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The 2-day network workshop starts with a conceptual introduction on why items in psychological data tend to co-occur, and what this implies about the constructs we work with. This is followed by an introduction to social and psychological network models; an overview of the network literature in psychopathology (the field where network psychometric models have been used most over the last years); and a summary of important topics (centrality, comorbidity, early warning signals). The first group of statistical models we learn are network models in cross-sectional data. We will use the free statistical environment R to learn the basics about (1) network estimation, (2) network inference, and (3) network accuracy. We will finish this section with some advanced topics and methods, such as network comparisons, modeling of different types of variables, and considerations about causality. The statistical focus of day 2 is on dynamic time-series models: how do variables impact on each other over time? After an introduction into the general modeling framework, we learn to estimate network models for n=1 and n larger than 1 time-series data, followed by a discussion of some common problems and advanced techniques. We round up the workshop with a practical session where workshop participants learn to apply the knowledge to several datasets.
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