Along with recent shifts in the Sociology of Culture towards relational techniques is the use of the correlation network. Instead of examining the answers to survey responses themselves, these approaches look at relationships between questions and try to take meaning from structural properties of the whole. I also used some of these techniques for the GHTC 2016 conference  with Lee exploring USAID data from Guatemala. The results appear in our paper , but the true inspiration comes from the work on statistical methods for gene co-expression . One particularly exciting work in sociology  also tries to explain the structure of political beliefs using these networks.
I plan to do further work using these techniques, so I created a python library for anyone interested.
I think the methods are really cool because they can be paired with modern tools for interactive network visualization to create interactive software. Although we’re still working on the interactive part, the process can still be useful for exploring data. Have a look at the code!
 Voth-gaeddert, L., & Cornell, D. (2016). Improving Health Information Systems in Guatemala Using Weighted Correlation Network Analysis. In Global Humanitarian Technology Conference.
 Zhang, B., & Horvath, S. (2005). Statistical Applications in Genetics and Molecular Biology A General Framework for Weighted Gene Co-Expression Network Analysis A General Framework for Weighted Gene Co-Expression Network Analysis ∗. Statistical Applications in Genetics and Molecular Biology, 4(1). http://doi.org/10.2202/1544-6115.1128
 Boutyline, A., & Stephen Vaisey. (2015). Belief Network Analysis: A Relational Approach to Understanding the Structure of Attitudes. American Journal of Sociology, 53, 160. http://doi.org/10.1017/CBO9781107415324.004