Social_sequence_analysis

Sequence analysis in social sciences

Sequence analysis in social sciences

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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.

Index plot of 10 family life sequences

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 80s by Andrew Abbott,[1][2] SA has gained much popularity after the release of dedicated software such as the SQ[3] and SADI[4] addons for Stata and the TraMineR R package[5] with its companions TraMineRextras[6] and WeightedCluster.[7]

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

History

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.[8] Scholars in psychology, economics, anthropology, demography, communication, political science, 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.[9][10][11][12] There has been a great deal of work on the sequential development of careers,[13][14][15] and there is increasing interest in how career trajectories intertwine with life-course sequences.[16][17] 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.[18][19][20] 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,[21][22] and to model and depict how whole networks evolve.[23] Social network epidemiologists have begun to examine social contact sequencing to better understand the spread of disease.[24] 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[10] 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.[25][26] 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[23] proposed the term "social sequence analysis" to distinguish the approach from bioinformatic sequence analysis. However, if we except the nice book by Benjamin Cornwell,[27] 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[28] 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,[5][29] the measure and analysis of the discrepancy of sequences,[30] the identification of representative sequences,[31] and the development of summary indicators of individual sequences.[32] Raab and Struffolino[33] 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[34] 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.[35][36][37][27][30][38][39]

Domain-specific theoretical foundation

Sociology

The analysis of sequence patterns has foundations in sociological theories that emerged in the middle of the 20th century.[27] 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.[40] 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."[41] 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.[42] 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.[43] 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.[44] Talcott Parsons's argument[40] 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.[19][18][45] 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[1][46] 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[47][48] 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.[49][50]

Demography and historical demography

In demography and historical demography, from the 1980s the rapid appropriation of the life course perspective and methods was part of a substantive paradigmatic change that implied a stronger embedment of demographic processes into social sciences dynamics. After a first phase with a focus on the occurrence and timing of demographic events studied separately from each other with a hypothetico-deductive approach, from the early 2000s[34][51] the need to consider the structure of the life courses and to make justice to its complexity led to a growing use of sequence analysis with the aim of pursuing a holistic approach. At an inter-individual level, pairwise dissimilarities and clustering appeared as the appropriate tools for revealing the heterogeneity in human development. For example, the meta-narrations contrasting individualized Western societies with collectivist societies in the South (especially in Asia) were challenged by comparative studies revealing the diversity of pathways to legitimate reproduction.[52] At an intra-individual level, sequence analysis integrates the basic life course principle that individuals interpret and make decision about their life according to their past experiences and their perception of contingencies.[34] The interest for this perspective was also promoted by the changes in individuals' life courses for cohorts born between the beginning and the end of the 20th century. These changes have been described as de-standardization, de-synchronization, de-institutionalization.[53] Among the drivers of these dynamics, the transition to adulthood is key:[54] for more recent birth cohorts this crucial phase along individual life courses implied a larger number of events and lengths of the state spells experienced. For example, many postponed leaving parental home and the transition to parenthood, in some context cohabitation replaced marriage as long-lasting living arrangement, and the birth of the first child occurs more frequently while parents cohabit instead of within a wedlock.[55] Such complexity required to be measured to be able to compare quantitative indicators across birth cohorts[11][56] (see[57] for an extension of this questioning to populations from low- and medium income countries). The demography's old ambition to develop a 'family demography' has found in the sequence analysis a powerful tool to address research questions at the cross-road with other disciplines: for example, multichannel techniques[58] represent precious opportunities to deal with the issue of compatibility between working and family lives.[59][37] Similarly, more recent combinations of sequence analysis and event history analysis have been developed (see[36] for a review) and can be applied, for instance, for understanding of the link between demographic transitions and health.

Political sciences

The analysis of temporal processes in the domain of political sciences[60] regards how institutions, that is, systems and organizations (regimes, governments, parties, courts, etc.) that crystallize political interactions, formalize legal constraints and impose a degree of stability or inertia. Special importance is given to, first, the role of contexts, which confer meaning to trends and events, while shared contexts offer shared meanings; second, to changes over time in power relationships, and, subsequently, asymmetries, hierarchies, contention, or conflict; and, finally, to historical events that are able to shape trajectories, such as elections, accidents, inaugural speeches, treaties, revolutions, or ceasefires. Empirically, political sequences' unit of analysis can be individuals, organizations, movements, or institutional processes. Depending on the unit of analysis, the sample sizes may be limited few cases (e.g., regions in a country when considering the turnover of local political parties over time) or include a few hundreds (e.g., individuals' voting patterns). Three broad kinds of political sequences may be distinguished. The first and most common is careers, that is, formal, mostly hierarchical positions along which individuals progress in institutional environments, such as parliaments, cabinets, administrations, parties, unions or business organizations.[61][62][63] We may name trajectories political sequences that develop in more informal and fluid contexts, such as activists evolving across various causes and social movements,[64][65] or voters navigating a political and ideological landscape across successive polls.[66] Finally, processes relate to non-individual entities, such as: public policies developing through successive policy stages across distinct arenas;[67] sequences of symbolic or concrete interactions between national and international actors in diplomatic and military contexts;[68][69] and development of organizations or institutions, such as pathways of countries towards democracy (Wilson 2014).[70]

Concepts

A sequence s is an ordered list of elements (s1,s2,...,sl) taken from a finite alphabet A. For a set S of sequences, three sizes matter: the number n of sequences, the size a = |A| of the alphabet, and the length l of the sequences (that could be different for each sequence). In social sciences, n is generally something between a few hundreds and a few thousands, the alphabet size remains limited (most often less than 20), while sequence length rarely exceeds 100.

We may distinguish between state sequences and event sequences,[71] where states last while events occur at one time point and do not last but contribute possibly together with other events to state changes. For instance, the joint occurrence of the two events leaving home and starting a union provoke a state change from 'living at home with parents' to 'living 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 aabbbc, bbb is a spell of length 3 in state b, and the whole sequence can be represented as (a,2)-(b,3)-(c,1).[71]

Basic concept of sequence analysis in social sciences

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)[10] 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.[35][8]) 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

Cross sectional view of sequences

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.

Chronogram of school to work transition by grade achieved at end of compulsory school. Monthly data from McVicar & Anyadike-Danes (2002)[72]

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 [5] pp 18–21).

Longitudinal view of sequences

Characteristics of individual sequences

Alternatively, we can look at the rows. The index plot[73] where each sequence is represented as a horizontal stacked bar or line is the basic plot for rendering individual sequences.

Index plot of school to work transition by grade achieved at end of compulsory school. Sequences sorted by state from the start. Monthly data from McVicar & Anyadike-Danes (2002)[72]

We can compute characteristics of the individual sequences and examine the cross-sectional distribution of these characteristics.

Main indicators of individual sequences[32]

  • Basic measures
    • Length
    • Number of states visited
    • Number of transitions (length of sequence of distinct successive states, DSS)
    • Number of subsequences[11][74]
    • Recurrence[56]
  • Diversity
    • Within sequence entropy[5]
    • Variance of spell duration[11][32]
  • Complexity of the sequence structure
  • Measures that take account of the nature of the states
    • Normative volatility[15] i.e. proportion of positive spells.
    • Integration index[15] also known as Quality index[77]
    • Degradation[32]
    • Badness[32]
    • Precarity index[78]
    • Insecurity[32]

Other overall descriptive measures

  • Mean time in the different states (overall state distribution) and their standard errors[5]
  • Transition probabilities between states.[5][4]

Visualization

State sequences can nicely be rendered graphically and such plots prove useful for interpretation purposes. As shown above, the two basic plots are the index plot that renders individual sequences and the chronogram that renders the evolution of the cross-sectional state distribution along the timeframe. Chronograms (also known as status proportion plot or state distribution plot) completely overlook the diversity of the sequences, while index plots are often too scattered to be readable. Relative frequency plots and plots of representative sequences attempt to increase the readability of index plots without falling in the oversimplification of a chronogram. In addition, there are many plots that focus on specific characteristics of the sequences. Below is a list of plots that have been proposed in the literature for rendering large sets of sequences. For each plot, we give examples of software (details in section Software) that produce it.

