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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Psychometrika Jg. 85; H. 1; S. 206 - 231
1. Verfasser: Epskamp, Sacha
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.03.2020
Springer Nature B.V
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie: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