SADI: Sequence Analysis Tools for Stata

  title={SADI: Sequence Analysis Tools for Stata},
  author={Brendan Halpin},
  journal={The Stata Journal},
  pages={546 - 572}
  • B. Halpin
  • Published 1 September 2017
  • Computer Science
  • The Stata Journal
The SADI package provides tools for sequence analysis, which focuses on the similarity and dissimilarity between categorical time series such as life-course trajectories. SADI‘s main components are tools to calculate intersequence distances using several different algorithms, including the optimal matching algorithm, but it also includes utilities to graph, summarize, and manage sequence data. It provides similar functionality to the R package TraMineR and the Stata package SQ but is… Expand

Figures and Topics from this paper

Using sequence analysis to visualize and validate model transitions
The conclusion is that SA is a useful tool also in a microsimulation context, in visualizing and validating simulated model transitions when statistically more sophisticated mixture modeling are not applicable. Expand
Investigating call record data using sequence analysis to inform adaptive survey designs
ABSTRACT Researchers have become increasingly interested in better understanding the survey data collection process in interviewer-administered surveys. However, tools for analysing paradataExpand
Multiple Imputation for Categorical Time Series
The mict package provides a method for multiple imputation of categorical time-series data (such as life course or employment status histories) that preserves longitudinal consistency, using aExpand
Longitudinal methods for life course research : a comparison of sequence analysis, latent class growth models, and multi-state event history models for studying partnership transitions
This paper qualitatively compares and contrasts three methods that are useful for life course researchers; the more widely used sequence analysis, and the promising but less often applied latentExpand
Going the distance in vocational behavior research: Introducing three extensions for optimal matching analysis based on distances between career sequences
Abstract Optimal matching analysis is the most commonly used method to analyze career sequences. It enables researchers to calculate the distance (i.e. the dissimilarity) between individuals'Expand
University of Limerick Department of Sociology Working Paper Series Working Paper WP 2015-02 September 2015
The MICT package provides a method for multiple imputation for categorical time-series data such as lifecourse or employment-status histories that preserves longitudinal consistency, using aExpand
Partnership formation and dissolution over the life course: applying sequence analysis and event history analysis in the study of recurrent events
We present two types of approach to the analysis of recurrent events for discretely measured data, and show how these methods can complement each other when analysing co-residential partnershipExpand
Agreement of Self-Reported and Administrative Data on Employment Histories in a German Cohort Study: A Sequence Analysis
It is likely that missing consent and failed record linkage lead to sample differences; yet, both strategies provide comparable and reliable life course data. Expand
Healthcare access: A sequence-sensitive approach
It is widely accepted that healthcare-seeking behaviour is neither limited to nor terminated by access to one single healthcare provider. Yet the sequential conceptualisation of healthcare-seekingExpand
Career pathways for temporary workers: exploring heterogeneous mobility dynamics with sequence analysis.
A typology of trajectories is derived and age, gender, and type of temporary work stand out as important factors shaping subsequent mobility patterns and their relative precariousness in relation to employment stability and wage and earnings levels and growth. Expand


Sequence Analysis with Stata
Stata becomes the first statistical package to offer a complete set of tools for sequence analysis and introduce SQ-Ados, a bundle of Stata programs implementing the proposed strategy. Expand
Discrepancy Analysis of State Sequences
In this article, the authors define a methodological framework for analyzing the relationship between state sequences and covariates. Inspired by the principles of analysis of variance, this approachExpand
Optimal Matching Analysis and Life-Course Data: The Importance of Duration
The optimal matching (OM) algorithm is widely used for sequence analysis in sociology. It has a natural interpretation for discrete-time sequences but is also widely used for life-history data, whichExpand
A rapid method for the comparison of cluster analyses
Cluster analysis has become a very popular tool for the exploration of high dimensional data. Dozens of algorithms have been proposed, each with its own merits and shortcomings. It is not known toExpand
Three Narratives of Sequence Analysis
How do we relate the distance between two sequences, as given by an algorithm such as optimal matching, to sociologically meaningful notions of similarity and dissimilarity? This has beenExpand
What matters in differences between life trajectories: a comparative review of sequence dissimilarity measures
The study shows that there is no universally optimal distance index, and that the choice of a measure depends on which aspect the authors want to focus on, and introduces novel ways of measuring dissimilarities that overcome some flaws in existing measures. Expand
Beyond Transitions: Applying Optimal Matching Analysis to Life Course Research
An analytic strategy is introduced that allows assessing the classification’s internal validity and produced a classification with better fit than straightforward CA, suggesting passages into adulthood have become more diverse since the 1970s. Expand
Optimal Matching Methods for Historical Sequences
common script is standard historical and sociological fare. In the passage from which this quote is drawn, Rude describes a script proceeding from general grievances to triggering events and on to aExpand
Class careers as sequences : An optimal matching analysis of work-life histories
The authors apply optimal matching techniques to class careers from age 15 to age 35 for two moderately large samples, as a means of exploring the utility of this sequence-oriented approach for theExpand
Information theoretic measures for clusterings comparison: is a correction for chance necessary?
This paper derives the analytical formula for the expected mutual information value between a pair of clusterings, and proposes the adjusted version for several popular information theoretic based measures. Expand