  • Index plot: renders the set of individual sequences[73] (SADI, SQ, TraMineR)
  • Chronogram (status proportion plot, state distribution plot): renders the sequence of cross-sectional distributions[79] (SADI, SQ, TraMineR)
  • Plot of multidomain/multichannel sequences grouped by channels[58][39] (TraMineR, seqHMM) or by individuals[64]
  • Plot of time series of cross-sectional indicators (entropy,[79] modal state, ...) (SQ, TraMineR)
  • Frequency plot (SQ, TraMineR)
  • Relative frequency plot[80] (TraMineR)
  • Representative sequences[31] (TraMineR)
  • Mean time in the different states and their standard errors (TraMineR)
  • State survival plot (TraMineRextras)
  • Position-wise group typical states, i.e., with highest implication strength[81] (TraMineRextras)
  • Transition patterns[4] (SADI)
  • Transition plot[82] (SQ; Gmisc[83]) and plot of transition probabilities[39] (seqHMM)
  • Parallel coordinate plot[84][29] (TraMineR, SQ)
  • Probabilistic suffix trees[38] (PST)
  • Sequence networks[27][85] (see social network analysis, Social network analysis software)
  • Narrative networks[48] (Software?)

Pairwise dissimilarities

Pairwise dissimilarities between sequences serve to compare sequences and many advanced SA methods are based on these dissimilarities. The most popular dissimilarity measure is optimal matching (OM), i.e. the minimal cost of transforming one sequence into the other by means of indel (insert or delete) and substitution operations with possibly costs of these elementary operations depending on the states involved. SA is so intimately linked with OM that it is sometimes named optimal matching analysis (OMA).

There are roughly three categories of dissimilarity measures:[86]

  • Optimal matching and other edit distances
    • Examples: OM,[87][2] OMloc (localized OM),[88] OMslen (spell-length sensitive OM),[89] OMspell (OM of spell sequences),[86] OMstran (OM of sequences of transitions),[90][86] TWED (time-warp edit distance),[91][92] HAM (Hamming and generalized Hamming),[93][86] DHD (Dynamic Hamming).[94]
    • Strategies for setting the substitution and indel costs
      • Constant costs (all substitution costs identical and single indel cost)
      • Theory-based costs
      • Feature-based costs[88]
      • Data-driven costs: based on transition probabilities[95] or state frequencies[86]
  • Measures based on the count of common attributes
    • Examples: LCS (derived from length of longest common subsequence), LCP (from length of longest common prefix), NMS (number of matching subsequences),[96] and NMSMST and SVRspell two variants of NMS.[97]
  • Distances between within-sequence state distributions
    • Examples: CHI2 and EUCLID defined as the average of respectively the Chi-squared and Euclidean distance between state distributions in successive sliding windows.[98][86]

Dissimilarity-based analysis

Pairwise dissimilarities between sequences give access to a series of techniques to discover holistic structuring characteristics of the sequence data. In particular, dissimilarities between sequences can serve as input to cluster algorithms and multidimensional scaling, but also allow to identify medoids or other representative sequences, define neighborhoods, measure the discrepancy of a set of sequences, proceed to ANOVA-like analyses, and grow regression trees.

Other methods of analysis

Although dissimilarity-based methods play a central role in social SA, essentially because of their ability to preserve the holistic perspective, several other approaches also prove useful for analyzing sequence data.

Advances: the third wave of sequence analysis

Some recent advances can be conceived as the third wave of SA.[33] 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, network analysis, or causal analysis and statistical modeling in general. Some examples are given below; see also "Other methods of analysis".

  • Effect of past trajectories on the hazard of an event: Sequence History Analysis, SHA[120]
  • Effect of time varying covariates on trajectories: Competing Trajectories Analysis (CTA),[121] and Sequence Analysis Multistate Model (SAMM)[122]
  • Validation of cluster typologies[123]
  • Discrepancy analysis to bring time back to qualitative comparative analysis (QCA)[124]

Open issues and limitations

Although SA witnesses a steady inflow of methodological contributions that address the issues raised two decades ago,[28] some pressing open issues remain.[36] Among the most challenging, we can mention:

  • Sequences of different lengths, truncated sequences, and missing values.[35][125][126]
  • Validation of cluster results[123][127]
  • Sequence length vs importance of recency: for example, when analyzing biographic sequences 40 year-long from age 1 to 40, one can only consider individuals born 40 years earlier and therefore the behavior of younger birth cohorts is disregarded.[122]

Up-to-date information on advances, methodological discussions, and recent relevant publications can be found on the Sequence Analysis Association webpage.

Fields of application

These techniques have proved valuable in a variety of contexts. In life-course research, for example, research has shown that retirement plans are affected not just by the last year or two of one's life, but instead how one's work and family careers unfolded over a period of several decades. People who followed an "orderly" career path (characterized by consistent employment and gradual ladder-climbing within a single organization) retired earlier than others, including people who had intermittent careers, those who entered the labor force late, as well as those who enjoyed regular employment but who made numerous lateral moves across organizations throughout their careers.[12] In the field of economic sociology, research has shown that firm performance depends not just on a firm's current or recent social network connectedness, but also the durability or stability of their connections to other firms. Firms that have more "durably cohesive" ownership network structures attract more foreign investment than less stable or poorly connected structures.[23] Research has also used data on everyday work activity sequences to identify classes of work schedules, finding that the timing of work during the day significantly affects workers' abilities to maintain connections with the broader community, such as through community events.[19] More recently, social sequence analysis has been proposed as a meaningful approach to study trajectories in the domain of creative enterprise, allowing the comparison among the idiosyncrasies of unique creative careers.[128] While other methods for constructing and analyzing whole sequence structure have been developed during the past three decades, including event structure analysis,[117][118] OM and other sequence comparison methods form the backbone of research on whole sequence structures.

Some examples of application include:

Sociology

  • Labor market entry sequences[15]
  • De-standardization of the life course[11][17]
  • Residential trajectories[129]
  • Time use[18][130][131]
  • Actual and idealized relationship scripts[132]
  • Basic types of figures in ritual dances[1]
  • Pathways of alcohol consumption[133]

Demography and historical demography

  • Transition to adulthood[134][135]
  • Partnership biographies[136]
  • Family formation life course[137]
  • Childbirth histories[31]

Political sciences

  • Pathways towards democratization[138]
  • Pathways of legislative processes[139]
  • Bargaining between actors during national crises[69]

Education and learning sciences

Psychology

  • Sequences of adolescences' social interactions[143]

Medical research

  • Care trajectory in chronic disease[144]

Survey methodology

Geography

Software

Two main statistical computing environment offer tools to conduct a sequence analysis in the form of user-written packages: Stata and R.

  • Stata: SQ[3] and SADI[4] are general SA toolkits. MICT[125] is dedicated to imputation of missing elements in sequences.
  • R: TraMineR[5] with its extension TraMineRextras[6] is probably the most comprehensive SA toolkit; ggseqplot,[153] provides ggplot versions of most TraMineR plots; seqhandbook[154] provides several specific tools such as heat maps of sequence data and the GIMSA method for measuring dissimilarities between multidomain sequences; seqimpute[155] provides tools for imputing missing elements in sequences; seqHMM,[39] although specialized in fitting Markov models, this package provides useful plotting facilities for rendering multichannel sequences and transition probabilities; WeightedCluster[7] versatile clustering package with original tools for grouping identical sequences and rendering hierarchical trees of sequences; PST[38] fits and renders probabilistic suffix trees of sequences.

Institutional development

The first international conference dedicated to social-scientific research that uses sequence analysis methods – the Lausanne Conference on Sequence Analysis, or LaCOSA – was held in Lausanne, Switzerland in June 2012.[156] A second conference (LaCOSA II) was held in Lausanne in June 2016.[157][158] The Sequence Analysis Association (SAA) was founded at the International Symposium on Sequence Analysis and Related Methods, in October 2018 at Monte Verità, TI, Switzerland. The SAA is an international organization whose goal is to organize events such as symposia and training courses and related events, and to facilitate scholars' access to sequence analysis resources.

