psychometrics
Definition
Psychometrics refers to the quantitative measurement of psychological constructs, with network psychometrics representing a contemporary extension that models variables as nodes and their statistical relationships as edges in a graph. Within this framework, Gaussian Graphical Models estimate regularized partial correlations among observed variables, with applications spanning mental health, intelligence, and personality research. Bayesian approaches to these models offer alternatives to Maximum Likelihood estimation by placing prior distributions over parameters, allowing regularization through shrinkage toward zero and probabilistic assessment of edge inclusion. A generalized formulation of Bayesian Gaussian Graphical Models, built on a transformation of the Cholesky decomposition matrix, further extends the approach to binary, ordinal, and continuous data types within a single unified estimation framework.
Sources: Franco et al. (2024)
Related Terms
- Bayesian analysis (1 shared article)
- regularization (1 shared article)
- probabilistic graphical modeling (1 shared article)
Applications
Psychometrics and Bayesian Gaussian Graphical Models
Bayesian Gaussian Graphical Models have gained prominence specifically within network psychometrics as tools for estimating partial correlations between psychological variables while accounting for uncertainty through prior distributions. Existing implementations, however, have been constrained by dependence on particular sampling schemes such as Gibbs sampling, limiting their adaptability to alternative estimation methods including variational Bayes or Laplace Approximation. A generalized approach using the Cholesky decomposition of the correlation matrix addresses these constraints by permitting any zero-centered symmetric distribution as a prior and supporting both MCMC and deterministic optimization.
Sources: Franco et al. (2024)
Psychometrics and Regularization
Regularization methods are applied in network psychometrics to distinguish genuine partial correlations from spurious ones by shrinking small coefficients toward zero. Bayesian regularization via an inverse Wishart prior on the covariance matrix offers an alternative to frequentist approaches, with the degree of regularization controlled by the degrees-of-freedom hyperparameter.
Sources: Franco et al. (2024)
