Golino, Hudson Christensen, Alexander P Moulder, Robert Kim, Seohyun Boker, Steven M
Published in
Psychometrika
The past few years were marked by increased online offensive strategies perpetrated by state and non-state actors to promote their political agenda, sow discord, and question the legitimacy of democratic institutions in the US and Western Europe. In 2016, the US congress identified a list of Russian state-sponsored Twitter accounts that were used t...
Epskamp, Sacha Isvoranu, Adela-Maria Cheung, Mike W-L
Published in
Psychometrika
A growing number of publications focus on estimating Gaussian graphical models (GGM, networks of partial correlation coefficients). At the same time, generalizibility and replicability of these highly parameterized models are debated, and sample sizes typically found in datasets may not be sufficient for estimating the underlying network structure....
Morales, Domingo Krause, Joscha Burgard, Jan Pablo
Published in
Psychometrika
Major depression is a severe mental disorder that is associated with strongly increased mortality. The quantification of its prevalence on regional levels represents an important indicator for public health reporting. In addition to that, it marks a crucial basis for further explorative studies regarding environmental determinants of the condition....
Henry, Teague R Robinaugh, Donald J Fried, Eiko I
Published in
Psychometrika
The combination of network theory and network psychometric methods has opened up a variety of new ways to conceptualize and study psychological disorders. The idea of psychological disorders as dynamic systems has sparked interest in developing interventions based on results of network analytic tools. However, simply estimating a network model is n...
Cavicchia, Carlo Vichi, Maurizio
Published in
Psychometrika
Hierarchical models are often considered to measure latent concepts defining nested sets of manifest variables. Therefore, by supposing a hierarchical relationship among manifest variables, the general latent concept can be represented by a tree structure where each internal node represents a specific order of abstraction for the latent concept mea...
Ryan, Oisín Hamaker, Ellen L
Published in
Psychometrika
Network analysis of ESM data has become popular in clinical psychology. In this approach, discrete-time (DT) vector auto-regressive (VAR) models define the network structure with centrality measures used to identify intervention targets. However, VAR models suffer from time-interval dependency. Continuous-time (CT) models have been suggested as an ...
Lee, Kevin H Chen, Qian DeSarbo, Wayne S Xue, Lingzhou
Published in
Psychometrika
Graphical models have received an increasing amount of attention in network psychometrics as a promising probabilistic approach to study the conditional relations among variables using graph theory. Despite recent advances, existing methods on graphical models usually assume a homogeneous population and focus on binary or continuous variables. Howe...
Brusco, Michael J Steinley, Douglas Watts, Ashley L
Published in
Psychometrika
Common outputs of software programs for network estimation include association matrices containing the edge weights between pairs of symptoms and a plot of the symptom network. Although such outputs are useful, it is sometimes difficult to ascertain structural relationships among symptoms from these types of output alone. We propose that matrix per...
Bergner, Yoav Halpin, Peter Vie, Jill-Jênn
Published in
Psychometrika
This paper presents a machine learning approach to multidimensional item response theory (MIRT), a class of latent factor models that can be used to model and predict student performance from observed assessment data. Inspired by collaborative filtering, we define a general class of models that includes many MIRT models. We discuss the use of penal...
Marsman, M. Huth, K. Waldorp, L. J. Ntzoufras, I.
Published in
Psychometrika
The Ising model is one of the most widely analyzed graphical models in network psychometrics. However, popular approaches to parameter estimation and structure selection for the Ising model cannot naturally express uncertainty about the estimated parameters or selected structures. To address this issue, this paper offers an objective Bayesian appro...
