IDEAS home Printed from https://ideas.repec.org/a/bla/stanee/v62y2008i1p58-82.html
   My bibliography  Save this article

Analyzing reciprocal relationships by means of the continuous‐time autoregressive latent trajectory model

Author

Listed:
  • Marc J. M. H. Delsing
  • Johan H. L. Oud

Abstract

Over the past decades, several analytic tools have become available for the analysis of reciprocal relations in a non‐experimental context using structural equation modeling (SEM). The autoregressive latent trajectory (ALT) model is a recently proposed model [BOLLEN and CURRAN Sociological Methods and Research (2004) Vol. 32, pp. 336–383; CURRAN and BOLLEN New Methods for the Analysis of Change (2001) American Psychological Association, Washington, DC], which captures features of both the autoregressive (AR) cross‐lagged model and the latent trajectory (LT) model. The present article discusses strengths and weaknesses and demonstrates how several of the problems can be solved by a continuous‐time version: the continuous‐time autoregressive latent trajectory (CALT) model. Using SEM to estimate the exact discrete model (EDM), the EDM/SEM continuous‐time procedure is applied to a CALT model of reciprocal relations between antisocial behavior and depressive symptoms.

Suggested Citation

  • Marc J. M. H. Delsing & Johan H. L. Oud, 2008. "Analyzing reciprocal relationships by means of the continuous‐time autoregressive latent trajectory model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 62(1), pages 58-82, February.
  • Handle: RePEc:bla:stanee:v:62:y:2008:i:1:p:58-82
    DOI: 10.1111/j.1467-9574.2007.00386.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9574.2007.00386.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1467-9574.2007.00386.x?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. Kenneth A. Bollen & Patrick J. Curran, 2004. "Autoregressive Latent Trajectory (ALT) Models A Synthesis of Two Traditions," Sociological Methods & Research, , vol. 32(3), pages 336-383, February.
    2. Wolfgang Jagodzinski & Steffen M. Kãœhnel & Peter Schmidt, 1987. "Is there a “Socratic Effect†in Nonexperimental Panel Studies?," Sociological Methods & Research, , vol. 15(3), pages 259-302, February.
    3. Johan Oud & Robert Jansen, 2000. "Continuous time state space modeling of panel data by means of sem," Psychometrika, Springer;The Psychometric Society, vol. 65(2), pages 199-215, June.
    4. Ledyard Tucker, 1958. "Determination of parameters of a functional relation by factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 23(1), pages 19-23, March.
    5. William Meredith & John Tisak, 1990. "Latent curve analysis," Psychometrika, Springer;The Psychometric Society, vol. 55(1), pages 107-122, March.
    6. Bergstrom, A. R., 1988. "The History of Continuous-Time Econometric Models," Econometric Theory, Cambridge University Press, vol. 4(3), pages 365-383, December.
    7. Phillips, P. C. B., 1973. "The problem of identification in finite parameter continuous time models," Journal of Econometrics, Elsevier, vol. 1(4), pages 351-362, December.
    8. Ellen L. Hamaker, 2005. "Conditions for the Equivalence of the Autoregressive Latent Trajectory Model and a Latent Growth Curve Model With Autoregressive Disturbances," Sociological Methods & Research, , vol. 33(3), pages 404-416, February.
    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. Areti Gkypali & Kostas Kounetas & Kostas Tsekouras, 2019. "European countries’ competitiveness and productive performance evolution: unraveling the complexity in a heterogeneity context," Journal of Evolutionary Economics, Springer, vol. 29(2), pages 665-695, April.
    2. Johan Oud & Manuel Voelkle, 2014. "Do missing values exist? Incomplete data handling in cross-national longitudinal studies by means of continuous time modeling," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3271-3288, November.
    3. Eldad Davidov & Stefan Thörner & Peter Schmidt & Stefanie Gosen & Carina Wolf, 2011. "Level and change of group-focused enmity in Germany: unconditional and conditional latent growth curve models with four panel waves," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 481-500, December.
    4. Hermann Singer, 2011. "Continuous-discrete state-space modeling of panel data with nonlinear filter algorithms," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 375-413, December.
    5. John McArdle, 2011. "Longitudinal dynamic analyses of cognition in the health and retirement study panel," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 453-480, December.
    6. Jost Reinecke & Daniel Seddig, 2011. "Growth mixture models in longitudinal research," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 415-434, December.
    7. Øystein Sørensen & Anders M. Fjell & Kristine B. Walhovd, 2023. "Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 456-486, June.
    8. Guido Alessandri & Michele Vecchione & Brent Donnellan & John Tisak, 2013. "An Application of the LC-LSTM Framework to the Self-esteem Instability Case," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 769-792, October.
    9. J. Oud, 2010. "Second-order stochastic differential equation model as an alternative for the ALT and CALT models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(2), pages 203-215, June.
    10. Kenneth A. Bollen & Patrick J. Curran, 2004. "Autoregressive Latent Trajectory (ALT) Models A Synthesis of Two Traditions," Sociological Methods & Research, , vol. 32(3), pages 336-383, February.
    11. Piotr Tarka, 2018. "An overview of structural equation modeling: its beginnings, historical development, usefulness and controversies in the social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 313-354, January.
    12. Pennoni, Fulvia & Romeo, Isabella, 2016. "Latent Markov and growth mixture models for ordinal individual responses with covariates: a comparison," MPRA Paper 72939, University Library of Munich, Germany.
    13. Sy‐Miin Chow & Guangjian Zhang, 2008. "Continuous‐time modelling of irregularly spaced panel data using a cubic spline model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 62(1), pages 131-154, February.
    14. Stephen Pudney, 2008. "The dynamics of perception: modelling subjective wellbeing in a short panel," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 21-40, January.
    15. Jeffrey R. Harring, 2009. "A Nonlinear Mixed Effects Model for Latent Variables," Journal of Educational and Behavioral Statistics, , vol. 34(3), pages 293-318, September.
    16. Peter Robinson, 2007. "On Discrete Sampling Of Time-Varyingcontinuous-Time Systems," STICERD - Econometrics Paper Series 520, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    17. Chen, Yushun & Lin, Lian-Shin, 2010. "Structural equation-based latent growth curve modeling of watershed attribute-regulated stream sensitivity to reduced acidic deposition," Ecological Modelling, Elsevier, vol. 221(17), pages 2086-2094.
    18. Sy-Miin Chow & Zhaohua Lu & Andrew Sherwood & Hongtu Zhu, 2016. "Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation–Maximization (SAEM) Algorithm," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 102-134, March.
    19. Chou, Chih-Ping & Yang, Dongyun & Pentz, Mary Ann & Hser, Yih-Ing, 2004. "Piecewise growth curve modeling approach for longitudinal prevention study," Computational Statistics & Data Analysis, Elsevier, vol. 46(2), pages 213-225, June.
    20. Marco Guerra & Francesca Bassi & José G. Dias, 2020. "A Multiple-Indicator Latent Growth Mixture Model to Track Courses with Low-Quality Teaching," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(2), pages 361-381, January.

    More about this item

    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:bla:stanee:v:62:y:2008:i:1:p:58-82. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0039-0402 .

    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.