I am a post-doctoral fellow at the Center for Philosophy of Science, Pittsburgh. The following provides a brief description of projects I work on.
Psychological attributes are traditionally studied in a latent variable framework, in which the latent attribute is measured by a set of observed variables by which we assume that the measured attribute causes the shared variance between these observed variables. In contrast, in a network perspective the correlations between observed variables are understood as resulting from direct causal relations between these observed variables. In this framework, the attribute is conceptualized as a cluster mutually reinforcing variables in a network. Clearly, these two frameworks propose diﬀerent views on how to understand psychological attributes, and the relations between the variables we observe. In my work, I compare network models and latent variable models both empirically and theoretically.
My dissertation can be found here.
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’?
In my work as a post-doctoral fellow, I investigate ways of quantifying model simplicity that account for the causal mechanisms expressed in psychometric models. Such forms of model simplicity enable the comparison of models that are statistically similar but causally different.
Here you can find my CV.