State of the aRt personality research: A tutorial on network analysis of personality data in R

By G. Costantini, Sacha Epskamp, Denny Borsboom, Marco Perugini, R. Mõttus, Lourens J. Waldorp, A. Cramer. 2015

890 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a67099f4602dd798cdb6a1c3872d01e57497b5e9

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In Experimental and Cognitive Psychology

In Statistical and Nonlinear Physics

Citations in the corpus

Generalized Network Psychometrics: Combining Network and Latent Variable Models (2016, neutral): “Network modeling highlights variance that is unique to pairs of variables, whereas latent variable modeling focuses on variance that is shared across all variables (Costantini et al., 2015).”
Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research (2016, background): “Similar arguments have been made for stable personality traits, which routinely come up as clusters in an estimated network structure [45, 47, 48].”
Descriptive, Predictive and Explanatory Personality Research: Different Goals, Different Approaches, but a Shared Need to Move beyond the Big Few Traits (2020, background): “Improved computational power and accessible data analytic tools have eased working with many-dimensional data to efficiently summarize, communicate and compare association patterns (Costantini et al., 2015; Revelle, 2020; Ellemann et al., 2020; Stachl et al., 2020).”
Mental Disorders as Causal Systems (2015, background): “, insomnia → fatigue → feelings of worthlessness → insomnia), causal relations in psychopathology networks should not necessarily be viewed as directional; most are typically modeled as undirected networks (Constantini et al., 2014).”
Integrating Personality Structure, Personality Process, and Personality Development (2017, background): “An important and often undesirable consequence of such aggregations is that the aggregate can no longer be traced to the processes that generated its elements (Asendorpf, 2016; Costantini et al., 2015).”
Network analysis: a brief overview and tutorial (2018, methodology): “When continuous data are multivariate normally distributed, analysing the partial correlations using the Gaussian graphical model (GGM; Costantini et al., 2015; Lauritzen, 1996) is appropriate.”
How well do network models predict observations? On the importance of predictability in network models (2016, background): “16, 15, 2, 28, 4] but network models are also applied in other subfields such as health psychology [26] and personality psychology [9, 8].”
Extending psychometric network analysis: Empirical evidence against g in favor of mutualism? (2019, methodology): “Although it was psychopathology that has motivated this advancement (Borsboom, 2008; Borsboom & Cramer, 2013), this type of analysis is gaining popularity in other fields as well, including personality (Costantini et al., 2015), attitudes (Dalege, Borsboom, Harreveld, & van der Maas, 2017), interest”
A Tutorial on Regularized Partial Correlation Networks (2016, methodology): “4Unregularized partial correlations can also be seen to already reduce spurious edges in a network comprised of marginal correlation coefficients (Costantini et al., 2015a).”
Regularized Gaussian Psychological Networks: Brief Report on the Performance of Extended BIC Model Selection (2016, methodology): “To control for spurious relationships a regularization technique called the ‘least absolute shrinkage and selection operator’ (LASSO; Tibshirani, 1996) is often used (Costantini et al., 2015).”
Estimating psychological networks and their accuracy: A tutorial paper (2016, methodology): “When the data follow a multivariate normal density, the appropriate PRMF model is called the Gaussian graphical model (GGM; Costantini et al. 2015a; Lauritzen 1996), in which edges can directly be interpreted as partial correlation coeﬃcients .”
What do centrality measures measure in psychological networks? (2019, background): “popular models to estimate a psychological network are partial correlations for cross-sectional data (i.e., cross-sectional network; Costantini et al., 2015; Epskamp, Waldorp, Mõttus, & Borsboom, 2018; van Borkulo et al., 2014) and vector autoregressive based models for intensive longitudinal data (”
A Network of Dark Personality Traits: What Lies at the Heart of Darkness? (2018, background): “Because network analysis visually depicts the associations among traits and provides measures of centrality, it may provide unique insight into the core of the Dark Triad (see Costantini et al., 2015, for a review). https://doi.org/10.1016/j.jrp.2017.11.003 0092-6566/ 2017 Elsevier Inc.”
Development of Indirect Measures of Conscientiousness: Combining a Facets Approach and Network Analysis (2015, methodology): “…relationships (Epskamp et al., 2012), which can be more easily understood even when the pattern of relationships is not a simple one; and (3) a representation that helps the researcher to focus on the differential relationships among variables (e.g. Costantini et al., 2015; Cramer et al., 2012).”
Moving Forward: Challenges and Directions for Psychopathological Network Theory and Methodology (2017, background): “And because such insights are critically dependent on nontechnical and accessible explanations that are presently scarce (Costantini et al., 2014; Epskamp et al., 2017; Epskamp & Fried, 2016), we call for tutorial papers on network estimation and interpretation in the realm of psychopathology.”
The Gaussian Graphical Model in Cross-Sectional and Time-Series Data (2016, methodology): “In recent literature, it has become increasingly popular to use regularization techniques, such as penalized MLE, to jointly estimate model structure and parameter values (Costantini et al., 2015; van Borkulo et al., 2014).”