ArticlePsychological methods2020
Latent variable GIMME using model implied instrumental variables (MIIVs).
Article in Psychological methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Appropriate modeling of endogeneity in cross-lagged models: Efficacy of auxiliary and model-implied instrumental variables.Behavior research methods · 2025Article
- Like No Other? A Family-Specific Network Approach to Parenting Adolescents.Journal of youth and adolescence · 2024Article
- Blind Subgrouping of Task-based fMRI.Psychometrika · 2023Article
- Estimating both directed and undirected contemporaneous relations in time series data using hybrid-group iterative multiple model estimation.Psychological methods · 2023Article
- Functional contextual implementation of an evolutionary, entropy-based, and embodied free energy framework: Utilizing Lagrangian mechanics and evolutionary game theory's truth vs. fitness test of the veridicality of phenomenological experience.Frontiers in psychology · 2023Article
- Unsupervised classification reveals consistency and degeneracy in neural network patterns of emotion.Social cognitive and affective neuroscience · 2022Article
- An introduction to model implied instrumental variables using two stage least squares (MIIV-2SLS) in structural equation models (SEMs).Psychological methods · 2022Review
- Heterogeneity in PFC-amygdala connectivity in middle childhood, and concurrent interrelations with inhibitory control and anxiety symptoms.Neuropsychologia · 2022Article
- Fifty years of structural equation modeling: A history of generalization, unification, and diffusion.Social science research · 2022Article
- Integrating a functional view on suicide risk into idiographic statistical models.Behaviour research and therapy · 2022Article
- Investigation of measurement invariance in longitudinal health-related quality of life in preemptive or previously dialyzed kidney transplant recipients.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2022Article
- Estimating and Testing Random Intercept Multilevel Structural Equation Models with Model Implied Instrumental Variables.Structural equation modeling : a multidisciplinary journal · 2022Article
- Article
- Individualized learning potential in stressful times: How to leverage intensive longitudinal data to inform online learning.Computers in human behavior · 2021Article
- Specifying exogeneity and bilinear effects in data-driven model searches.Behavior research methods · 2021Article
- How are you doing? The person-specificity of daily links between neuroticism and physical health.Journal of psychosomatic research · 2020Article
- "How Well Does Your Structural Equation Model Fit Your Data?": Is Marcoulides and Yuan's Equivalence Test the Answer?CBE life sciences education · 2020Article
- Personalized models of psychopathology as contextualized dynamic processes: An example from individuals with borderline personality disorder.Journal of consulting and clinical psychology · 2020Article
- BRAIN Initiative: Cutting-Edge Tools and Resources for the Community.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Review
- Clustering Individuals Based on Similarity in Idiographic Factor Loading Patterns.Multivariate behavioral researchArticle
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3 authors.
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Abstract
Researchers across many domains of psychology increasingly wish to arrive at personalized and generalizable dynamic models of individuals' processes. This is seen in psychophysiological, behavioral, and emotional research paradigms, across a range of data types. Errors of measurement are inherent in most data. For this reason, researchers typically gather multiple indicators of the same latent construct and use methods, such as factor analysis, to arrive at scores from these indices. In addition to accurately measuring individuals, researchers also need to find the model that best describes the relations among the latent constructs. Most currently available data-driven searches do not include latent variables. We present an approach that builds from the strong foundations of group iterative multiple model estimation (GIMME), the idiographic filter, and model implied instrumental variables with two-stage least squares estimation (MIIV-2SLS) to provide researchers with the option to include latent variables in their data-driven model searches. The resulting approach is called latent variable GIMME (LV-GIMME). GIMME is utilized for the data-driven search for relations that exist among latent variables. Unlike other approaches such as the idiographic filter, LV-GIMME does not require that the latent variable model to be constant across individuals. This requirement is loosened by utilizing MIIV-2SLS for estimation. Simulated data studies demonstrate that the method can reliably detect relations among latent constructs, and that latent constructs provide more power to detect effects than using observed variables directly. We use empirical data examples drawn from functional MRI and daily self-report data. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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