Psychometric network models from time-series and panel data

Researchers in the field of network psychometrics often focus on the estimation of Gaussian graphical models (GGMs)—an undirected network model of partial correlations—between observed variables of cross-sectional data or single-subject time-series data. This assumes that all variables are measured...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Psychometrika Ročník 85; číslo 1; s. 206 - 231
Hlavní autor:	Epskamp, Sacha
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.03.2020 Springer Nature B.V
Témata:	Assessment Behavioral Science and Psychology Computer Simulation - statistics & numerical data Cross-Sectional Studies Depression - drug therapy Depression - psychology Ecological Momentary Assessment - standards Error of Measurement Humanities Humans Interrupted Time Series Analysis - methods Law Longitudinal studies Male Metabolic Networks and Pathways Middle Aged Models, Statistical Networks Normal Distribution Psychology Psychometrics Psychometrics - methods Quantitative psychology Random variables Regression analysis Sample variance Software packages Statistical Theory and Methods Statistics for Social Sciences Structural equation modeling Testing and Evaluation Theory and Methods Time Time series dynamics Gaussian graphical model time-series data network psychometrics structural equation modeling panel data
ISSN:	0033-3123, 1860-0980, 1860-0980
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	Researchers in the field of network psychometrics often focus on the estimation of Gaussian graphical models (GGMs)—an undirected network model of partial correlations—between observed variables of cross-sectional data or single-subject time-series data. This assumes that all variables are measured without measurement error, which may be implausible. In addition, cross-sectional data cannot distinguish between within-subject and between-subject effects. This paper provides a general framework that extends GGM modeling with latent variables, including relationships over time. These relationships can be estimated from time-series data or panel data featuring at least three waves of measurement. The model takes the form of a graphical vector-autoregression model between latent variables and is termed the ts-lvgvar when estimated from time-series data and the panel-lvgvar when estimated from panel data. These methods have been implemented in the software package psychonetrics , which is exemplified in two empirical examples, one using time-series data and one using panel data, and evaluated in two large-scale simulation studies. The paper concludes with a discussion on ergodicity and generalizability. Although within-subject effects may in principle be separated from between-subject effects, the interpretation of these results rests on the intensity and the time interval of measurement and on the plausibility of the assumption of stationarity.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0033-3123 1860-0980 1860-0980
DOI:	10.1007/s11336-020-09697-3