scholarly journals Changes in Size and Interpretation of Parameter Estimates in Within-Person Models in the Presence of Time-Invariant and Time-Varying Covariates

2021 ◽  
Vol 12 ◽  
Author(s):  
Marcus Mund ◽  
Matthew D. Johnson ◽  
Steffen Nestler
Keyword(s):  
Parameter Estimates ◽  
Methodological Research ◽  
Panel Model ◽  
Random Intercept ◽  
Curve Model ◽  
Latent Curve ◽  
Research Questions ◽  
Time Invariant ◽  
Time Varying Covariates ◽  
Reciprocal Associations

For several decades, cross-lagged panel models (CLPM) have been the dominant statistical model in relationship research for investigating reciprocal associations between two (or more) constructs over time. However, recent methodological research has questioned the frequent usage of the CLPM because, amongst other things, the model commingles within-person associations with between-person associations, while most developmental research questions pertain to within-person processes. Furthermore, the model presumes that there are no third variables that confound the relationships between the longitudinally assessed variables. Therefore, the usage of alternative models such as the Random-Intercept Cross-Lagged Panel Model (RI-CLPM) or the Latent Curve Model with Structured Residuals (LCM-SR) has been suggested. These models separate between-person from within-person variation and they also control for time constant covariates. However, there might also be third variables that are not stable but rather change across time and that can confound the relationships between the variables studied in these models. In the present article, we explain the differences between the two types of confounders and investigate how they affect the parameter estimates of within-person models such as the RI-CLPM and the LCM-SR.

scholarly journals Accounting for Time-Varying Inter-Individual Differences of Trajectories when Assessing Cross-Lagged Models

2020 ◽  
Author(s):  
Paul Scott
Keyword(s):  
Bayes Factor ◽  
Time Varying ◽  
Fit Indices ◽  
Latent Growth ◽  
Latent Growth Curve Model ◽  
Latent Trajectory ◽  
Panel Model ◽  
Random Intercept ◽  
True Model ◽  
Curve Model

This paper explores relationships amongst cross-lagged models allowing trajectories to be freely estimated, some accounting for time-varying differences amongst individuals (Autoregressive Latent Trajectory (ALT), General Cross-lagged Model (GCLM), and Latent Growth Curve Model with Structured Residuals and Unspecified Growth Trajectory (LGCM-SR-UGT)) and some not (Cross-lagged Panel Model (CLPM), Random Intercept Cross-lagged Panel Model (RI-CLPM), and Mean Stationary GCLM). An applied example using LSAY data demonstrates these models. Simulations examine (1) fit indices assessing “good” fit and Bayes Factor for model selection; (2) consequences of ignoring variability in trajectories on cross-lagged estimates. Findings were (1) RMSEA discerned “good” fit and Bayes Factor tended to select models closely related to true model over less related models; (2) various patterns of bias in path estimates and standard errors are found, in particular, causal dominance in conjunction with time-variant between-person variance and covariance were notably influential on bias in path estimates.

A binary choice model with partial observability for panel data

2020 ◽  
Vol 15 (4) ◽  
pp. 315-322
Author(s):  
Ekaterina Batalova ◽  
Kirill Furmanov ◽  
Ekaterina Shelkova
Keyword(s):  
Job Search ◽  
Latent Class ◽  
Choice Model ◽  
Maximum Likelihood Estimates ◽  
Binary Choice ◽  
Panel Model ◽  
Binary Response Variable ◽  
Monitoring Survey ◽  
Binary Choice Model ◽  
Time Invariant

We consider a panel model with a binary response variable that is a product of two unobservable factors, each determined by a separate binary choice equation. One of these factors is assumed to be time-invariant and may be interpreted as a latent class indicator. A simulation study shows that maximum likelihood estimates from even the shortest panel are much more reliable than those obtained from a cross-section. As an illustrative example, the model is applied to Russian Longitudinal Monitoring Survey data to estimate a proportion of the non-employed population who are participating in job search.

scholarly journals Bullying Perpetration, Moral Disengagement and Need for Popularity: Examining Reciprocal Associations in Adolescence

2021 ◽  
Author(s):  
Eva M. Romera ◽  
Rosario Ortega-Ruiz ◽  
Kevin Runions ◽  
Antonio Camacho
Keyword(s):  
Moral Disengagement ◽  
Motivational Factors ◽  
Bullying Perpetration ◽  
Random Intercept ◽  
Bidirectional Association ◽  
Bullying Behavior ◽  
Longitudinal Associations ◽  
Reciprocal Associations

