Predicting Successful and Unsuccessful Transitions from School to Work by Using Sequence Methods

McVicar, Duncan; Anyadike-Danes, Michael

doi:10.1111/1467-985x.00641

Cited by 133 publications

(101 citation statements)

References 18 publications

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“…For analysis purposes the trajectories were clustered. Initially optimal matching (OM) and cluster analysis was considered using methodology established by McVicar and Anyadike-Danes to cluster time-series sequences of monthly labor market activities (22). However OM approaches did not lead to coherent clusters as the terminal RTW end state was underweighted using this methodology.…”

Section: Discussionmentioning

confidence: 99%

Identifying return-to-work trajectories using sequence analysis in a cohort of workers with work-related musculoskeletal disorders

McLeod

Reiff

Maas

et al. 2018

Scand J Work Environ Health

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Using population-based data in British Columbia over one year, we constructed nine return-to-work trajectory clusters after a work-related musculoskeletal injury. Workers with back strains were most likely to have early sustained return-to-work trajectories, workers with fractures or dislocations were more likely to have sickness absence trajectories or non-return-to-work trajectories. The results can guide approaches and interventions to improve return-to-work outcomes.Affiliation:

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Section: Discussionmentioning

confidence: 99%

Identifying return-to-work trajectories using sequence analysis in a cohort of workers with work-related musculoskeletal disorders

McLeod

Reiff

Maas

et al. 2018

Scand J Work Environ Health

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“…Following years of enthusiastic evangelism from Andrew Abbott and colleagues (Abbott, 1983(Abbott, , 1984(Abbott, , 1988(Abbott, , 1990Abbott and Hrycak, 1990;Abbott, 1991a,b;Abbott and DeViney, 1992;Abbott, 1992Abbott, , 1995Abbott and Tsay, 2000;Abbott, 2000), the use of sequence analysis -in particular the optimal matching algorithm -is seeing a steady increase in application in the social sciences (inter alia, Chan, 1995;Stovel et al, 1996;Halpin and Chan, 1998;Han and Moen, 1999;Blair-Loy, 1999;Scherer, 2001;McVicar and Anyadike-Danes, 2002;Clark et al, 2003;Malo and Muñoz-Bullón, 2003;Stovel and Bolan, 2004;Anyadike-Danes and McVicar, 2005;Wilson, 2006;Levy et al, 2006;Pollock, 2007;Aasave et al, 2007). However, the use of OM is not without controversy.…”

Section: The Sociological Meaningfulness Of the Optimal Matching Algomentioning

confidence: 99%

Optimal Matching Analysis and Life-Course Data: The Importance of Duration

Halpin

2010

Sociological Methods & Research

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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, which are continuous in time. Life-history data are arguably better dealt with in terms of episodes rather than as strings of time-unit observations, and in this article, the author examines whether the OM algorithm is unsuitable for such sequences. A modified version of the algorithm is proposed, weighting OM’s elementary operations inversely with episode length. In the general case, the modified algorithm produces pairwise distances much lower than the standard algorithm, the more the sequences are composed of long spells in the same state. However, where all the sequences in a data set consist of few long spells, and there is low variability in the number of spells, the modified algorithm generates an overall pattern of distances that is not very different from standard OM.

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“…They have since been applied in the social sciences, where they have been used to analyse life trajectories, career paths, and school to work transitions (cf. Abbott and Forrest 1986;Halpin and Chan 1998;Abbott and Tsay 2000;McVicar and Anyadike-Danes 2002), and, in a small number of studies, to analyse time diaries (Lesnard 2004;2008;2010;Vrotsou, Ynnerman, and Cooper 2014).…”

Section: Optimal Matchingmentioning

confidence: 99%

Sequencing the real time of the elderly: Evidence from South Africa

Grapsa¹,

Posel²

2016

DemRes

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BACKGROUNDUnderstanding how the elderly in developing countries spend their time has received little attention. Moreover, the potential of time use data to discern variation in activity patterns has not been fully realized by methods which use a mean added time approach. OBJECTIVESTo uncover patterns of time use among the elderly (60 years and older) in South Africa by applying an innovative methodology that incorporates the timing, duration, and frequency of activities in the analysis. METHODSWe use sequence analysis, which treats the daily series of activities of each individual as a sequence, and cluster analysis, to group these sequences into common clusters of time use behaviour. We then estimate multinomial logit regressions to identify the characteristics of the elderly which predict cluster membership. RESULTSWe find that the time use behaviour of the elderly in South Africa can be divided into five distinct clusters, according to the relative importance in their day of personal care, household maintenance, work, mass media, and social or cultural activities. In comparison to men, women are overrepresented in the cluster where household work dominates, while they are underrepresented in the cluster of the elderly who engage in production work. A range of other individual and household characteristics are also important in predicting cluster membership.

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Predicting Successful and Unsuccessful Transitions from School to Work by Using Sequence Methods

Cited by 133 publications

References 18 publications

Identifying return-to-work trajectories using sequence analysis in a cohort of workers with work-related musculoskeletal disorders

Identifying return-to-work trajectories using sequence analysis in a cohort of workers with work-related musculoskeletal disorders

Optimal Matching Analysis and Life-Course Data: The Importance of Duration

Sequencing the real time of the elderly: Evidence from South Africa

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