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

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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 longitudinal designs (e.g., data collected using an Experience Sampling Method). In this paper, we present a cross-lagged panel network model to reveal item-level longitudinal effects that occur within and across constructs that are measured at a small set of measurement occasions. The proposed model uses a combination of regularized regression estimation and structural equation modeling to estimate auto-regressive and cross-lagged pathways that characterize the effects of observed components of psychological constructs on each other over time. We demonstrate the application of this model to longitudinal data on students' commitment to school and self-esteem.
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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 and more efficient than the GMM method under a wide range of conditions. ML-SEM also makes it possible to test and relax many of the constraints that are typically embodied in dynamic panel models.
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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-Bond dynamic panel data methods; and autoregressive latent trajectory models. We describe the implications of overlooking temporal dynamics in static models and show how even popular cross-lagged models fail to control for stable factors over time. We also show that Arellano-Bond and autoregressive latent trajectory models have various shortcomings. By contrasting these approaches, we clarify the benefits and drawbacks of common methods for modeling panel data, including the GCLM approach we propose. We conclude with a discussion of issues regarding causal inference, including difficulties in separating different types of time-invariant and time-varying effects over time.
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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 correlated errors. The lagged dependent variable approach makes it easier for researchers to examine dynamics and allows for natural generalizations in a manner that the serially correlated errors approach does not. We also show that the generalized least squares correction for panel heteroskedasticity is, in general, no improvement over ordinary least squares and is, in the presence of parameter heterogeneity, inferior to it. In the conclusion we present a unified method for analyzing time-series-cross-section data.
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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 ratio, lagged rule of law and lagged control of corruption.
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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 Male Relationship Quality. Results indicate that own attachment was a stronger predictor of partner relationship quality over time than was own relationship quality to partner attachment; although male relationship quality did predict female attachment from T1 to T3, it was not significant at all other time points. Clinical implications and future research are discussed.
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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 structural equation modeling (SEM). The result of CFA showed EC measurement model included emotional preparation, novelty, effectiveness and authenticity. The result of SEM showed there is no significant associative between background variables and EC. Cross-lagged panel models testing showed EC was positive associative with faith. According to those results, implication for future research was discussed. Key words: cross-lagged panel models, emotional creativity, faith, structural equation modeling.
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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 phenomenon requires attention not only because of its heavy impact in terms of public health and the high incidence within the population, but also for the consequences that engagement in NSSI entails. The long-term effects of self-injurious behavior can be destructive, with consequences for emotional and cognitive development (Baetens et al., 2011). NSSI is used as a maladaptive means of coping with intense emotions. Both interpersonal (e.g., social interaction with peers and family; Brausch & Gutierrez, 2010) and intrapersonal factors (e.g., emotion regulation, self-efficacy, and self-esteem; Baetens et al., 2011) can serve to initiate and maintain NSSI (Nock, 2009; Nock & Prinstein, 2004; Zetterqvist et al., 2013). In the last ten years, the attention given to this issue has become increasingly important. Most of the existing literature has examined this behavior (e.g., prevalence, risks factors) at the cross-sectional level, while few studies have explored the longitudinal development of NSSI, and the role played by interpersonal and intrapersonal factors at the longitudinal level. For these reasons, the general aim of the present dissertation is to analyze the longitudinal development of NSSI and the association with interpersonal and intrapersonal risk factors. Three empirical studies are presented. They cover three main issues: 1) a meta-analysis on the longitudinal development of NSSI; 2) the reciprocal associations between peer problems and NSSI; 3) the mediational role of Covid-19 related stress in the association between pre-existing vulnerabilities and NSSI. In the first study (Chapter 1), we presented a