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Review your selected methods side by side; rows that differ are highlighted.
| Optimal Matching Analysis× | Relational Event Model× | |
|---|---|---|
| Field | Sociology | Sociology |
| Family≠ | Process / pipeline | Regression model |
| Year of origin≠ | 1970 (algorithm); 1980s (sociology) | 2008 |
| Originator≠ | Needleman & Wunsch (algorithm); Andrew Abbott (sociological use) | Carter T. Butts |
| Type≠ | Edit-distance dissimilarity between categorical sequences | Event-history model for time-stamped relational events |
| Seminal source≠ | Abbott, A., & Tsay, A. (2000). Sequence analysis and optimal matching methods in sociology: review and prospect. Sociological Methods & Research, 29(1), 3–33. DOI ↗ | Butts, C. T. (2008). A relational event framework for social action. Sociological Methodology, 38(1), 155–200. DOI ↗ |
| Aliases | optimal matching, OMA, edit-distance sequence comparison, Levenshtein sequence distance | REM, relational event framework, dynamic network event model, event-history network model |
| Related≠ | 5 | 4 |
| Summary≠ | Optimal matching analysis measures how dissimilar two categorical sequences are by computing the minimum total cost of editing one sequence into the other through substitution and insertion/deletion operations. Borrowed from computer science and molecular biology and introduced to sociology by Andrew Abbott, it supplies the pairwise distances that underpin sequence analysis of careers, family histories, and other life-course trajectories. | The relational event model (REM), introduced by Carter Butts in 2008, analyzes streams of time-stamped interactions — emails, radio calls, messages, citations — as a continuous-time event-history process. Rather than treating a network as a static set of ties, it models the instantaneous rate at which any sender directs an action at any receiver as a function of the history of past events, letting researchers test how prior interaction shapes future interaction. |
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