See also

References

  1. [1]
    Abbott, Andrew (1983). "Sequences of Social Events: Concepts and Methods for the Analysis of Order in Social Processes". Historical Methods: A Journal of Quantitative and Interdisciplinary History. 16 (4): 129–147. doi:10.1080/01615440.1983.10594107. ISSN 0161-5440.
  2. [2]
    Abbott, Andrew; Forrest, John (1986). "Optimal Matching Methods for Historical Sequences". Journal of Interdisciplinary History. 16 (3): 471. doi:10.2307/204500. JSTOR 204500.
  3. [3]
    Brzinsky-Fay, Christian; Kohler, Ulrich; Luniak, Magdalena (2006). "Sequence Analysis with Stata". The Stata Journal: Promoting Communications on Statistics and Stata. 6 (4): 435–460. doi:10.1177/1536867X0600600401. ISSN 1536-867X. S2CID 15581275.
  4. [4]
    Halpin, Brendan (2017). "SADI: Sequence Analysis Tools for Stata". The Stata Journal: Promoting Communications on Statistics and Stata. 17 (3): 546–572. doi:10.1177/1536867X1701700302. hdl:10344/3783. ISSN 1536-867X. S2CID 62691156.
  5. [5]
    Gabadinho, Alexis; Ritschard, Gilbert; Müller, Nicolas S.; Studer, Matthias (2011). "Analyzing and Visualizing State Sequences in R with TraMineR". Journal of Statistical Software. 40 (4). doi:10.18637/jss.v040.i04. ISSN 1548-7660. S2CID 4603927.
  6. [6]
    Ritschard, Gilbert; Studer, Matthias; Buergin, Reto; Liao, Tim; Gabadinho, Alexis; Fonta, Pierre-Alexandre; Muller, Nicolas; Rousset, Patrick (2021-06-24), TraMineRextras: TraMineR Extension, CRAN, retrieved 2021-09-26
  7. [7]
    Studer, Matthias (2013). "WeightedCluster Library Manual: A practical guide to creating typologies of trajectories in the social sciences with R". LIVES Working Papers. 24. doi:10.12682/lives.2296-1658.2013.24.
  8. [8]
    Liao, Tim F.; Bolano, Danilo; Brzinsky-Fay, Christian; Cornwell, Benjamin; Fasang, Anette Eva; Helske, Satu; Piccarreta, Raffaella; Raab, Marcel; Ritschard, Gilbert; Struffolino, Emanuela; Studer, Matthias (2022-08-26). "Sequence analysis: Its past, present, and future". Social Science Research. 107: 102772. doi:10.1016/j.ssresearch.2022.102772. hdl:2434/936870. PMID 36058612. S2CID 251873619.
  9. [9]
    Abbott, Andrew (1995). "Sequence Analysis: New Methods for Old Ideas". Annual Review of Sociology. 21 (1): 93–113. doi:10.1146/annurev.so.21.080195.000521. ISSN 0360-0572.
  10. [10]
    Abbott, Andrew; Tsay, Angela (2000). "Sequence Analysis and Optimal Matching Methods in Sociology: Review and Prospect". Sociological Methods & Research. 29 (1): 3–33. doi:10.1177/0049124100029001001. ISSN 0049-1241. S2CID 121097811.
  11. [11]
    Elzinga, Cees H.; Liefbroer, Aart C. (2007). "De-standardization of Family-Life Trajectories of Young Adults: A Cross-National Comparison Using Sequence Analysis: Dé-standardisation des trajectoires de vie familiale des jeunes adultes: comparaison entre pays par analyse séquentielle". European Journal of Population / Revue européenne de Démographie. 23 (3–4): 225–250. doi:10.1007/s10680-007-9133-7. ISSN 0168-6577. S2CID 15176366.
  12. [12]
    Han, Shin-Kap; Moen, Phyllis (1999). "Work and Family Over Time: A Life Course Approach". The Annals of the American Academy of Political and Social Science. 562 (1): 98–110. doi:10.1177/000271629956200107. ISSN 0002-7162. S2CID 146614540.
  13. [13]
    Abbott, Andrew; Hrycak, Alexandra (1990). "Measuring Resemblance in Sequence Data: An Optimal Matching Analysis of Musicians' Careers". American Journal of Sociology. 96 (1): 144–185. doi:10.1086/229495. ISSN 0002-9602. S2CID 145014876.
  14. [14]
    Blair-Loy, Mary (1999). "Career Patterns of Executive Women in Finance: An Optimal Matching Analysis". American Journal of Sociology. 104 (5): 1346–1397. doi:10.1086/210177. ISSN 0002-9602. S2CID 144155808.
  15. [15]
    Brzinsky-Fay, C. (2007). "Lost in Transition? Labour Market Entry Sequences of School Leavers in Europe". European Sociological Review. 23 (4): 409–422. doi:10.1093/esr/jcm011. ISSN 0266-7215.
  16. [16]
    Pollock, Gary (2007). "Holistic trajectories: a study of combined employment, housing and family careers by using multiple-sequence analysis". Journal of the Royal Statistical Society, Series A (Statistics in Society). 170 (1): 167–183. doi:10.1111/j.1467-985X.2006.00450.x. ISSN 0964-1998. S2CID 123689911.
  17. [17]
    Widmer, Eric D.; Ritschard, Gilbert (2009). "The de-standardization of the life course: Are men and women equal?". Advances in Life Course Research. 14 (1–2): 28–39. doi:10.1016/j.alcr.2009.04.001.
  18. [18]
    Lesnard, Laurent (2008). "Off-Scheduling within Dual-Earner Couples: An Unequal and Negative Externality for Family Time". American Journal of Sociology. 114 (2): 447–490. doi:10.1086/590648. ISSN 0002-9602. S2CID 144446191.
  19. [19]
    Cornwell, Benjamin; Warburton, Elizabeth (2014). "Work Schedules and Community Ties". Work and Occupations. 41 (2): 139–174. doi:10.1177/0730888413498399. ISSN 0730-8884. S2CID 145332280.
  20. [20]
    Wight, V. R.; Raley, S. B.; Bianchi, S. M. (2008). "Time for Children, One's Spouse and Oneself among Parents Who Work Nonstandard Hours". Social Forces. 87 (1): 243–271. doi:10.1353/sof.0.0092. ISSN 0037-7732. S2CID 145627027.
  21. [21]
    Butts, Carter T. (2008). "4. A Relational Event Framework for Social Action". Sociological Methodology. 38 (1): 155–200. doi:10.1111/j.1467-9531.2008.00203.x. ISSN 0081-1750. S2CID 120970495.
  22. [22]
    Cornwell, Benjamin (2013). "Switching Dynamics and the Stress Process". Social Psychology Quarterly. 76 (2): 99–124. doi:10.1177/0190272513482133. ISSN 0190-2725. PMC 4126261. PMID 25110381.
  23. [23]
    Stark, David; Vedres, Balázs (2006). "Social Times of Network Spaces: Network Sequences and Foreign Investment in Hungary". American Journal of Sociology. 111 (5): 1367–1411. doi:10.1086/499507. ISSN 0002-9602. S2CID 9458839.
  24. [24]
    Morris, Martina; Kretzschmar, Mirjam (1995). "Concurrent partnerships and transmission dynamics in networks". Social Networks. 17 (3–4): 299–318. doi:10.1016/0378-8733(95)00268-S.
  25. [25]
    Levine, Joel H. (2000). "But What Have You Done for Us Lately?: Commentary on Abbott and Tsay". Sociological Methods & Research. 29 (1): 34–40. doi:10.1177/0049124100029001002. ISSN 0049-1241. S2CID 119966819.
  26. [26]
    Wu, Lawrence L. (2000). "Some Comments on "Sequence Analysis and Optimal Matching Methods in Sociology: Review and Prospect"". Sociological Methods & Research. 29 (1): 41–64. doi:10.1177/0049124100029001003. ISSN 0049-1241. S2CID 145351355.
  27. [27]
    Cornwell, Benjamin (2015). Social Sequence Analysis: Methods and Applications. Cambridge: Cambridge University Press. doi:10.1017/cbo9781316212530. ISBN 978-1-316-21253-0.
  28. [28]
    Aisenbrey, Silke; Fasang, Anette E. (2010). "New Life for Old Ideas: The "Second Wave" of Sequence Analysis Bringing the "Course" Back Into the Life Course". Sociological Methods & Research. 38 (3): 420–462. doi:10.1177/0049124109357532. ISSN 0049-1241. S2CID 60532456.
  29. [29]
