Using Stata for sequence analysis

Sequence analysis (SA) is a very different way of looking at categorical longitudinal data, such as life-course or labor-market histories (or any ordered categorical data, for that matter). Instead of focusing on transition rates (for example, via hazard rate, Markov, or panel models), SA takes individual time series and compares them as a whole. It has significant advantages at a descriptive and exploratory level and may help detect patterns that conventional methods overlook. As availability of longitudinal data increases, this becomes a significant advantage. SA hinges on defining measures of similarity between sequences, typically to generate data-driven classifications, for example by cluster analysis. Most SA uses the optimal matching distance, but other measures are used. There is some controversy about the applicability of SA algorithms to social science data and about their parameterization. Comparison of different methods and parameterizations helps clarify the issues. For a long time, TDA was the only package social scientists had access to for SA, but in more recent years, both Stata and R have had relevant functionality; in Stata’s case, this is provided by the SQ and SADI packages. In this talk, I will discuss the current state of the SADI package. SADI differs from SQ that is based on a plugin; therefore, it is significantly faster: many of the distance measures are computationally intensive, and typically, _N*(_N-1)/2 comparisons will be made. It also provides additional distance measures, including dynamic hamming, time-warp edit distance, and a version of Elzinga’s number of matching subsequences measure. It includes tools for inspecting and graphing sequence data and for comparing distance measures and the resulting cluster analyses. I will also briefly discuss the advantages and disadvantages of using plugins rather than Mata and comment about cross-compiling plugins under Linux.