I am a PhD student in the Psychological Methods group at the University of Amsterdam. In my PhD project I develop methods to distinguish network models and latent variable models both empirically and theoretically.
My interest in comparing these different modeling frameworks ties in with my broader interest in philosophy of statistics and the interpretation of statistical models. Some examples: What are the implications of interpreting the common factor in a common factor model as a summary of the data rather than as an underlying common cause? What are possible chance experiments that result in the response variables in a factor model or IRT model being ‘random variables’? What is actually ‘observed’ in ‘observed variables’?