Estimating psychopathological networks: Be careful what you wish for
| Autor: | Maarten Marsman, Joost Kruis, Sacha Epskamp |
|---|---|
| Přispěvatelé: | Psychologische Methodenleer (Psychologie, FMG), FMG |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
050103 clinical psychology Theoretical computer science Computer science Emotions Normal Distribution Datasets as Topic Social Sciences lcsh:Medicine Anxiety Material Fatigue 0302 clinical medicine Materials Physics Medicine and Health Sciences Psychology lcsh:Science Network model Multidisciplinary Psychopathology Artificial neural network Approximation Methods Depression Simulation and Modeling Physics 05 social sciences Classical Mechanics Neurology Research Design Physical Sciences Neurons and Cognition (q-bio.NC) medicine.symptom Network Analysis Research Article Network analysis Computer and Information Sciences Insomnia Materials Science Research and Analysis Methods Methodology (stat.ME) Normal distribution 03 medical and health sciences Mental Health and Psychiatry medicine Humans 0501 psychology and cognitive sciences Statistics - Methodology Structure (mathematical logic) Damage Mechanics Mood Disorders Psychological research lcsh:R Biology and Life Sciences Probability Theory Probability Distribution Dyssomnias Logistic Models Sample size determination FOS: Biological sciences Quantitative Biology - Neurons and Cognition Multivariate Analysis lcsh:Q Neural Networks Computer Sleep Disorders Mathematics 030217 neurology & neurosurgery |
| Zdroj: | PLoS ONE, Vol 12, Iss 6, p e0179891 (2017) PLoS ONE, 12(6):e0179891. Public Library of Science PLoS ONE |
| ISSN: | 1932-6203 |
| Popis: | Network models, in which psychopathological disorders are conceptualized as a complex interplay of psychological and biological components, have become increasingly popular in the recent psychopathological literature. These network models often contain significant numbers of unknown parameters, yet the sample sizes available in psychological research are limited. As such, general assumptions about the true network are introduced to reduce the number of free parameters. Incorporating these assumptions, however, means that the resulting network will lead to reflect the particular structure assumed by the estimation method---a crucial and often ignored aspect of psychopathological networks. For example, observing a sparse structure and simultaneously assuming a sparse structure does not imply that the true model is, in fact, sparse. To illustrate this point, we discuss recent literature and show the effect of the assumption of sparsity in three simulation studies. Published in PlosOne |
| Databáze: | OpenAIRE |
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