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), it takes individual time series and compares them as wholes. It has significant advantages at a descriptive and exploratory level and can help detect patterns that conventional methods will 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 in use. 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 recent years, both Stata and R have had relevant functionality, in Stata's case provided by the sq and sadi packages. In this talk, I will discuss the current state of the sadi package. sadi differs from sq in being 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 for N observations. sadi 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 make some remarks about cross-compiling plugins under Linux.