Relevant bibliographies by topics / Cross-lagged panel models

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Cross-lagged panel models'

Author: Grafiati
Published: 4 June 2025
Last updated: 14 July 2025

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Journal articles on the topic "Cross-lagged panel models"

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Wysocki, Anna, Ian McCarthy, Riet van Bork, Angélique O. J. Cramer, and Mijke Rhemtulla. "Cross-lagged panel networks." advances.in/psychology 2, no. 1 (2025): e739621. https://doi.org/10.56296/aip00037.

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Network theory and accompanying methodology are becoming increasingly popular as an alternative to latent variable models for representing and, ultimately, understanding psychological constructs. The core feature of network models is that observed variables (e.g., symptoms of depression) directly influence one another over time (e.g., low mood --> concentration problems), resulting in an interconnected dynamical system. The dynamics of such a system might result in certain states (e.g., a depressive episode). Network modeling has been applied to cross-sectional data and intensive longitudin
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MAYER, LAWRENCE S., and STEVEN S. CARROLL. "Measures of Dependence for Cross-Lagged Panel Models." Sociological Methods & Research 17, no. 1 (1988): 93–120. http://dx.doi.org/10.1177/0049124188017001005.

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Mayer, Lawrence S. "On Cross-Lagged Panel Models with Serially Correlated Errors." Journal of Business & Economic Statistics 4, no. 3 (1986): 347. http://dx.doi.org/10.2307/1391576.

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Mayer, Lawrence S. "On Cross-Lagged Panel Models With Serially Correlated Errors." Journal of Business & Economic Statistics 4, no. 3 (1986): 347–57. http://dx.doi.org/10.1080/07350015.1986.10509531.

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Gauld, Christophe, Raoul P. P. P. Grasman, and Sébastien Bailly. "Usefulness of Cross-Lagged Panel Models for Clinical Research." CHEST 167, no. 6 (2025): 1537–40. https://doi.org/10.1016/j.chest.2024.12.013.

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Allison, Paul D., Richard Williams, and Enrique Moral-Benito. "Maximum Likelihood for Cross-lagged Panel Models with Fixed Effects." Socius: Sociological Research for a Dynamic World 3 (January 1, 2017): 237802311771057. http://dx.doi.org/10.1177/2378023117710578.

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Panel data make it possible both to control for unobserved confounders and allow for lagged, reciprocal causation. Trying to do both at the same time, however, leads to serious estimation difficulties. In the econometric literature, these problems have been solved by using lagged instrumental variables together with the generalized method of moments (GMM). Here we show that the same problems can be solved by maximum likelihood (ML) estimation implemented with standard software packages for structural equation modeling (SEM). Monte Carlo simulations show that the ML-SEM method is less biased an
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Zyphur, Michael J., Manuel C. Voelkle, Louis Tay, et al. "From Data to Causes II: Comparing Approaches to Panel Data Analysis." Organizational Research Methods 23, no. 4 (2019): 688–716. http://dx.doi.org/10.1177/1094428119847280.

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This article compares a general cross-lagged model (GCLM) to other panel data methods based on their coherence with a causal logic and pragmatic concerns regarding modeled dynamics and hypothesis testing. We examine three “static” models that do not incorporate temporal dynamics: random- and fixed-effects models that estimate contemporaneous relationships; and latent curve models. We then describe “dynamic” models that incorporate temporal dynamics in the form of lagged effects: cross-lagged models estimated in a structural equation model (SEM) or multilevel model (MLM) framework; Arellano-Bon
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Velasquez, Gertrudes, and Qian Zhang. "Cross-lagged Panel Mediation Models with Latent Constructs: Specification and Estimation." Multivariate Behavioral Research 55, no. 1 (2019): 142–43. http://dx.doi.org/10.1080/00273171.2019.1695569.

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Beck, Nathaniel, and Jonathan N. Katz. "Nuisance vs. Substance: Specifying and Estimating Time-Series-Cross-Section Models." Political Analysis 6 (1996): 1–36. http://dx.doi.org/10.1093/pan/6.1.1.

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In a previous article we showed that ordinary least squares with panel corrected standard errors is superior to the Parks generalized least squares approach to the estimation of time-series-cross-section models. In this article we compare our proposed method with another leading technique, Kmenta's “cross-sectionally heteroskedastic and timewise autocorrelated” model. This estimator uses generalized least squares to correct for both panel heteroskedasticity and temporally correlated errors. We argue that it is best to model dynamics via a lagged dependent variable rather than via serially corr
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Višić, Josipa, and Blanka Škrabić Perić. "The determinants of value of incoming cross-border mergers & acquisitions in European transition countries." Communist and Post-Communist Studies 44, no. 3 (2011): 173–82. http://dx.doi.org/10.1016/j.postcomstud.2011.07.004.

