Abstract
What happens if we decide to plot sequences as graphs? Can this approach increase our knowledge of the underlying structure common to the single sequences? Moreover, will this approach bring to the surface the structures, patterns, and careers still (perhaps) hidden to our eyes and knowledge?
The use of graphic visualization and the social network analysis suggested in this paper have two purposes. One is to find new ways to present results; the other is to gain a new perspective from which to observe sequences. To do this, however, we need to see sequences not as individuals moving from one state to another but as individuals who exhibit common career patterns.
This proposal starts from the intent to find a new way to observe how careers develop over time and thus to capture their dynamic evolution. To this end, we need to give physical form to sequences and their underlying generative processes: that is to say, we must convert sequences into objects—networks graphs—with which it is possible to explore how they evolve.
I thank Jacques-Antoine Gauthier and the participants in the LaCOSA Conference for helpful comments.
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- 1.
Interpretation of these graphs should always be combined with other measures that help the researcher to read the content.
- 2.
For information about the data used in this article, see Bison (2011).
- 3.
The EGP class scheme used here is the follows: (I-II) Higher and lower-grade professionals, administrators, officials and higher-grade technicians; managers in large and small industrial establishments; large proprietors; supervisors of nonmanual employees; (IIIa) Routine nonmanual employees, higher grade (administration and commerce); (IVab) Small proprietors, artisans, etc., with and without employees; (IVc) Farmers and smallholders; other self-employed workers in primary production; (IIIb + V-VI + VIIa) Routine nonmanual employees, lower grades (sales and services); lower-grade technicians; supervisors of manual workers; skilled manual workers; semi-skilled and unskilled manual workers (not in agriculture, etc.); (VIIb) Agricultural and other workers in primary production.
- 4.
Here ‘order’ means that each element in the sequence is in temporal relation to what precedes and follows it.
- 5.
To ensure that the temporal order of states does not change when collapsing the individual sequences into the network, we generate a new coding that substitutes the original code of sequences and combines each individual state recorded in sequence with its temporal position. In the example, the new coding of the first sequence will be: {01a, 02b, 03b, 04b, 05b, 06b, 07b}. Here 01, 02, …, n code the state time positions t1, t2, …, tn and the letters (a) and (b) are the identifiers of states/events. This encoding allows, during visualization and analysis, identification of the states/events according to the temporal order in which they have occurred.
- 6.
The node shapes are exactly the same as those in the previous graph. The changes in this graph are the following: (a) the observation window is monthly; (b) the size of a node is based on the proportion in the total population; and (c) the number of the label next to the node reports the order/succession of transitions (the number of event).
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Bison, I. (2014). Sequence as Network: An Attempt to Apply Network Analysis to Sequence Analysis. In: Blanchard, P., Bühlmann, F., Gauthier, JA. (eds) Advances in Sequence Analysis: Theory, Method, Applications. Life Course Research and Social Policies, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-04969-4_12
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