Applications of optimal matching analysis in the social sciences are typically based on sequences of specific social statuses that model the residential, family, or occupational trajectories of individuals. Despite the broadly recognized interdependence of these statuses, few attempts have been made to systematize the ways in which optimal matching analysis should be applied multidimensionally-that is, in an approach that takes into account multiple trajectories simultaneously. Based on methods pioneered in the field of bioinformatics, this paper proposes a method of multichannel sequence analysis (MCSA) that simultaneously extends the usual optimal matching analysis (OMA) to multiple life spheres. Using data from the Swiss household panel (SHP), we examine the types of trajectories obtained using MCSA. We also consider a random data set and find that MCSA offers an alternative to the sole use of ex-post sum of distance matrices by locally aligning distinct life trajectories simultaneously. Moreover, MCSA reduces the complexity of the typologies it allows to produce, without making them less informative. It is more robust to noise in the data, and it provides more reliable alignments than two independent OMA.

/pdf/multichannel-sequence-analysis-applied-to-social-science-2myflo0wiy.pdf

Multichannel sequence analysis applied to social science data

One major methodological problem in analysis of sequence data is the determination of costs from which distances between sequences are derived. Although this problem is currently not optimally dealt with in the social sciences, it has some similarity with problems that have been solved in bioinformatics for three decades. In this article, the authors propose an optimization of substitution and deletion/insertion costs based on computational methods. The authors provide an empirical way of determining costs for cases, frequent in the social sciences, in which theory does not clearly promote one cost scheme over another. Using three distinct data sets, the authors tested the distances and cluster solutions produced by the new cost scheme in comparison with solutions based on cost schemes associated with other research strategies. The proposed method performs well compared with other cost-setting strategies, while it alleviates the justification problem of cost schemes.

How much does it cost? Optimization of costs in sequence analysis of social science data

Rejected Cultural Biases Shape Our Political Views: A Migrant Household Study and Two Large-Scale Surveys

This study reviews and compares indicators that can serve to characterize numerically the nature of state sequences. It also introduces several new indicators. Alongside basic measures such as the ...

https://journals.sagepub.com/doi/pdf/10.1177/00491241211036156

Measuring the Nature of Individual Sequences

This paper is concerned with the goodness-of-fit of induced decision trees. Namely, we explore the possibility to measure the goodness-of-fit as it is classically done in statistical modeling. We show how Chi-square statistics and especially the Log-likelihood Ratio statistic that is abundantly used in the modeling of cross tables, can be adapted for induction trees. The Log-likelihood Ratio is well suited for testing the significance of the difference between two nested trees. In addition, we derive from it pseudo R 2’s. We propose also adapted forms of the Akaike (AIC) and Bayesian (BIC) information criteria that prove useful in selecting the best compromise model between fit and complexity.

/pdf/goodness-of-fit-measures-for-induction-trees-xemqfkcn13.pdf

Goodness-of-Fit Measures for Induction Trees

SUMMARY This paper is concerned with the sampling behaviour of raw and partial measures of association between categorical variables. It summarizes the asymptotic results established for raw measures and extends them in a systematic way for the derived partial associations. The validity of the asymptotic results is then stressed by means of a simulation study. Three proportional reduction in error of prediction measures are considered for nominal variables and three concordance-discordance indices for ordinal variables.

The Behavior of Nominal and Ordinal Partial Association Measures

This paper is concerned with the sampling behaviour of raw and partial measures of association between categorical variables. It summarizes the asymptotic results established for raw measures and extends them in a systematic way for the derived partial associations. The validity of the asymptotic results is then stressed by means of a simulation study. Three proportional reduction in error of prediction measures are considered for nominal variables and three concordance-discordance indices for ordinal variables.

/pdf/the-behaviour-of-nominal-and-ordinal-partial-association-4n2uq4og6w.pdf

The behaviour of nominal and ordinal partial association measures

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The Behavior of Nominal and Ordinal Partial Association Measures

The behaviour of nominal and ordinal partial association measures