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This research aims to determine variables that affect the aggregate value of incoming cross-border M&As in European transitional countries. Dynamic panel models have been estimated using Arellano and Bond GMM estimator for period between year 1994 and 2008. The ratio of the total value of cross-border M&A to GDP of the country is the dependent variable. Independent variables include following indicators: lagged value of cross-border M&A to GDP, lagged GDP per capita, lagged GDP growth, inflation, interest rate spread, lagged private credit to GDP ratio, market capitalization to GDP
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Dissertations / Theses on the topic "Cross-lagged panel models"

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Alder, Meagan Cahoon. "Attachment and Relationship Quality: A Longitudinal Cross-Lagged Panel Model Examining the Association of Attachment Styles and Relationship Quality in Married Couples." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8795.

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This is a longitudinal cross-lagged panel model examining the bi-directional association of attachment styles and relationship quality in a community sample of 355 married couples, with at least one child between 10-14 years of age at the beginning of the study and 17-21 years of age at the end of the study. An Actor-Partner Interdependence Model (APIM), was used to test for actor and partner effects, thereby accounting for the non-independent nature of the data. Two separate APIM models were tested with Male Attachment predicting Female Relationship Quality and Female Attachment predicting Ma
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Jian-Hong, LI, and 黎建宏. "Measurement model analysis of emotional creativity and cross-lagged panel models with faith." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/qcx3u6.

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碩士<br>國立臺東大學<br>特殊教育學系碩士班<br>96<br>Abstract The study was involved four purposes. Firstly, this study was to develop emotional creativity (EC) scale. Secondly, this study was aimed to explore factor structure of EC. Thirdly, his study was aimed to explore the potential association among background variables and EC. Fourthly, this study was aimed at testing the cross-lagged panel models of the reciprocal effects of EC and faith. The data were collected through questionnaires from the sample of 200 college students. The obtained data was analyzed by confirmatory factor analysis (CFA), and struct
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Luca, Lisa De. "The Development of Non-Suicidal Self-Injury in Adolescence: The Role of Interpersonal and Intrapersonal Risk Factors." Doctoral thesis, 2022. http://hdl.handle.net/2158/1275911.

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The present dissertation aims to improve our knowledge on the longitudinal development of Non-Suicidal Self-Injury (NSSI) and the role of interpersonal and intrapersonal risk factors associated with it. Non-Suicidal Self-injury (NSSI), defined as the direct and deliberate self-inflicted damage of body tissue without suicidal intent, is a serious public health concern worldwide (Kiekens et al., 2018). Adolescents are the most at-risk group, given that the transition into adolescence may represent a critical vulnerability period for the onset of NSSI behaviors (Lloyd-Richardson, 2008). This phen
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Book chapters on the topic "Cross-lagged panel models"

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Newsom, Jason T. "Cross-Lagged Panel Models." In Longitudinal Structural Equation Modeling, 2nd ed. Routledge, 2023. http://dx.doi.org/10.4324/9781003263036-5.

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Gershoff, Elizabeth T., J. Lawrence Aber, and Margaret Clements. "Parent learning support and child reading ability: A cross-lagged panel analysis for developmental transactions." In The transactional model of development: How children and contexts shape each other. American Psychological Association, 2009. http://dx.doi.org/10.1037/11877-011.

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Shingles, Richard D., and H. M. Blalock Jr. "Causal Inference in Cross-Lagged Panel Analysis* *." In Causal Models in Experimental Designs. Routledge, 2017. http://dx.doi.org/10.4324/9781315081670-15.

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Allison, Paul D., Richard Williams, and Enrique Moral-Benito. "Maximum Likelihood for Cross-Lagged Panel Models With Fixed Effects." In Panel Data Econometrics. Elsevier, 2019. http://dx.doi.org/10.1016/b978-0-12-815859-3.00017-2.

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Leonhardt, Nathan D., Jeremy B. Yorgason, and Brian J. Willoughby. "Navigating the Maze." In Flourishing as a Scholar. Oxford University Press, 2025. https://doi.org/10.1093/oso/9780197677797.003.0014.

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Abstract With shifting identities, priorities, and relationships, emerging adulthood is a hallmark period for potential change. Ample statistical tools are available to better understand change over this time period, but the many options available can make it challenging for new scholars to know which tools could be matched to which research questions. This chapter provides an overview of some of the main model choices (i.e., cross-lagged panel models, random-intercept cross-lagged panel models, latent growth curve, and latent change score) and references the extended family of these models when selecting the best choice for modeling change in emerging adulthood. With the tools from in-depth examples provided in this chapter, new scholars should be better equipped to recognize, select, and estimate a variety of different models assessing change in emerging adulthood.
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Schlueter, Elmar, Eldad Davidov, and Peter Schmidt. "Applying Autoregressive Cross-Lagged and Latent Growth Curve Models to a Three-Wave Panel Study." In Longitudinal Models in the Behavioral and Related Sciences. Routledge, 2017. http://dx.doi.org/10.4324/9781315091655-13.

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Conference papers on the topic "Cross-lagged panel models"

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Aston, Elizabeth, Lidia Meshesha, Angela Stevens, Brian Borsari, and Jane Metrik. "Cannabis Demand and Use among Veterans: A Prospective Examination." In 2021 Virtual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2022. http://dx.doi.org/10.26828/cannabis.2022.01.000.20.

