As a department engaged in innovative designs, measurement, and data analysis, we use a wide range of techniques to address both straightforward and complex research questions.  Whether the focus is on enumerating hard to reach populations, demonstrating causal connections, or unfolding complex patterns of interrelated social processes, the fundamental goal is to build on the strengths of data collection strategies, measurement theory, statistical theory, and analytic techniques to answer these questions.  Listed below are some of the ways we’ve been approaching our research:

Data Collection 

  • Natural experiment research design;
  • Fixed effects models to address unobserved heterogeneity;
  • Retrospective life history calendars to track changes in life events;
  • Daily time use diaries;
  • Identifying genetic ‘matches;’
  • Egocentric, family-based, group-focused, and global network data;
  • Biomarker and physiological data
  • Survey data
  • Geospatial and individual-GPS data;
  • Neural imaging;
  • Natural experiments;
  • Focus groups;
  • In-depth interviews;
  • Web scraping;  
  • Historical and Cultural contextualization;



  • Operationalizing ‘neighborhoods;’
  • Leveraging ‘big data’ to assess aspects of community social organization;
  • Using GPS, survey, and ethnographic data to measure ‘activity spaces;’
  • Reconfiguring census and GIS to assess segregation;
  • Assessing ethnoracial diversity and spatial diffusion;
  • Environmental exposures;
  • Diets;



  • Pairing survey data with in-depth interviews to develop new theory;
  • Simulations;
  • Latent growth curve and trajectory models;
  • Propensity scores;
  • Modeling ethnoracial diversity and spatial diffusion processes;
  • Imputing unobserved statuses, such as immigrants’ legal status;
  • Analyzing both sending and receiving contexts for migrants;
  • Structural equation models;
  • Network analysis;
  • Longitudinal models;