TY - JOUR
T1 - Bayesian Analysis of Cross-Sectional Networks
T2 - A Tutorial in R and JASP
AU - Huth, Karoline B. S.
AU - de Ron, Jill
AU - Goudriaan, Anneke E.
AU - Luigjes, Judy
AU - Mohammadi, Reza
AU - van Holst, Ruth J.
AU - Wagenmakers, Eric-Jan
AU - Marsman, Maarten
N1 - Funding Information: M. Marsman was supported by the European Union (ERC, BAYESIAN P-NETS, 101040876). Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. Publisher Copyright: © The Author(s) 2023.
PY - 2023/10/1
Y1 - 2023/10/1
N2 - Network psychometrics is a new direction in psychological research that conceptualizes psychological constructs as systems of interacting variables. In network analysis, variables are represented as nodes, and their interactions yield (partial) associations. Current estimation methods mostly use a frequentist approach, which does not allow for proper uncertainty quantification of the model and its parameters. Here, we outline a Bayesian approach to network analysis that offers three main benefits. In particular, applied researchers can use Bayesian methods to (1) determine structure uncertainty, (2) obtain evidence for edge inclusion and exclusion (i.e., distinguish conditional dependence or independence between variables), and (3) quantify parameter precision. In this article, we provide a conceptual introduction to Bayesian inference, describe how researchers can facilitate the three benefits for networks, and review the available R packages. In addition, we present two user-friendly software solutions: a new R package, easybgm, for fitting, extracting, and visualizing the Bayesian analysis of networks and a graphical user interface implementation in JASP. The methodology is illustrated with a worked-out example of a network of personality traits and mental health.
AB - Network psychometrics is a new direction in psychological research that conceptualizes psychological constructs as systems of interacting variables. In network analysis, variables are represented as nodes, and their interactions yield (partial) associations. Current estimation methods mostly use a frequentist approach, which does not allow for proper uncertainty quantification of the model and its parameters. Here, we outline a Bayesian approach to network analysis that offers three main benefits. In particular, applied researchers can use Bayesian methods to (1) determine structure uncertainty, (2) obtain evidence for edge inclusion and exclusion (i.e., distinguish conditional dependence or independence between variables), and (3) quantify parameter precision. In this article, we provide a conceptual introduction to Bayesian inference, describe how researchers can facilitate the three benefits for networks, and review the available R packages. In addition, we present two user-friendly software solutions: a new R package, easybgm, for fitting, extracting, and visualizing the Bayesian analysis of networks and a graphical user interface implementation in JASP. The methodology is illustrated with a worked-out example of a network of personality traits and mental health.
KW - Bayesian inference
KW - JASP
KW - R
KW - network psychometrics
KW - tutorial
UR - http://www.scopus.com/inward/record.url?scp=85175731144&partnerID=8YFLogxK
U2 - https://doi.org/10.1177/25152459231193334
DO - https://doi.org/10.1177/25152459231193334
M3 - Article
SN - 2515-2459
VL - 6
SP - 1
EP - 18
JO - Advances in Methods and Practices in Psychological Science
JF - Advances in Methods and Practices in Psychological Science
IS - 4
ER -