• Tuesday, June 12th 2018 at 14:30 - 16:30 UTC (Other timezones)
  • General participation info   |   Participate online   |   + Phone in United States (Toll Free): 1 877 309 2073 United States: +1 (571) 317-3129 Australia (Toll Free): 1 800 193 385 Australia: +61 2 8355 1020 Austria (Toll Free): 0 800 202148 Belgium (Toll Free): 0 800 78884 Canada (Toll Free): 1 888 455 1389 Denmark (Toll Free): 8090 1924 France (Toll Free): 0 805 541 047 Germany (Toll Free): 0 800 184 4222 Greece (Toll Free): 00 800 4414 3838 Hungary (Toll Free): (06) 80 986 255 Iceland (Toll Free): 800 9869 Ireland (Toll Free): 1 800 946 538 Israel (Toll Free): 1 809 454 830 Italy (Toll Free): 800 793887 Japan (Toll Free): 0 120 663 800 Luxembourg (Toll Free): 800 22104 Netherlands (Toll Free): 0 800 020 0182 New Zealand (Toll Free): 0 800 47 0011 Norway (Toll Free): 800 69 046 Poland (Toll Free): 00 800 1213979 Portugal (Toll Free): 800 819 575 Spain (Toll Free): 800 900 582 Sweden (Toll Free): 0 200 330 905 Switzerland (Toll Free): 0 800 740 393 United Kingdom (Toll Free): 0 800 169 0432 Access Code: 731-636-357

Analysis of mental health data is usually based on sum-scores of symptoms or the estimation of factor models. Both types of analyses disregard direct associations among symptoms that are well-understood in clinical practice: mental disorders can be conceptualized as vicious circles of problems that are hard to escape. A novel research framework, the network perspective on psychopathology, understands mental disorders as complex networks of interacting symptoms. Despite its comparably recent debut, with conceptual foundations in 2008 and empirical foundations in 2010, the framework has received considerable attention and recognition in recent years.

In this webinar, we will use R to learn about (1) network estimation, (2) network inference, and (3) network stability in cross-sectional data. Regarding network estimation, the state-of-the-art network model for cross-sectional data is the pairwise Markov Random Field or regularized partial correlation network that estimates the conditional dependence relations among items. We will learn to estimate appropriate network models for our data: the Ising Model for binary data, and the Gaussian Graphical Model for metric data. In this first section, we will also cover regularization methods that avoid the estimation of false positive associations in networks. The second topic, network inference, covers graph theoretical measures such as centrality that allow us to interpret networks. What symptoms are most connected with other symptoms? Finally, network stability allows us to gain insight into the robustness of our networks. If time allows, we will conclude the webinar with advanced methods such as the statistical comparison of networks, and how to deal with ordinal and mixed data. Is it noteworthy that network analysis is not limited to psychopathology data, but has been employed to study other psychological constructs such as intelligence, personality traits, and attitudes.

Researchers do not need to run R during the webinar; I will show the estimation routines in R on my screen, and make the data and syntax I work with freely available so that participants can reproduce the analysis after the workshop, and use the code to work on their own data.

 

Eiko Fried, PhD
Postdoctoral research fellow
Psychological Methods
University of Amsterdam

www.psych-networks.com

 

 

 

Eiko Fried – Network Analysis Tutorial