    Brzinsky-Fay, Christian (2014), Blanchard, Philippe; Bühlmann, Felix; Gauthier, Jacques-Antoine (eds.), "Graphical Representation of Transitions and Sequences", Advances in Sequence Analysis: Theory, Method, Applications, Life course Research and Social Policies, vol. 2, Cham: Springer International Publishing, pp. 265–284, doi:10.1007/978-3-319-04969-4_14, ISBN 978-3-319-04968-7, retrieved 2021-09-28
  30. [30]
    Studer, Matthias; Ritschard, Gilbert; Gabadinho, Alexis; Müller, Nicolas S. (2011). "Discrepancy Analysis of State Sequences". Sociological Methods & Research. 40 (3): 471–510. doi:10.1177/0049124111415372. ISSN 0049-1241. S2CID 13307797.
  31. [31]
    Gabadinho, Alexis; Ritschard, Gilbert (2013). Levy, René; Widmer, Eric D. (eds.). "Searching for typical life trajectories, applied to childbirth histories". Gendered Life Courses, Between Standardization and Individualization: A European Approach Applied to Switzerland. Zurich: LIT: 287–312.
  32. [32]
    Ritschard, Gilbert (2021). "Measuring the Nature of Individual Sequences". Sociological Methods & Research. 52 (4): 2016–2049. doi:10.1177/00491241211036156. ISSN 0049-1241. S2CID 236454203.
  33. [33]
    Raab, Marcel; Struffolino, Emanuela (2022). Sequence Analysis. Quantitative Application in the Social Sciences. Vol. 190. [S.l.]: Sage Publications. ISBN 978-1-0718-0188-8. OCLC 1286311303.
  34. [34]
    Billari, Francesco C. (2005). "Life course analysis: two (complementary) cultures? Some reflections with examples from the analysis of the transition to adulthood". Advances in Life Course Research. 10: 261–281. doi:10.1016/S1040-2608(05)10010-0.
  35. [35]
    Ritschard, Gilbert; Studer, Matthias (2018), Ritschard, Gilbert; Studer, Matthias (eds.), "Sequence Analysis: Where Are We, Where Are We Going?", Sequence Analysis and Related Approaches, Life Course Research and Social Policies, vol. 10, Cham: Springer International Publishing, pp. 1–11, doi:10.1007/978-3-319-95420-2_1, ISBN 978-3-319-95419-6, S2CID 70139529
  36. [36]
    Piccarreta, Raffaella; Studer, Matthias (2019). "Holistic analysis of the life course: Methodological challenges and new perspectives". Advances in Life Course Research. 41: 100251. doi:10.1016/j.alcr.2018.10.004. PMID 36738029. S2CID 149677674.
  37. [37]
    Barban, Nicola; Sironi, Maria (2019), Schoen, Robert (ed.), "Sequence Analysis as a Tool for Family Demography", Analytical Family Demography, The Springer Series on Demographic Methods and Population Analysis, vol. 47, Cham: Springer International Publishing, pp. 101–123, doi:10.1007/978-3-319-93227-9_5, ISBN 978-3-319-93226-2, S2CID 165317119, retrieved 2021-09-29
  38. [38]
    Gabadinho, Alexis; Ritschard, Gilbert (2016). "Analyzing State Sequences with Probabilistic Suffix Trees: The PST R Package". Journal of Statistical Software. 72 (3). doi:10.18637/jss.v072.i03. ISSN 1548-7660. S2CID 63681202.
  39. [39]
    Helske, Satu; Helske, Jouni (2019). "Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R". Journal of Statistical Software. 88 (3). arXiv:1704.00543. doi:10.18637/jss.v088.i03. ISSN 1548-7660. S2CID 14192465.
  40. [40]
    Parsons, Talcott (1951). The social system. London: Routledge. ISBN 0-203-99295-4. OCLC 69952066.
  41. [41]
    Gershuny, Jonathan (2000). Changing times : work and leisure in postindustrial society. Oxford [England]: Oxford University Press. ISBN 0-19-828787-9. OCLC 43930105.
  42. [42]
    Giddens, Anthony (1986). The constitution of society : outline of the theory of structuration (1st pbk. ed.). Berkeley: University of California Press. ISBN 0-520-05728-7. OCLC 19097700.
  43. [43]
    Bourdieu, Pierre (1984). Distinction : a social critique of the judgement of taste. Richard Nice. Cambridge, Mass.: Harvard University Press. ISBN 978-0-674-21280-0. OCLC 10323218.
  44. [44]
    Sokin, Pitirim Aleksandrovich; Berger, Clarence Quinn (1939). Time-budgets of human behavior. Cambridge, MA: Harvard University Press. ISBN 0674891600.
  45. [45]
    Presser, Harriet B. (1994). "Employment Schedules Among Dual-Earner Spouses and the Division of Household Labor by Gender". American Sociological Review. 59 (3): 348–364. doi:10.2307/2095938. JSTOR 2095938.
  46. [46]
    Abell, Peter (2004). "Narrative Explanation: An Alternative to Variable-Centered Explanation?". Annual Review of Sociology. 30 (1): 287–310. doi:10.1146/annurev.soc.29.010202.100113. ISSN 0360-0572.
  47. [47]
    Bearman, Peter; Faris, Robert; Moody, James (1999). "Blocking the Future: New Solutions for Old Problems in Historical Social Science". Social Science History. 23 (4): 501–533. doi:10.1017/S0145553200021854. ISSN 0145-5532. S2CID 142075647.
  48. [48]
    Bearman, Peter S.; Stovel, Katherine (2000). "Becoming a Nazi: A model for narrative networks". Poetics. 27 (2–3): 69–90. doi:10.1016/S0304-422X(99)00022-4. S2CID 143086214.
  49. [49]
    Simmel, Georg (1955) [1922]. Conflict & The Web of Group-Affiliations. Translated by Wolff, Kurt H.; Bendix, Reinhard. New York: Free Press.
  50. [50]
    Breiger, Ronald L. (1974). "The Duality of Persons and Groups". Social Forces. 53 (2): 181–190. doi:10.2307/2576011. JSTOR 2576011.
  51. [51]
    Ritschard, Gilbert; Oris, Michel (2005). "Life Course Data In Demography And Social Sciences: Statistical And Data-Mining Approaches". Advances in Life Course Research. 10: 283–314. doi:10.1016/S1040-2608(05)10011-2.
  52. [52]
    Dribe, Martin; Manfredini, Matteo; Oris, Michel (2014). Lundh, Christer; Kuroso, Satomi (eds.). "The roads to reproduction. Comparing life course trajectories in preindustrial Eurasia". Similarity in Difference. Marriage in Europe and Asia, 1700-1900. Harvard: MIT Press: 85–116. doi:10.7551/mitpress/9944.003.0010. ISBN 9780262325837.
  53. [53]
    Brückner, Hannah; Mayer, Karl Ulrich (2005). "De-Standardization of the Life Course: What it Might Mean? And if it Means Anything, Whether it Actually Took Place?". Advances in Life Course Research. 9: 27–53. doi:10.1016/S1040-2608(04)09002-1.
  54. [54]
    Billari, Francesco C.; Liefbroer, Aart C. (2010). "Towards a new pattern of transition to adulthood?". Advances in Life Course Research. 15 (2–3): 59–75. doi:10.1016/j.alcr.2010.10.003.
  55. [55]
    Burgin, Reto; Schumacher, Reto; Ritschard, Gilbert (2017). "Changes in the Order of Family Life Events in 20th-Century Europe: A Cross-Regional Perspective". Historical Life Course Studies. 4: 41–58. doi:10.51964/hlcs9338. ISSN 2352-6343.
  56. [56]
    Pelletier, David; Bignami-Van Assche, Simona; Simard-Gendron, Anaïs (2020). "Measuring Life Course Complexity with Dynamic Sequence Analysis". Social Indicators Research. 152 (3): 1127–1151. doi:10.1007/s11205-020-02464-y. ISSN 0303-8300. S2CID 225474747.
  57. [57]
    Pesando, Luca Maria; Barban, Nicola; Sironi, Maria; Furstenberg, Frank F. (2021). "A Sequence-Analysis Approach to the Study of the Transition to Adulthood in Low- and Middle-Income Countries". Population and Development Review. 47 (3): 719–747. doi:10.1111/padr.12425. ISSN 0098-7921. PMC 9292029. PMID 35873669. S2CID 226405934.
  58. [58]
