2017
DOI: 10.1177/1536867x1701700302
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Abstract: 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 substantially faster than … Show more

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Cited by 42 publications
(21 citation statements)
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References 23 publications
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“…The similarity between two sequences is systematically determined by calculating the total “costs” of turning one sequence into another (Brzinsky-Fay and Kohler 2010). We use transition rates (probability) between two states as the transformation (substitution) costs (Halpin 2014), yielding a dissimilarity matrix for every two pairs of sequences in the data. In the third step, we reduce the large number of sequences into a finite number of substantively distinct clusters, using the dissimilarity matrix obtained in the second step.…”
Section: Methodsmentioning
confidence: 99%
“…The similarity between two sequences is systematically determined by calculating the total “costs” of turning one sequence into another (Brzinsky-Fay and Kohler 2010). We use transition rates (probability) between two states as the transformation (substitution) costs (Halpin 2014), yielding a dissimilarity matrix for every two pairs of sequences in the data. In the third step, we reduce the large number of sequences into a finite number of substantively distinct clusters, using the dissimilarity matrix obtained in the second step.…”
Section: Methodsmentioning
confidence: 99%
“…Comparing each sequence to all other sequences results in a matrix that quantifies the distances for each pair of individual sequences in the sample and that can then be used to group sequences into clusters based on cluster analyses ('Ward's linkage'). To determine the most appropriate number of clusters, for both men and women, we compare solutions between 4 and 10 clusters, including commonly used quality measures: Calinski-Harabasz and Duda-Hart stopping rules (Duda et al, 2012;Halpin, 2017). We also look at the resulting cluster sizes and evaluate each cluster solution in terms of its content validity, and whether a higher cluster solution adds another cluster of interest.…”
Section: Analytical Strategymentioning
confidence: 99%
“…Importantly, to address potential reverse causality (poor health leading to particular trajectories), all regression models exclude participants who reported that they were diagnosed with depression before the age of 30 (8 men and 18 women). All calculations and figures are based on Stata 13 with the Sadi extension for sequence analysis (Halpin, 2017).…”
Section: Analytical Strategymentioning
confidence: 99%
“…Finally, we used the clusters as the categorical dependent variable in a multinomial logistic regression to understand how certain individual characteristics influence the probability of separated individuals to belong to the identified clusters. The sequence analysis, OM, and cluster analysis were performed using the SADI package in STATA (Halpin 2017), which relies on the SQ package (Brzinsky-Fay, Kohler, and Luniak 2006) to calculate the descriptive statistics and prepare graphs.…”
Section: Methodsmentioning
confidence: 99%