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Background: Cannabis demand (i.e., relative value), assessed cross-sectionally via a hypothetical marijuana purchase task (MPT), has been associated with cannabis use, problems, and dependence symptoms, among others. However, neither the prospective stability of the MPT, nor the cyclical relationship between demand and use over time has been investigated. Moreover, cannabis demand among cannabis using veterans has yet to be examined. Method: Two waves of data from a veteran sample (N=133) reporting current (past 6-month) cannabis use were analyzed to assess stability and change in cannabis dem
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Wijnia, Lisette. "The Relationships Between Motivation and Achievement in Problem-Based Learning: A Cross-Lagged Panel Model." In 2021 AERA Annual Meeting. AERA, 2021. http://dx.doi.org/10.3102/1690302.

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Yang, Yang, and WenBin Gao. "A Cross-lagged Panel Model on Smartphone Addiction of Middle School Students Based on Mplus8.0 Software." In 2022 3rd International Conference on Education, Knowledge and Information Management (ICEKIM). IEEE, 2022. http://dx.doi.org/10.1109/icekim55072.2022.00048.

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Zhu, Jinjie. "Examining the Relationship Between Teacher Collaboration and Teacher Autonomy: Evidence From Multilevel Cross-Lagged Panel Model." In 2023 AERA Annual Meeting. AERA, 2023. http://dx.doi.org/10.3102/2015869.

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Reports on the topic "Cross-lagged panel models"

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Zhang, Zhen. Longitudinal SEM in Mplus: Latent Growth and Cross-Lagged Models. Instats Inc., 2022. http://dx.doi.org/10.61700/shmr7uf60jtgi469.

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This seminar introduces longitudinal panel data models in Mplus using SEM, including latent growth models (i.e., latent curve or latent trajectory models) and cross-lagged panel models (i.e., panel vector autoregression) with random and fixed effects, including the random intercept cross-lagged panel model (RI-CLPM) to assess time-varying and stable relationships. Short-run and long-run effects will be covered and methods for assessing them provided. An official Instats certificate of completion is provided at the conclusion of the seminar.
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Zyphur, Michael. Longitudinal SEM in Mplus: Latent Growth and Cross-Lagged Panel Models. Instats Inc., 2022. http://dx.doi.org/10.61700/zvz8cn20pod2l469.

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This seminar introduces longitudinal panel data models in Mplus using SEM, including latent growth models (i.e., latent curve or latent trajectory models) and cross-lagged panel models (i.e., panel vector autoregression) with random and fixed effects, including the random intercept cross-lagged panel model (RI-CLPM) to assess time-varying and stable relationships. Short-run and long-run effects will be covered and methods for assessing them provided. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS
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Zhang, Zhen. Longitudinal SEM in Mplus: Latent Growth and Cross-Lagged Panel Models. Instats Inc., 2022. http://dx.doi.org/10.61700/k7ip0jnkhqk0z469.

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This seminar introduces longitudinal panel data models in Mplus using SEM, including latent growth models (i.e., latent curve or latent trajectory models) and cross-lagged panel models (i.e., panel vector autoregression) with random and fixed effects, including the random intercept cross-lagged panel model (RI-CLPM) to assess time-varying and stable relationships. Short-run and long-run effects will be covered and methods for assessing them provided. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS
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Zyphur, Michael. Longitudinal SEM in R: Latent Growth and Cross-Lagged Panel Models. Instats Inc., 2022. http://dx.doi.org/10.61700/0cgexcmkbt2w4469.

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This seminar introduces longitudinal panel data models in Lavaan using SEM, including latent growth models (i.e., latent curve or latent trajectory models) and cross-lagged panel models (i.e., panel vector autoregression) with random and fixed effects, including the random intercept cross-lagged panel model (RI-CLPM) to assess time-varying and stable relationships. Short-run and long-run effects will be covered and methods for assessing them provided. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS
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Zhang, Zhen. Longitudinal SEM in Mplus (Free with Course Purchase). Instats Inc., 2023. http://dx.doi.org/10.61700/qbnb0rzkq0afl469.

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This seminar introduces longitudinal panel data models in Mplus using SEM, including latent growth models (i.e., latent curve or latent trajectory models) and cross-lagged panel models (i.e., panel vector autoregression) with random and fixed effects, including the random intercept cross-lagged panel model (RI-CLPM) to assess time-varying and stable relationships. Short-run and long-run effects will be covered and methods for assessing them provided. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS
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Comparing findings from the random-intercept cross-lagged panel model and the monozygotic twin difference cross-lagged panel model: Maladaptive parenting and offspring emotional and behavioural problems. ACAMH, 2024. http://dx.doi.org/10.13056/acamh.26056.

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Open Access paper from JCPP Advances - 'We examine associations between maladaptive parenting and child emotional and behavioural problems in identical twins aged 9, 12 and 16.' Marie-Louise J. Kullberg (pic) et al.
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