AbstractPrecursors and consequences of bullying have been widely explored, but much remains unclear about the association of moral and motivational factors. This study examined longitudinal associations between need for popularity, moral disengagement, and bullying perpetration. A total of 3017 participants, aged 11 to 16 years in wave 1 (49% girls; Mage = 13.15, SD = 1.09), were surveyed across four waves with six-month intervals. At the between-person level, cross-lagged modeling revealed a positive bidirectional association between moral disengagement and need for popularity; bullying perpetration was predicted by both need for popularity and moral disengagement. From the within-person level, random intercept cross-lagged analyses revealed that need for popularity predicted both moral disengagement and bullying perpetration. The results highlight the interplay between motivational and moral mechanisms that underlies bullying behavior.

scholarly journals Mediating role of depressive symptoms linking insecure attachment and disordered eating in adolescents: A multiwave longitudinal study

2020 ◽  
pp. 1-13
Author(s):  
L. Cortés-García ◽  
K. R. Viddal ◽  
L. Wichstrøm ◽  
C. Senra
Keyword(s):  
Depressive Symptoms ◽  
Disordered Eating ◽  
Subsequent Development ◽  
Eating Attitudes ◽  
Insecure Attachment ◽  
Attachment Figure ◽  
Dynamic Panel Model ◽  
Mediating Role ◽  
Panel Model ◽  
Time Invariant

Abstract Research has supported a link between insecure attachment and disordered eating in adolescents; however, how this influence is exerted remains unclear. This study explored whether depressive symptoms constitute a pathway through which insecure attachment to parents predicts subsequent development of disordered eating in the transition from childhood to adolescence. The study also examines whether there are differential effects regarding the attachment figure, child's gender, or reciprocity between variables. A community-based sample of Spanish youth (n = 904; 49.4% girls) was followed biennially from age 10 to 16 years. Attachment, depressive symptoms, and disordered eating were measured using the Inventory of Parental and Peer Attachment, Children's Depression Inventory, and Children's Eating Attitudes Test, respectively. Prospective data were analyzed using a dynamic panel model, which accounts for unmeasured time-invariant factors. Whereas insecure attachment to the father did not predict later depression or disordered eating, higher insecure attachment to the mother at ages 10 and 12 years predicted more disordered eating at ages 14 and 16 years via increased depressive symptoms at ages 12 and 14 years. No child's gender-specific or reverse mediational effects were found. This study suggests that an increase in depressive symptoms might be one mechanism by which insecure attachment exerts its influence on the development of eating disorders symptomatology in adolescence. Intervention efforts aimed at strengthening particularly the mother–child attachment relationship may reduce the vulnerability to develop depressive symptoms and disordered eating.

scholarly journals Towards early risk biomarkers: serum metabolic signature in childhood predicts cardio-metabolic risk in adulthood

2019 ◽  
Author(s):  
Xiaowei Ojanen ◽  
Runtan Cheng ◽  
Timo Törmäkangas ◽  
Na Wu ◽  
Noa Rappaport ◽  
...  
Keyword(s):  
Cardiovascular Risk ◽  
Early Adulthood ◽  
Metabolic Risk ◽  
Metabolomics Data ◽  
Metabolic Signature ◽  
Panel Model ◽  
Random Intercept ◽  
Increased Risk ◽  
Machine Learning Approach ◽  
Risk Biomarkers

AbstractCardiovascular diseases have their origin in childhood. Early biomarkers identifying individuals with increased risk for disease are needed to support early detection and to optimize prevention strategies. By applying machine learning approach on high throughput NMR-based metabolomics data, we identified metabolic predictors of cardiovascular risk in circulation in a cohort of 396 females, followed from childhood (mean age 11.2 years) to early adulthood (mean age 18.1 years). The identified childhood metabolic signature included three circulating biomarkers robustly associating with increased cardiovascular risk in early adulthood (AUC = 0.641 to 0.802, all p<0.01). These associations were confirmed in two validation cohorts including middle-aged women, with similar effect estimates. We subsequently applied random intercept cross-lagged panel model analysis, which suggested causal relationship between metabolites and cardio-metabolic risk score from childhood to early adulthood. These results provide evidence for the utility of circulating metabolomics panel to identify children and adolescents at risk for cardiovascular disease, to whom preventive measures and follow-up could be indicated.

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