meta-analysis on the development of NSSI from childhood to young adulthood, using a Bayesian approach. The aim was to examine both the occurrence and the frequency of NSSI over time, considering all studies published up until November 2020. Subsequently, we examined the role of possible moderators, such as gender, mean age during the first wave of data collection, and number of months covered by the assessment. The results show the important role of gender (i.e., females) and age in the explanation of the expected proportion and mean changes of NSSI over time. Specifically, what emerges from the findings is how being female represents an important risk factor for the occurrence of this behavior. As for the frequency of this behavior, a higher percentage of females are associated with higher severity of NSSI, but it tends to decrease over time. The results show that mid-adolescence (i.e., 14/15 years) appears to be the period of highest risk for the occurrence of NSSI over time. Instead, over time, findings suggest that the frequency of this behavior is higher in adolescence, at a mean age of 15-16 years of age, and it decreases in late adolescence (e.g., Plener et al., 2015). In the second study (Chapter 2), we investigated the reciprocal associations between peer problems (e.g., peer victimization, friendship stress, and loneliness) and NSSI throughout adolescence, distinguishing between- and within-person effects. Participants were 866 adolescents (54.5% females; Mage = 13.12 years, SD = 0.78), who took part in six waves of data collection. Random Intercept Cross-Lagged Panel Models (RI-CLPM) were used to estimate within-person cross-lagged effects between each peer problem and NSSI from Grade 7 to 12. After accounting for between-person associations between peer problems and NSSI, results indicated that higher-than-usual levels of NSSI predicted higher-than-usual levels of adolescents’ own friendship stress, loneliness, and peer victimization at the subsequent time point. Yet, sensitivity analyses revealed that most of these effects were strongly attenuated and explained by within-person fluctuations in depressive symptoms. No within-person cross-lagged effects from peer problems to NSSI were found. In the third study (Chapter 3), we examined the role of Covid-19 related stress in the association between pre-existing vulnerabilities and the engagement in NSSI during the pandemic. Specifically, the study aimed to examine if adolescents with pre-existing vulnerabilities, including a prior history of NSSI, higher levels of internalizing symptoms, and poorer regulatory emotional self-efficacy, were more likely to show increases in NSSI across the pandemic period through higher levels of Covid-19 related stress. The analysis was conducted on 1061 adolescents (52.4% females; Mage = 15.49 years, SD = 0.76), enrolled in the 9th and 10th grade in Tuscany, Italy, who took part in two waves of data collection. Results showed that adolescents with pre-existing vulnerabilities were at higher risk of engaging in NSSI through the role of Covid-19 related stress. Specifically, adolescents with a prior history of NSSI, higher levels of anxious and depressive symptoms, and poorer regulatory emotional self-efficacy showed a higher level of Covid-19 related stress, which in turn it was associated with an increased risk of occurrence of NSSI. In the final chapter (Chapter 4), the results of the previous three studies have been discussed highlighting their contribution to the literature on the longitudinal development of NSSI, strengths and limitations, and the implications for future studies.
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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 demand over six months. Autoregressive cross-lagged panel models assessed the longitudinal associations between demand indices (i.e., intensity, Omax, Pmax, breakpoint) and cannabis use. Results: Models revealed unique directions of effects for each demand index. Baseline cannabis use predicted greater intensity (ß = .32, p&lt;.001), Omax (ß = .37, p&lt;.001), breakpoint (ß = .28, p&lt;.001), and Pmax (ß = .21, p = .017) at 6-months. Conversely, baseline intensity (ß = .14, p=.028), breakpoint (ß = .12, p=.038), and Pmax (ß = .12, p = .043), but not Omax, predicted greater use at 6-months. Discussion: Cannabis demand indices demonstrated prospective stability over six months and varied along with natural changes in cannabis use. Importantly, intensity, Pmax, and breakpoint displayed bidirectional predictive associations with cannabis use, and across indices, the prospective pathway from use to demand was consistently stronger. Findings highlight the value of assessing cannabis demand longitudinally, particularly among clinical samples, to determine how demand fluctuates in response to experimental manipulation, intervention, and treatment.
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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 Equivalent point.
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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 Equivalent point.
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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 Equivalent point.
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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 Equivalent point.
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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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