IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/ha4cw.html
   My bibliography  Save this paper

Network Model Trees

Author

Listed:
  • Jones, Payton J.
  • Mair, Patrick
  • Simon, Thorsten
  • Zeileis, Achim

Abstract

In many areas of psychology, correlation-based network approaches (i.e., psychometric networks) have become a popular tool. In this paper we define a statistical model for correlation-based networks and propose an approach that recursively splits the sample based on covariates in order to detect significant differences in the network structure. We adapt model-based recursive partitioning and conditional inference tree approaches for finding covariate splits in a recursive manner. This approach is implemented in the networktree R package. The empirical power of these approaches is studied in several simulation conditions. Examples are given using real-life data from personality and clinical research.

Suggested Citation

  • Jones, Payton J. & Mair, Patrick & Simon, Thorsten & Zeileis, Achim, 2019. "Network Model Trees," OSF Preprints ha4cw, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:ha4cw
    DOI: 10.31219/osf.io/ha4cw
    as

    Download full text from publisher

    File URL: https://osf.io/download/5d4096bc26ebf5001b852340/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/ha4cw?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Epskamp, Sacha & Cramer, Angélique O.J. & Waldorp, Lourens J. & Schmittmann, Verena D. & Borsboom, Denny, 2012. "qgraph: Network Visualizations of Relationships in Psychometric Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i04).
    2. Edgar Merkle & Jinyan Fan & Achim Zeileis, 2014. "Testing for Measurement Invariance with Respect to an Ordinal Variable," Psychometrika, Springer;The Psychometric Society, vol. 79(4), pages 569-584, October.
    3. Hansen, Bruce E, 1997. "Approximate Asymptotic P Values for Structural-Change Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 60-67, January.
    4. Hothorn, Torsten & Hornik, Kurt & van de Wiel, Mark A. & Zeileis, Achim, 2006. "A Lego System for Conditional Inference," The American Statistician, American Statistical Association, vol. 60, pages 257-263, August.
    5. Donald W. K. Andrews, 2003. "Tests for Parameter Instability and Structural Change with Unknown Change Point: A Corrigendum," Econometrica, Econometric Society, vol. 71(1), pages 395-397, January.
    6. Achim Zeileis & Kurt Hornik, 2007. "Generalized M‐fluctuation tests for parameter instability," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 61(4), pages 488-508, November.
    7. Edgar Merkle & Achim Zeileis, 2013. "Tests of Measurement Invariance Without Subgroups: A Generalization of Classical Methods," Psychometrika, Springer;The Psychometric Society, vol. 78(1), pages 59-82, January.
    8. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    9. Seibold Heidi & Zeileis Achim & Hothorn Torsten, 2016. "Model-Based Recursive Partitioning for Subgroup Analyses," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 45-63, May.
    10. Sacha Epskamp & Mijke Rhemtulla & Denny Borsboom, 2017. "Generalized Network Psychometrics: Combining Network and Latent Variable Models," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 904-927, December.
    11. Mair, Patrick & de Leeuw, Jan, 2010. "A General Framework for Multivariate Analysis with Optimal Scaling: The R Package aspect," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i09).
    12. Carolin Strobl & Julia Kopf & Achim Zeileis, 2015. "Rasch Trees: A New Method for Detecting Differential Item Functioning in the Rasch Model," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 289-316, June.
    13. Carolin Strobl & Florian Wickelmaier & Achim Zeileis, 2011. "Accounting for Individual Differences in Bradley-Terry Models by Means of Recursive Partitioning," Journal of Educational and Behavioral Statistics, , vol. 36(2), pages 135-153, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Payton J. Jones & Patrick Mair & Thorsten Simon & Achim Zeileis, 2020. "Network Trees: A Method for Recursively Partitioning Covariance Structures," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 926-945, December.
    2. repec:osf:osfxxx:ha4cw_v1 is not listed on IDEAS
