

Modular Open-Source Software for Item Factor Analysis

Wednesday, November 5th, 2014

Pritikin, J. N., Hunter, M. D., & Boker, S. M. (2015) Modular Open-Source Software for Item Factor Analysis. Educational and Psychological Measurement 75:(3), 458-474

This paper introduces an Item Factor Analysis (IFA) module for OpenMx, a free, open-source, and modular statistical modeling package that runs within the R programming environment on GNU/Linux, Mac OS X, and Microsoft Windows. The IFA module offers a novel model specification language that is well suited to programmatic generation and manipulation of models. Modular organization of the source code facilitates the easy addition of item models, item parameter estimation algorithms, optimizers, test scoring algorithms, and fit diagnostics all within an integrated framework. Three short example scripts are pre- sented for fitting item parameters, latent distribution parameters, and a multiple group model. The availability of both IFA and structural equation modeling in the same software is a step toward the unification of these two methodologies.

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 Educational and Psychological Measurement. It is not the copy of record.

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.

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.

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.

Parallel Workflows for Data Driven Structural Equation Modeling in Functional Neuroimaging

Tuesday, October 6th, 2009

Kenny, S., Andric, M., Boker, S. M., Neale, M. C., Wilde, M., & Small, S. L. (2009) Parallel Workflows for Data–Driven Structural Equation Modeling in Functional Neuroimaging. Frontiers in Neuroscience

This article presents a computational framework suitable for a data-driven approach to structural equation modeling (SEM) and describe several workflows for modeling functional magnetic resonance imaging (fMRI) data within this framework. The Computational Neuroscience Applications Research Infrastructure (CNARI) employs a high-level scripting language called Swift, which is capable of spawning hundreds of thousands of simultaneous R processes (R Core Development Team, 2008), consisting of self-contained structural equation models, on a high performance computing system (HPC). These self-contained R processing jobs are data objects generated by OpenMx, a plug-in for R, which can generate a single model object containing the matrices and algebraic information necessary to estimate parameters of the model. With such an infrastructure in place a structural modeler may begin to investigate exhaustive searches of the model space. Specific applications of the infrastructure, statistics related to model fit, and limitations are discussed in relation to exhaustive SEM. In particular, we discuss how workflow management techniques can help to solve large computational problems in neuroimaging.

Frontiers in Neuroscience is an open-access journal, so a PDF of the article can be downloaded for free.