    Ritschard, Gilbert; Liao, Tim F.; Struffolino, Emanuela (2023-04-25). "Strategies for Multidomain Sequence Analysis in Social Research". Sociological Methodology. 53 (2): 288–322. doi:10.1177/00811750231163833. hdl:2434/967184. ISSN 0081-1750. S2CID 258340377.
  59. [59]
    Studer, Matthias; Struffolino, Emanuela; Fasang, Anette E. (2018). "Estimating the Relationship between Time-varying Covariates and Trajectories: The Sequence Analysis Multistate Model Procedure". Sociological Methodology. 48 (1): 103–135. doi:10.1177/0081175017747122. hdl:10419/191543. ISSN 0081-1750. S2CID 125988462.
  60. [60]
    Blanchard, Philippe (2020), "Sequence Analysis", SAGE Research Methods Foundations, London, UK: SAGE Publications Ltd, doi:10.4135/9781526421036857077, ISBN 978-1-5264-2103-6, S2CID 241309596, retrieved 2021-09-29
  61. [61]
    Blanchard, Philippe; Dudouet, François-Xavier; Vion, Antoine (2015-10-15). "Le coeur des affaires de la zone euro.: Une analyse structurale et séquentielle des élites économiques transnationales". Cultures & Conflits (98): 71–99. doi:10.4000/conflits.19009. ISSN 1157-996X.
  62. [62]
    Ohmura, Tamaki; Bailer, Stefanie; Meiβner, Peter; Selb, Peter (2018). "Party animals, career changers and other pathways into parliament". West European Politics. 41 (1): 169–195. doi:10.1080/01402382.2017.1323485. ISSN 0140-2382. S2CID 158009222.
  63. [63]
    Claessen, Clint; Bailer, Stefanie; Turner-Zwinkels, Tomas (2021). "The winners of legislative mandate: An analysis of post-parliamentary career positions in Germany and the Netherlands". European Journal of Political Research. 60 (1): 25–45. doi:10.1111/1475-6765.12385. ISSN 0304-4130. S2CID 214312406.
  64. [64]
    Fillieule, O. and Blanchard, P. (2013). Fighting Together. Assessing Continuity and Change in Social Movement Organizations Through the Study of Constituencies' Heterogeneity. In A Political Sociology of Transnational Europe, chapter 4. ECPR Press, Colchester.
  65. [65]
    Henriksen, Lasse Folke; Seabrooke, Leonard (2016). "Transnational organizing: Issue professionals in environmental sustainability networks". Organization. 23 (5): 722–741. doi:10.1177/1350508415609140. ISSN 1350-5084. PMC 5405819. PMID 28490973.
  66. [66]
    Buton, François; Lemercier, Claire; Mariot, Nicolas (2012). "The household effect on electoral participation. A contextual analysis of voter signatures from a French polling station (1982–2007)". Electoral Studies. 31 (2): 434–447. doi:10.1016/j.electstud.2011.11.010.
  67. [67]
    Abbott, Andrew; DeViney, Stanley (1992). "The Welfare State as Transnational Event: Evidence from Sequences of Policy Adoption". Social Science History. 16 (2): 245–274. doi:10.1017/S0145553200016473. ISSN 0145-5532. S2CID 147541414.
  68. [68]
    Stovel, K. (2001). "Local Sequential Patterns: The Structure of Lynching in the Deep South, 1882-1930". Social Forces. 79 (3): 843–880. doi:10.1353/sof.2001.0026. ISSN 0037-7732. S2CID 131868278.
  69. [69]
    Casper, Gretchen; Wilson, Matthew (2015). "Using Sequences to Model Crises". Political Science Research and Methods. 3 (2): 381–397. doi:10.1017/psrm.2014.27. ISSN 2049-8470. S2CID 55149551.
  70. [70]
    Wilson, Matthew Charles (2014), Blanchard, Philippe; Bühlmann, Felix; Gauthier, Jacques-Antoine (eds.), "Governance Built Step-by-Step: Analysing Sequences to Explain Democratization", Advances in Sequence Analysis: Theory, Method, Applications, Life Course Research and Social Policies, vol. 2, Cham: Springer International Publishing, pp. 213–227, doi:10.1007/978-3-319-04969-4_11, ISBN 978-3-319-04968-7, retrieved 2021-09-29
  71. [71]
    Ritschard, Gilbert; Gabadinho, Alexis; Studer, Matthias; Müller, Nicolas S. (2009), Ras, Zbigniew W.; Dardzinska, Agnieszka (eds.), "Converting between Various Sequence Representations", Advances in Data Management, Studies in Computational Intelligence, vol. 223, Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 155–175, doi:10.1007/978-3-642-02190-9_8, ISBN 978-3-642-02189-3, retrieved 2021-09-29
  72. [72]
    McVicar, Duncan; Anyadike-Danes, Michael (2002). "Predicting successful and unsuccessful transitions from school to work by using sequence methods". Journal of the Royal Statistical Society, Series A (Statistics in Society). 165 (2): 317–334. doi:10.1111/1467-985X.00641. ISSN 0964-1998. S2CID 123442137.
  73. [73]
    Scherer, S. (2001). "Early Career Patterns: A Comparison of Great Britain and West Germany". European Sociological Review. 17 (2): 119–144. doi:10.1093/esr/17.2.119.
  74. [74]
    Elzinga, Cees H. (2010). "Complexity of Categorical Time Series". Sociological Methods & Research. 38 (3): 463–481. doi:10.1177/0049124109357535. ISSN 0049-1241. S2CID 119486274.
  75. [75]
    Brzinsky-Fay, C. (2018). Unused Resources: Sequence and Trajectory Indicators. In International Symposium on Sequence Analysis and Related Methods, Monte Verita, TI, Switzerland, October 10–12, 2018, .
  76. [76]
    Gabadinho, Alexis; Ritschard, Gilbert; Studer, Matthias; Müller, Nicolas S. (2010). "Indice de complexité pour le tri et la comparaison de séquences catégorielles". Revue des nouvelles technologies de l'information RNTI. E-19: 61–66.
  77. [77]
    Manzoni, Anna; Mooi-Reci, Irma (2018), Ritschard, Gilbert; Studer, Matthias (eds.), "Measuring Sequence Quality", Sequence Analysis and Related Approaches, Life Course Research and Social Policies, vol. 10, Cham: Springer International Publishing, pp. 261–278, doi:10.1007/978-3-319-95420-2_15, ISBN 978-3-319-95419-6, S2CID 158637400
  78. [78]
    Ritschard, Gilbert; Bussi, Margherita; O'Reilly, Jacqueline (2018), Ritschard, Gilbert; Studer, Matthias (eds.), "An Index of Precarity for Measuring Early Employment Insecurity", Sequence Analysis and Related Approaches, Life Course Research and Social Policies, vol. 10, Cham: Springer International Publishing, pp. 279–295, doi:10.1007/978-3-319-95420-2_16, ISBN 978-3-319-95419-6
  79. [79]
    Billari, Francesco C. (2001). "The analysis of early life courses: Complex descriptions of the transition to adulthood". Journal of Population Research. 18 (2): 119–142. doi:10.1007/BF03031885. ISSN 1443-2447. S2CID 145013793.
  80. [80]
    Fasang, Anette Eva; Liao, Tim Futing (2014). "Visualizing Sequences in the Social Sciences: Relative Frequency Sequence Plots". Sociological Methods & Research. 43 (4): 643–676. doi:10.1177/0049124113506563. hdl:10419/209702. ISSN 0049-1241. S2CID 61487252.
  81. [81]
    Studer, Matthias (2015). "Comment: On the Use of Globally Interdependent Multiple Sequence Analysis". Sociological Methodology. 45 (1): 81–88. doi:10.1177/0081175015588095. ISSN 0081-1750.
  82. [82]
    Brzinsky-Fay, Christian (2014), Blanchard, Philippe; Bühlmann, Felix; Gauthier, Jacques-Antoine (eds.), "Graphical Representation of Transitions and Sequences", Advances in Sequence Analysis: Theory, Method, Applications, Life Course Research and Social Policies, vol. 2, Cham: Springer International Publishing, pp. 265–284, doi:10.1007/978-3-319-04969-4_14, ISBN 978-3-319-04968-7, retrieved 2021-09-29
  83. [83]
    Gordon, Max (2021). "Gmisc: Descriptive Statistics, Transition Plots, and More". Comprehensive R Archive Network (CRAN) (R package version 2.0.1).
