with a partner'. When a state sequence is represented as the list of states observed at the successive time points, the position of each element in the sequence conveys this time information and the distance between positions reflects duration. An alternative more compact representation of a sequence, is the list of the successive spells stamped with their duration, where a spell (also called episode) is a substring in a same state. For example, in is a spell of length 3 in state b, and the whole sequence can be represented as (a,2)-(b,3)-(c,1). A crucial point when looking at state sequences is the timing scheme used to time align the sequences. This could be the historical calendar time, or a process time such as age, i.e. time since birth. In event sequences, positions do not convey any time information. Therefore event occurrence time must be explicitly provided (as a timestamp) when it matters. SA is essentially concerned with state sequences. Methods Conventional SA consists essentially in building a typology of the observed trajectories. Abbott and Tsay (2000) describe this typical SA as a three-step program: 1. Coding individual narratives as sequences of states; 2. Measuring pairwise dissimilarities between sequences; and 3. Clustering the sequences from the pairwise dissimilarities. However, SA is much more (see e.g.) and encompasses also among others the description and visual rendering of sets of sequences, ANOVA-like analysis and regression trees for sequences, the identification of representative sequences, the study of the relationship between linked sequences (e.g. dyadic, linked-lives, or various life dimensions such as occupation, family, health), and sequence-network. Describing and rendering state sequences Given an alignment rule, a set of sequences can be represented in tabular form with sequences in rows and columns corresponding to the positions in the sequences. Sequences of cross-sectional distributions To describe such data, we may look at the columns and consider the cross-sectional state distributions at the successive positions. The chronogram or density plot of a set of sequences renders these successive cross-sectional distributions. For each (column) distribution we can compute characteristics such as entropy or modal state and look at how these values evolve over the positions (see pp 18–21). Characteristics of individual sequences Alternatively, we can look at the rows. The index plot where each sequence is represented as a horizontal stacked bar or line is the basic plot for rendering individual sequences. We can compute characteristics of the