    3. K. B. S. Huth & L. J. Waldorp & J. Luigjes & A. E. Goudriaan & R. J. Holst & M. Marsman, 2022. "A Note on the Structural Change Test in Highly Parameterized Psychometric Models," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 1064-1080, September.
    4. Ting Wang & Carolin Strobl & Achim Zeileis & Edgar C. Merkle, 2018. "Score-Based Tests of Differential Item Functioning via Pairwise Maximum Likelihood Estimation," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 132-155, March.
    5. Ting Wang & Carolin Strobl & Achim Zeileis & Edgar C. Merkle, 2016. "Score-Based Tests of Differential Item Functioning in the Two-Parameter Model," Working Papers 2016-05, Faculty of Economics and Statistics, Universität Innsbruck.
    6. Carolin Strobl & Julia Kopf & Achim Zeileis, 2015. "Rasch Trees: A New Method for Detecting Differential Item Functioning in the Rasch Model," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 289-316, June.
    7. Gutiérrez-Vargas, Álvaro A. & Meulders, Michel & Vandebroek, Martina, 2023. "Modeling preference heterogeneity using model-based decision trees," Journal of choice modelling, Elsevier, vol. 46(C).
    8. Edgar Merkle & Achim Zeileis, 2013. "Tests of Measurement Invariance Without Subgroups: A Generalization of Classical Methods," Psychometrika, Springer;The Psychometric Society, vol. 78(1), pages 59-82, January.
    9. Ting Wang & Benjamin Graves & Yves Rosseel & Edgar C. Merkle, 2022. "Computation and application of generalized linear mixed model derivatives using lme4," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 1173-1193, September.
    10. Edgar C. Merkle & Achim Zeileis, 2011. "Generalized Measurement Invariance Tests with Application to Factor Analysis," Working Papers 2011-09, Faculty of Economics and Statistics, Universität Innsbruck.
    11. Hyeongwoo Kim & Ying Lin & Henry Thompson, 2021. "Exchange Rate Pass-Through to Consumer Prices: The Increasing Role of Energy Prices," Open Economies Review, Springer, vol. 32(2), pages 395-415, April.
    12. Hyeongwoo Kim & Ying Lin, 2018. "Exchange Rate Pass-Through to Consumer Prices and the Role of Energy Prices," Auburn Economics Working Paper Series auwp2018-05, Department of Economics, Auburn University.
    13. Mishra, Bibhuti Ranjan & Pradhan, Ashis Kumar & Tiwari, Aviral Kumar & Shahbaz, Muhammad, 2019. "The dynamic causality between gold and silver prices in India: Evidence using time-varying and non-linear approaches," Resources Policy, Elsevier, vol. 62(C), pages 66-76.
    14. repec:rim:rimwps:22-04 is not listed on IDEAS
    15. Gómez-Puig, Marta & Sosvilla-Rivero, Simón, 2014. "Causality and contagion in EMU sovereign debt markets," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 12-27.
    16. Ulrich Fritsche & Vladimir Kuzin, 2005. "Declining output volatility in Germany: impulses, propagation, and the role of monetary policy," Applied Economics, Taylor & Francis Journals, vol. 37(21), pages 2445-2457.
    17. Rebeca Jiménez-Rodríguez, 2004. "Oil Price Shocks: Testing for Non-linearity," CSEF Working Papers 115, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    18. Bertrand Groslambert & Raphaël Chiappini & Olivier Bruno, 2015. "Bank Output Calculation in the Case of France: What Do New Methods Tell About the Financial Intermediation Services in the Aftermath of the Crisis?," GREDEG Working Papers 2015-32, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    19. Gianna Boero & Jeremy Smith & Kenneth F. Wallis, 2008. "Uncertainty and Disagreement in Economic Prediction: The Bank of England Survey of External Forecasters," Economic Journal, Royal Economic Society, vol. 118(530), pages 1107-1127, July.
    20. Jean Boivin & Marc P. Giannoni, 2006. "Has Monetary Policy Become More Effective?," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 445-462, August.
    21. Brent Bundick & Andrew Lee Smith, 2020. "Did the Federal Reserve Break the Phillips Curve? Theory and Evidence of Anchoring Inflation Expectations," Research Working Paper RWP 20-11, Federal Reserve Bank of Kansas City.
    22. Ting Wang & Edgar C. Merkle & Achim Zeileis, 2013. "Score-Based Tests of Measurement Invariance: Use in Practice," Working Papers 2013-33, Faculty of Economics and Statistics, Universität Innsbruck.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:ha4cw. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.