  84. [84]
    Bürgin, Reto; Ritschard, Gilbert (2014). "A Decorated Parallel Coordinate Plot for Categorical Longitudinal Data". The American Statistician. 68 (2): 98–103. doi:10.1080/00031305.2014.887591. ISSN 0003-1305. S2CID 121106778.
  85. [85]
    Bison, Ivano (2014), Blanchard, Philippe; Bühlmann, Felix; Gauthier, Jacques-Antoine (eds.), "Sequence as Network: An Attempt to Apply Network Analysis to Sequence Analysis", Advances in Sequence Analysis: Theory, Method, Applications, Life Course Research and Social Policies, vol. 2, Cham: Springer International Publishing, pp. 231–248, doi:10.1007/978-3-319-04969-4_12, ISBN 978-3-319-04968-7, retrieved 2021-09-29
  86. [86]
    Studer, Matthias; Ritschard, Gilbert (2016). "What matters in differences between life trajectories: a comparative review of sequence dissimilarity measures". Journal of the Royal Statistical Society, Series A (Statistics in Society). 179 (2): 481–511. doi:10.1111/rssa.12125. ISSN 0964-1998. S2CID 73566525.
  87. [87]
    Levenshtein, V. (1966). "Binary codes capable of correcting deletions, insertions, and reversals". Soviet Physics Doklady. 10: 707–710. Bibcode:1966SPhD...10..707L.
  88. [88]
    Hollister, Matissa (2009). "Is Optimal Matching Suboptimal?". Sociological Methods & Research. 38 (2): 235–264. doi:10.1177/0049124109346164. ISSN 0049-1241. S2CID 120824998.
  89. [89]
    Halpin, Brendan (2010). "Optimal Matching Analysis and Life-Course Data: The Importance of Duration". Sociological Methods & Research. 38 (3): 365–388. doi:10.1177/0049124110363590. hdl:10344/3627. ISSN 0049-1241. S2CID 56229999.
  90. [90]
    Biemann, Torsten (2011). "A Transition-Oriented Approach to Optimal Matching". Sociological Methodology. 41 (1): 195–221. doi:10.1111/j.1467-9531.2011.01235.x. ISSN 0081-1750. S2CID 60508791.
  91. [91]
    Marteau, Pierre-François (2009). "Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching". IEEE Transactions on Pattern Analysis and Machine Intelligence. 31 (2): 306–318. arXiv:cs/0703033. doi:10.1109/TPAMI.2008.76. ISSN 1939-3539. PMID 19110495. S2CID 10049446.
  92. [92]
    Halpin, Brendan (2014), Blanchard, Philippe; Bühlmann, Felix; Gauthier, Jacques-Antoine (eds.), "Three Narratives of Sequence Analysis", Advances in Sequence Analysis: Theory, Method, Applications, Life Course Research and Social Policies, vol. 2, Cham: Springer International Publishing, pp. 75–103, doi:10.1007/978-3-319-04969-4_5, hdl:10344/3624, ISBN 978-3-319-04969-4, retrieved 2021-10-01
  93. [93]
    Hamming, R. W. (1950). "Error Detecting and Error Correcting Codes". Bell System Technical Journal. 29 (2): 147–160. doi:10.1002/j.1538-7305.1950.tb00463.x. S2CID 61141773.
  94. [94]
    Lesnard, Laurent (2010). "Setting Cost in Optimal Matching to Uncover Contemporaneous Socio-Temporal Patterns". Sociological Methods & Research. 38 (3): 389–419. doi:10.1177/0049124110362526. ISSN 0049-1241. S2CID 4602338.
  95. [95]
    Rohwer, G. and Pötter, U. (2002). TDA User's Manual. Software, Ruhr-Universität Bochum, Fakultät für Sozialwissenschaften, Bochum. URL http://www.stat.ruhr-uni-bochum.de/tda.doc.
  96. [96]
    Elzinga, Cees H. (2003). "Sequence Similarity: A Nonaligning Technique". Sociological Methods & Research. 32 (1): 3–29. doi:10.1177/0049124103253373. ISSN 0049-1241. S2CID 16031880.
  97. [97]
    Elzinga, Cees H.; Studer, Matthias (2015). "Spell Sequences, State Proximities, and Distance Metrics". Sociological Methods & Research. 44 (1): 3–47. doi:10.1177/0049124114540707. ISSN 0049-1241. S2CID 53684713.
  98. [98]
    Deville, J.-C.; Saporta, G. (1983). "Correspondence analysis, with an extension towards nominal time series". Journal of Econometrics. 22 (1–2): 169–189. doi:10.1016/0304-4076(83)90098-2.
  99. [99]
    Liao, Tim Futing; Fasang, Anette Eva (2021). "Comparing Groups of Life-Course Sequences Using the Bayesian Information Criterion and the Likelihood-Ratio Test". Sociological Methodology. 51 (1): 44–85. doi:10.1177/0081175020959401. ISSN 0081-1750. S2CID 225163205.
  100. [100]
    Piccarreta, Raffaella; Billari, Francesco C. (2007). "Clustering work and family trajectories by using a divisive algorithm". Journal of the Royal Statistical Society, Series A (Statistics in Society). 170 (4): 1061–1078. doi:10.1111/j.1467-985X.2007.00495.x. ISSN 0964-1998. S2CID 121190067.
  101. [101]
    Stovel, Katherine; Savage, Michael; Bearman, Peter (1996). "Ascription into Achievement: Models of Career Systems at Lloyds Bank, 1890-1970". American Journal of Sociology. 102 (2): 358–399. doi:10.1086/230950. ISSN 0002-9602. S2CID 53967897.
  102. [102]
    Gauthier, Jacques-Antoine; Widmer, Eric D.; Bucher, Philipp; Notredame, Cédric (2010). "1. Multichannel Sequence Analysis Applied to Social Science Data". Sociological Methodology. 40 (1): 1–38. doi:10.1111/j.1467-9531.2010.01227.x. ISSN 0081-1750. S2CID 220749824.
  103. [103]
    Robette, Nicolas; Bry, Xavier; Lelièvre, Éva (2015). "A "Global Interdependence" Approach to Multidimensional Sequence Analysis". Sociological Methodology. 45 (1): 1–44. doi:10.1177/0081175015570976. ISSN 0081-1750. S2CID 56303196.
  104. [104]
    Piccarreta, Raffaella (2017). "Joint Sequence Analysis: Association and Clustering". Sociological Methods & Research. 46 (2): 252–287. doi:10.1177/0049124115591013. ISSN 0049-1241. S2CID 124969400.
  105. [105]
    Emery, Kevin; Berchtold, André (2022). "Comparison of two approaches in multichannel sequence analysis using the Swiss Household Panel". Longitudinal and Life Course Studies. -1 (aop): 592–623. doi:10.1332/175795921X16698302233894. ISSN 1757-9597. PMID 37874200. S2CID 254964548.
  106. [106]
    Liefbroer, Aart C.; Elzinga, Cees H. (2012). "Intergenerational transmission of behavioural patterns: How similar are parents' and children's demographic trajectories?". Advances in Life Course Research. 17 (1): 1–10. doi:10.1016/j.alcr.2012.01.002.
  107. [107]
    Liao, Tim (2021). "Using Sequence Analysis to Quantify How Strongly Life Courses Are Linked". Sociological Science. 8: 48–72. doi:10.15195/v8.a3. S2CID 231781073.
  108. [108]
    Vermunt, J., Tran, B. and Magidson, J. (2008). Latent class models in longitudinal research. In S Menard (ed.), Handbook of Longitudinal Research: Design, Measurement, and Analysis, pp. 373–385. Elsevier, Burlington, MA. URL https://www.statisticalinnovations.com/wp-content/uploads/Vermunt2008.pdf.
  109. [109]
    Barban, Nicola; Billari, Francesco C. (2012). "Classifying life course trajectories: a comparison of latent class and sequence analysis: Classifying Life Course Trajectories". Journal of the Royal Statistical Society, Series C (Applied Statistics). 61 (5): 765–784. doi:10.1111/j.1467-9876.2012.01047.x. hdl:10.1111/j.1467-9876.2012.01047.x. S2CID 122482708.
  110. [110]
    Han, Yu; Liefbroer, Aart; Elzinga, Cees (2017-10-26). "Comparing methods of classifying life courses: sequence analysis and latent class analysis". Longitudinal and Life Course Studies. 8 (4). doi:10.14301/llcs.v8i4.409. hdl:20.500.11755/04f4db6c-e4cd-4784-8b25-16a951ce8d2e.
