In social sciences, sequence analysis (SA) is concerned with the analysis of sets of categorical sequences that typically describe longitudinal data. Analyzed sequences are encoded representations of, for example, individual life trajectories such as family formation, school to work transitions, working careers, but they may also describe daily or weekly time use or represent the evolution of observed or self-reported health, of political behaviors, or the development stages of organizations. Such sequences are chronologically ordered unlike words or DNA sequences for example.

SA is a longitudinal analysis approach that is holistic in the sense that it considers each sequence as a whole. SA is essentially exploratory. Broadly, SA provides a comprehensible overall picture of sets of sequences with the objective of characterizing the structure of the set of sequences, finding the salient characteristics of groups, identifying typical paths, comparing groups, and more generally studying how the sequences are related to covariates such as sex, birth cohort, or social origin.

Introduced in the social sciences in the 1980s by Andrew Abbott, SA has gained much popularity after the release of dedicated software such as the SQ and SADI addons for Stata and the TraMineR R package with its companions TraMineRextras and WeightedCluster.

Despite some connections, the aims and methods of SA in social sciences strongly differ from those of sequence analysis in bioinformatics.

Sequence analysis methods were first imported into the social sciences from the information and biological sciences (see Sequence alignment) by the University of Chicago sociologist Andrew Abbott in the 1980s, and they have since developed in ways that are unique to the social sciences. Scholars in psychology, economics, anthropology, demography, communication, political science, learning sciences, organizational studies, and especially sociology have been using sequence methods ever since.

In sociology, sequence techniques are most commonly employed in studies of patterns of life-course development, cycles, and life histories. There has been a great deal of work on the sequential development of careers, and there is increasing interest in how career trajectories intertwine with life-course sequences. Many scholars have used sequence techniques to model how work and family activities are linked in household divisions of labor and the problem of schedule synchronization within families. The study of interaction patterns is increasingly centered on sequential concepts, such as turn-taking, the predominance of reciprocal utterances, and the strategic solicitation of preferred types of responses (see Conversation Analysis). Social network analysts (see Social network analysis) have begun to turn to sequence methods and concepts to understand how social contacts and activities are enacted in real time, and to model and depict how whole networks evolve. Social network epidemiologists have begun to examine social contact sequencing to better understand the spread of disease. Psychologists have used those methods to study how the order of information affects learning, and to identify structure in interactions between individuals (see Sequence learning).

Many of the methodological developments in sequence analysis came on the heels of a special section devoted to the topic in a 2000 issue of Sociological Methods & Research, which hosted a debate over the use of the optimal matching (OM) edit distance for comparing sequences. In particular, sociologists objected to the descriptive and data-reducing orientation of optimal matching, as well as to a lack of fit between bioinformatic sequence methods and uniquely social phenomena. The debate has given rise to several methodological innovations (see Pairwise dissimilarities below) that address limitations of early sequence comparison methods developed in the 20th century. In 2006, David Stark and Balazs Vedres proposed the term "social sequence analysis" to distinguish the approach from bioinformatic sequence analysis. However, if we except the nice book by Benjamin Cornwell, the term was seldom used, probably because the context prevents any confusion in the SA literature. Sociological Methods & Research organized a special issue on sequence analysis in 2010, leading to what Aisenbrey and Fasang referred to as the "second wave of sequence analysis", which mainly extended optimal matching and introduced other techniques to compare sequences. Alongside sequence comparison, recent advances in SA concerned among others the visualization of sets of sequence data, the measure and analysis of the discrepancy of sequences, the identification of representative sequences, and the development of summary indicators of individual sequences. Raab and Struffolino have conceived more recent advances as the third wave of sequence analysis. This wave is largely characterized by the effort of bringing together the stochastic and the algorithmic modeling culture by jointly applying SA with more established methods such as analysis of variance, event history analysis, Markovian modeling, social network analysis, or causal analysis and statistical modeling in general.

The analysis of sequence patterns has foundations in sociological theories that emerged in the middle of the 20th century. Structural theorists argued that society is a system that is characterized by regular patterns. Even seemingly trivial social phenomena are ordered in highly predictable ways. This idea serves as an implicit motivation behind social sequence analysts' use of optimal matching, clustering, and related methods to identify common "classes" of sequences at all levels of social organization, a form of pattern search. This focus on regularized patterns of social action has become an increasingly influential framework for understanding microsocial interaction and contact sequences, or "microsequences." This is closely related to Anthony Giddens's theory of structuration, which holds that social actors' behaviors are predominantly structured by routines, and which in turn provides predictability and a sense of stability in an otherwise chaotic and rapidly moving social world. This idea is also echoed in Pierre Bourdieu's concept of habitus, which emphasizes the emergence and influence of stable worldviews in guiding everyday action and thus produce predictable, orderly sequences of behavior. The resulting influence of routine as a structuring influence on social phenomena was first illustrated empirically by Pitirim Sorokin, who led a 1939 study that found that daily life is so routinized that a given person is able to predict with about 75% accuracy how much time they will spend doing certain things the following day. Talcott Parsons's argument that all social actors are mutually oriented to their larger social systems (for example, their family and larger community) through social roles also underlies social sequence analysts' interest in the linkages that exist between different social actors' schedules and ordered experiences, which has given rise to a considerable body of work on synchronization between social actors and their social contacts and larger communities. All of these theoretical orientations together warrant critiques of the general linear model of social reality, which as applied in most work implies that society is either static or that it is highly stochastic in a manner that conforms to Markov processes This concern inspired the initial framing of social sequence analysis as an antidote to general linear models. It has also motivated recent attempts to model sequences of activities or events in terms as elements that link social actors in non-linear network structures This work, in turn, is rooted in Georg Simmel's theory that experiencing similar activities, experiences, and statuses serves as a link between social actors.