Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2025-01-05T14:58:24.346Z Has data issue: false hasContentIssue false
  • English
  • Français

OpenMx: An Open Source Extended Structural Equation Modeling Framework

Published online by Cambridge University Press:  01 January 2025

Steven Boker ,
Michael Neale ,
Hermine Maes ,
Michael Wilde ,
Michael Spiegel ,
Timothy Brick ,
Jeffrey Spies ,
Ryne Estabrook ,
Sarah Kenny  and
Timothy Bates
...Show all authors
Steven Boker*
Affiliation:
University of Virginia
Michael Neale
Affiliation:
Virginia Commonwealth University
Hermine Maes
Affiliation:
Virginia Commonwealth University
Michael Wilde
Affiliation:
University of Chicago, Argonne National Labs
Michael Spiegel
Affiliation:
University of Virginia
Timothy Brick
Affiliation:
University of Virginia
Jeffrey Spies
Affiliation:
University of Virginia
Ryne Estabrook
Affiliation:
University of Virginia
Sarah Kenny
Affiliation:
University of Chicago, Argonne National Labs
Timothy Bates
Affiliation:
University of Edinburgh
Paras Mehta
Affiliation:
University of Houston
John Fox
Affiliation:
McMaster University
*
Correspondence may be addressed to Steven M. Boker, Department of Psychology, The University of Virginia, PO Box 400400, Charlottesville, VA 22903, USA; email sent to [email protected]; or browsers pointed to http://openmx.psyc.virginia.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

Keywords

structural equation modelingSEMsoftwareopen sourceOpenMx
Type
Original Paper
Information
Psychometrika , Volume 76 , Issue 2 , April 2011 , pp. 306 - 317
Copyright
Copyright © 2011 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Arbuckle, J.L. (2009). Amos user’s guide, Chicago: SPSS.Google Scholar
Bentler, P.M. (2009). EQS structural equations program manual, Encino: Multivariate Software.Google Scholar
Browne, M.W., Mels, G. (2009). RAMONA: SYSTAT for Windows, Chicago: SYSTAT.Google Scholar
Eaves, L.J., Neale, M.C., Maes, H.H. (1996). Multivariate multipoint linkage analysis of quantitative trait loci. Behavior Genetics, 26, 519–526.CrossRefGoogle ScholarPubMed
Everitt, B.S., Hand, D.J. (1981). Finite mixture distributions, London: Chapman and Hall.CrossRefGoogle Scholar
Fox, J. (2009). sem: structural equation models (Computer software manual). Available from http://CRAN.R-project.org/package=sem (R package version 0.9–19).Google Scholar
Ihaka, R., Gentleman, R. (1996). R: a language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5(3), 299–314.CrossRefGoogle Scholar
Jöreskog, K.G. (1967). Some contributions to maximum likelihood factor analysis. Psychometrika, 32, 443–482.CrossRefGoogle Scholar
Jöreskog, K.G., Sörbom, D. (2009). LISREL, Chicago: Scientific Software International.Google Scholar
Longford, N.T., Muthén, B. (1992). Factor analysis for clustered observations. Psychometrika, 57, 581–597.CrossRefGoogle Scholar
Marcoulides, G., Schumacker, E. (1996). Advanced structural equation modeling, Hillsdale: Erlbaum.Google Scholar
McArdle, J.J., Hamagami, F. (1996). Multilevel models from a multiple group structural equation perspective. In Marcoulides, G., Schumacker, E. (Eds.), Advanced structural equation modeling (pp. 89–124). Hillsdale: Erlbaum.Google Scholar
McLachlan, G.J., Peel, D. (2000). Finite mixture models, New York: Wiley.CrossRefGoogle Scholar
Moore, G.E. (1965). Cramming more components onto integrated circuits. Electronics, 38(8).Google Scholar
Muthén, L.K., Muthén, B.O. (2009). Mplus user’s guide, Los Angeles: Muthén & Muthén.Google Scholar
Neale, M.C. (1998). Modeling interaction and nonlinear effects with mx: a general approach. In Marcoulides, G., Schumacker, R. (Eds.), Interaction and non-linear effects in structural equation modeling (pp. 43–61). Hillsdale: Erlbaum.Google Scholar
Neale, M.C., Boker, S.M., Xie, G., Maes, H. (2006). Mx: statistical modeling, (7th ed.). Richmond: Department of Psychiatry, Virginia Commonwealth University.Google Scholar
PROC CALIS (Computer software manual). Cary, SAS Institute, Inc.Google Scholar
SEPath (Computer software manual) (2009). Tulsa, StatSoft, Inc.Google Scholar
Sörbom, D. (1974). A general method for studying differences in factor means and factor structures between groups. British Journal of Mathematical & Statistical Psychology, 27, 229–239.CrossRefGoogle Scholar
Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20, 557–585.Google Scholar