  111. [111]
    Melnykov, Volodymyr (2016). "ClickClust : An R Package for Model-Based Clustering of Categorical Sequences". Journal of Statistical Software. 74 (9). doi:10.18637/jss.v074.i09. ISSN 1548-7660. S2CID 63370765.
  112. [112]
    Murphy, Keefe; Murphy, T. Brendan; Piccarreta, Raffaella; Gormley, I. Claire (2021-07-08). "Clustering longitudinal life-course sequences using mixtures of exponential-distance models". Journal of the Royal Statistical Society, Series A (Statistics in Society). 184 (4): 1414–1451. arXiv:1908.07963. doi:10.1111/rssa.12712. ISSN 0964-1998. S2CID 235828978.
  113. [113]
    Bison, Ivano (2014), Blanchard, Philippe; Bühlmann, Felix; Gauthier, Jacques-Antoine (eds.), "Sequence as Network: An Attempt to Apply Network Analysis to Sequence Analysis", Advances in Sequence Analysis: Theory, Method, Applications, Life Course Research and Social Policies, vol. 2, Cham: Springer International Publishing, pp. 231–248, doi:10.1007/978-3-319-04969-4_12, ISBN 978-3-319-04968-7, retrieved 2021-10-01
  114. [114]
    Cornwell, Benjamin; Watkins, Kate (2015-07-08), Thye, Shane R.; Lawler, Edward J. (eds.), "Sequence-Network Analysis: A New Framework for Studying Action in Groups", Advances in Group Processes, vol. 32, Emerald Group Publishing Limited, pp. 31–63, doi:10.1108/s0882-614520150000032002, ISBN 978-1-78560-077-7, retrieved 2021-10-01
  115. [115]
    Fitzhugh, Sean M.; Butts, Carter T.; Pixley, Joy E. (2015). "A life history graph approach to the analysis and comparison of life histories". Advances in Life Course Research. 25: 16–34. doi:10.1016/j.alcr.2015.05.001.
  116. [116]
    Berchtold, André; Raftery, Adrian (2002-08-01). "The Mixture Transition Distribution Model for High-Order Markov Chains and Non-Gaussian Time Series". Statistical Science. 17 (3). doi:10.1214/ss/1042727943. ISSN 0883-4237.
  117. [117]
    Heise, David R. (1989). "Modeling event structures*". The Journal of Mathematical Sociology. 14 (2–3): 139–169. doi:10.1080/0022250X.1989.9990048. ISSN 0022-250X.
  118. [118]
    Corsaro, William A.; Heise, David R. (1990). "Event Structure Models from Ethnographic Data". Sociological Methodology. 20: 1–57. doi:10.2307/271081. JSTOR 271081.
  119. [119]
    Ritschard, Gilbert; Bürgin, Reto; Studer, Matthias (2013). McArdle, John J.; Ritschard, Gilbert (eds.). "Exploratory Mining of Life Event Histories". Contemporary Issues in Exploratory Data Mining in the Behavioral Sciences. New York: Routledge: 221–253.
  120. [120]
    Rossignon, Florence; Studer, Matthias; Gauthier, Jacques-Antoine; Goff, Jean-Marie Le (2018), Ritschard, Gilbert; Studer, Matthias (eds.), "Sequence History Analysis (SHA): Estimating the Effect of Past Trajectories on an Upcoming Event", Sequence Analysis and Related Approaches, Life Course Research and Social Policies, vol. 10, Cham: Springer International Publishing, pp. 83–100, doi:10.1007/978-3-319-95420-2_6, ISBN 978-3-319-95419-6, S2CID 73556149, retrieved 2021-10-01
  121. [121]
    Studer, Matthias; Liefbroer, Aart C.; Mooyaart, Jarl E. (2018). "Understanding trends in family formation trajectories: An application of Competing Trajectories Analysis (CTA)". Advances in Life Course Research. 36: 1–12. doi:10.1016/j.alcr.2018.02.003. S2CID 148866407.
  122. [122]
    Studer, Matthias; Struffolino, Emanuela; Fasang, Anette E. (August 2018). "Estimating the Relationship between Time-varying Covariates and Trajectories: The Sequence Analysis Multistate Model Procedure". Sociological Methodology. 48 (1): 103–135. doi:10.1177/0081175017747122. hdl:10419/191543. ISSN 0081-1750. S2CID 125988462.
  123. [123]
    Studer, Matthias (2021). "Validating Sequence Analysis Typologies Using Parametric Bootstrap". Sociological Methodology. 51 (2): 290–318. doi:10.1177/00811750211014232. ISSN 0081-1750. PMC 8314995. PMID 34366497.
  124. [124]
    Borgna, Camilla; Struffolino, Emanuela (2018), Ritschard, Gilbert; Studer, Matthias (eds.), "Unpacking Configurational Dynamics: Sequence Analysis and Qualitative Comparative Analysis as a Mixed-Method Design", Sequence Analysis and Related Approaches, Life Course Research and Social Policies, vol. 10, Cham: Springer International Publishing, pp. 167–184, doi:10.1007/978-3-319-95420-2_10, hdl:2434/851427, ISBN 978-3-319-95419-6, S2CID 125101124
  125. [125]
    Halpin, Brendan (2016). "Multiple Imputation for Categorical Time Series". The Stata Journal: Promoting Communications on Statistics and Stata. 16 (3): 590–612. doi:10.1177/1536867X1601600303. ISSN 1536-867X. S2CID 124260727.
  126. [126]
    Lazar, Alina; Jin, Ling; Spurlock, C. Anna; Wu, Kesheng; Sim, Alex; Todd, Annika (2019). "Evaluating the Effects of Missing Values and Mixed Data Types on Social Sequence Clustering Using t-SNE Visualization". Journal of Data and Information Quality. 11 (2): 1–22. doi:10.1145/3301294. ISSN 1936-1955. S2CID 75139730.
  127. [127]
    Piccarreta, Raffaella; Struffolino, Emanuela (2019-09-26). "An Integrated Heuristic for Validation in Sequence Analysis". SocArXiv Papers. doi:10.31235/osf.io/v7mj8. S2CID 241175463.
  128. [128]
    Formilan, Giovanni; Ferriani, Simone; Cattani, Gino (2020), "A methodological essay on the application of social sequence analysis to the study of creative trajectories", Handbook of Research Methods on Creativity, Edward Elgar Publishing, pp. 329–350, doi:10.4337/9781786439659.00034, ISBN 978-1-78643-965-9, S2CID 225632580, retrieved 2021-10-01
  129. [129]
    Stovel, Katherine; Bolan, Marc (2004). "Residential Trajectories: Using Optimal Alignment to Reveal The Structure of Residential Mobility". Sociological Methods & Research. 32 (4): 559–598. doi:10.1177/0049124103262683. ISSN 0049-1241. S2CID 120959137.
  130. [130]
    Cornwell, Benjamin; Gershuny, Jonathan; Sullivan, Oriel (2019). "The Social Structure of Time: Emerging Trends and New Directions". Annual Review of Sociology. 45 (1): 301–320. doi:10.1146/annurev-soc-073018-022416. ISSN 0360-0572. S2CID 155798483.
  131. [131]
    Vagni, Giacomo (2020). "The social stratification of time use patterns". The British Journal of Sociology. 71 (4): 658–679. doi:10.1111/1468-4446.12759. ISSN 0007-1315. PMID 32347545. S2CID 216647800.
  132. [132]
    Soller, Brian (2014). "Caught in a Bad Romance: Adolescent Romantic Relationships and Mental Health". Journal of Health and Social Behavior. 55 (1): 56–72. doi:10.1177/0022146513520432. ISSN 0022-1465. PMID 24578396. S2CID 1315015.
  133. [133]
    Andrade, Stefan B. (2019). "The Temporal Rhythm of Alcohol Consumption: On the Development of Young People's Weekly Drinking Patterns". YOUNG. 27 (3): 225–244. doi:10.1177/1103308818782264. ISSN 1103-3088. S2CID 150162516.
  134. [134]
    Oris, Michel; Ritschard, Gilbert (2014), Blanchard, Philippe; Bühlmann, Felix; Gauthier, Jacques-Antoine (eds.), "Sequence Analysis and Transition to Adulthood: An Exploration of the Access to Reproduction in Nineteenth-Century East Belgium", Advances in Sequence Analysis: Theory, Method, Applications, Life Course Research and Social Policies, vol. 2, Cham: Springer International Publishing, pp. 151–167, doi:10.1007/978-3-319-04969-4_8, ISBN 978-3-319-04968-7, retrieved 2021-10-01
  135. [135]
    Zhang, Yang; Ang, Shannon (2020). "Trajectories of Union Transition in Emerging Adulthood: Socioeconomic Status and Race/Ethnicity Differences in the National Longitudinal Survey of Youth 1997 Cohort". Journal of Marriage and Family. 82 (2): 713–732. doi:10.1111/jomf.12662. hdl:2027.42/154487. ISSN 0022-2445. S2CID 213174352.
  136. [136]
    Raab, Marcel; Struffolino, Emanuela (2020). "The Heterogeneity of Partnership Trajectories to Childlessness in Germany". European Journal of Population. 36 (1): 53–70. doi:10.1007/s10680-019-09519-y. ISSN 0168-6577. PMC 7018890. PMID 32116478.
  137. [137]
    Struffolino, Emanuela; Studer, Matthias; Fasang, Anette Eva (2016). "Gender, education, and family life courses in East and West Germany: Insights from new sequence analysis techniques". Advances in Life Course Research. 29: 66–79. doi:10.1016/j.alcr.2015.12.001. hdl:10419/216743.
  138. [138]
    Wilson, Matthew Charles (2014), Blanchard, Philippe; Bühlmann, Felix; Gauthier, Jacques-Antoine (eds.), "Governance Built Step-by-Step: Analysing Sequences to Explain Democratization", Advances in Sequence Analysis: Theory, Method, Applications, Life Course Research and Social Policies, vol. 2, Cham: Springer International Publishing, pp. 213–227, doi:10.1007/978-3-319-04969-4_11, ISBN 978-3-319-04968-7, retrieved 2021-10-02
  139. [139]
    Borghetto, Enrico (2014). "Legislative processes as sequences: exploring temporal trajectories of Italian law-making by means of sequence analysis". International Review of Administrative Sciences. 80 (3): 553–576. doi:10.1177/0020852313517996. ISSN 0020-8523. S2CID 153792145.
  140. [140]
    Haas, Christina (2022-11-23), "Applying Sequence Analysis in Higher Education Research: A Life Course Perspective on Study Trajectories", in Huisman, Jeroen; Tight, Malcolm (eds.), Theory and Method in Higher Education Research, Emerald Publishing Limited, pp. 127–147, doi:10.1108/s2056-375220220000008007, ISBN 978-1-80455-385-5, retrieved 2023-08-17
  141. [141]
    Fan, Yizhou; Jovanović, Jelena; Saint, John; Jiang, Yuhang; Wang, Qiong; Gašević, Dragan (2022). "Revealing the regulation of learning strategies of MOOC retakers: A learning analytic study". Computers & Education. 178: 104404. doi:10.1016/j.compedu.2021.104404. S2CID 245085107.
  142. [142]
    Saqr, Mohammed; López-Pernas, Sonsoles; Jovanović, Jelena; Gašević, Dragan (2023). "Intense, turbulent, or wallowing in the mire: A longitudinal study of cross-course online tactics, strategies, and trajectories". The Internet and Higher Education. 57: 100902. doi:10.1016/j.iheduc.2022.100902. S2CID 255121454.
  143. [143]
    Laceulle, Odilia M.; Veenstra, René; Vollebergh, Wilma A. M.; Ormel, Johan (2019). "Sequences of maladaptation: Preadolescent self-regulation, adolescent negative social interactions, and young adult psychopathology". Development and Psychopathology. 31 (1): 279–292. doi:10.1017/S0954579417001808. ISSN 0954-5794. PMID 29229016. S2CID 11613254.
  144. [144]
    Le Meur, Nolwenn; Vigneau, Cécile; Lefort, Mathilde; Lebbah, Saïd; Jais, Jean-Philippe; Daugas, Eric; Bayat, Sahar (2019). "Categorical state sequence analysis and regression tree to identify determinants of care trajectory in chronic disease: Example of end-stage renal disease". Statistical Methods in Medical Research. 28 (6): 1731–1740. doi:10.1177/0962280218774811. ISSN 0962-2802. PMID 29742976. S2CID 13663554.
  145. [145]
    Durrant, Gabriele B.; Maslovskaya, Olga; Smith, Peter W.F. (2019). "Investigating call record data using sequence analysis to inform adaptive survey designs". International Journal of Social Research Methodology. 22 (1): 37–54. doi:10.1080/13645579.2018.1490981. ISSN 1364-5579. S2CID 149780493.
  146. [146]
    Brum-Bastos, Vanessa S.; Long, Jed A.; Demšar, Urška (2018). "Weather effects on human mobility: a study using multi-channel sequence analysis". Computers, Environment and Urban Systems. 71: 131–152. Bibcode:2018CEUS...71..131B. doi:10.1016/j.compenvurbsys.2018.05.004. hdl:10023/18993. S2CID 52074761.
  147. [147]
    Mattioli, Giulio; Anable, Jillian; Vrotsou, Katerina (2016). "Car dependent practices: Findings from a sequence pattern mining study of UK time use data". Transportation Research Part A: Policy and Practice. 89: 56–72. doi:10.1016/j.tra.2016.04.010.
  148. [148]
    Vrotsou, Katerina; Ynnerman, Anders; Cooper, Matthew (2014). "Are we what we do? Exploring group behaviour through user-defined event-sequence similarity". Information Visualization. 13 (3): 232–247. doi:10.1177/1473871613477852. ISSN 1473-8716. S2CID 7455826.
  149. [149]
    Connolly, James J. T.; Anguelovski, Isabelle (2021). "Three Histories of Greening and Whiteness in American Cities". Frontiers in Ecology and Evolution. 9. doi:10.3389/fevo.2021.621783. ISSN 2296-701X.
  150. [150]
    Pyrohova, Svitlana; Hu, Jiafei; Corcoran, Jonathan (2023). "Urban land use transitions: Examining change over 19 years using sequence analysis. The case of South-East Queensland, Australia". Environment and Planning B: Urban Analytics and City Science. 50 (9): 2579–2593. doi:10.1177/23998083231163569. ISSN 2399-8083. S2CID 257488136.
  151. [151]
    Hansmeier, Hendrik; Losacker, Sebastian (2023). "Regional Eco-Innovation Trajectories". Papers in Evolutionary Economic Geography (PEEG). 23 (13).
  152. [152]
    Mas, Jean-François; Nogueira de Vasconcelos, Rodrigo; Franca-Rocha, Washington (2019). "Analysis of High Temporal Resolution Land Use/Land Cover Trajectories". Land. 8 (2): 30. doi:10.3390/land8020030. ISSN 2073-445X.
  153. [153]
    Raab, Marcel (2022), ggseqplot: Render Sequence Plots using 'ggplot2', CRAN, retrieved 2022-07-30
  154. [154]
    Robette, Nicolas (2020-06-29), seqhandbook: Miscellaneous Tools for Sequence Analysis, CRAN, retrieved 2022-09-21
  155. [155]
    Berchtold, Andre; Guinchard, Anthony; Emery, Kevin; Taher, Kamyar (2022-09-08), seqimpute: Imputation of Missing Data in Sequence Analysis, Version 1.7, CRAN, retrieved 2022-09-21
  156. [156]
    Blanchard, Philippe; Bühlmann, Felix; Gauthier, Jacques-Antoine, eds. (2014). Advances in Sequence Analysis: Theory, Method, Applications. Life Course Research and Social Policies. Vol. 2. Cham: Springer International Publishing. doi:10.1007/978-3-319-04969-4. ISBN 978-3-319-04968-7.
  157. [157]
    Ritschard, Gilbert; Studer, Matthias, eds. (2016). Proceedings of the International Conference on Sequence Analysis and Related Methods (LaCOSA II), Lausanne, June 8-10, 2016 (PDF). Lausanne: NCCR LIVES.
  158. [158]
    Ritschard, Gilbert; Studer, Matthias, eds. (2018). Sequence Analysis and Related Approaches: Innovative Methods and Applications. Life Course Research and Social Policies. Vol. 10. Cham: Springer International Publishing. doi:10.1007/978-3-319-95420-2. ISBN 978-3-319-95419-6.
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