From rvosylis at live.com Wed Jan 28 17:27:21 2015 From: rvosylis at live.com (Rimantas Vosylis) Date: Wed, 28 Jan 2015 18:27:21 +0200 Subject: [Traminer-users] combining short sequences with long sequences Message-ID: Dear Traminer users and experts, I am using Traminer for my PhD data analyses, but I am stuck with some issues that I cannot find answer to. Hopefully You will be able to help me with this. I am interested in transitions to adulthood. I have two groups one is called 30-year-olds and another one - 25-year-olds, as participants of these groups are very close to those ages. I have gathered data with Life History Calendar on various life statuses in areas of parenthood, partner, education, work and living situation. From this data I have created two sequences for each participant. There are now two sequences, one for work-education transitions, another for family transitions. Apart of LHC data I have also collected various data from these participants on psychosocial functioning. My original idea was to first find the typology of transitions to adulthood by using only 30-year-olds that have sequences of about 24 objects (1 object represents some life situation status in 6-months period at some point after finishing school; 24 statuses represent change in these statuses during 12 years after finishing school). Then I would find some representative sequences (ideal types) in each cluster and then I would somehow assign the 25-year-olds (about 16 objects per sequence) based on similarity of their sequence to the ideal type sequences, that were found in older group. This way I would have participants of two age groups assigned to the same transitional typology. After that I would compare how all these groups differ on psychosocial indicators (e.g. identity issues). So the main issue for me is how can I assign the 25-year-olds that have shorter sequences to the clusters that were found based on 30-year-old group analyses. I came up with several strategies, but I am not sure which on is better, or maybe there is something else I can do but I don't know. 1. The first strategy is that I simply run optimal matching calculations for the full dataset (including the ones that have long sequences and shorter ones) and those that have shorter ones' are already assigned to some cluster. When I specify missing values in right end cells as void, it does seem to work ok. Q1. My first question to You is: does this seem like a valid strategy to assign 25-year-olds to the clusters that are actually created using also 30-year-olds? 2. The second strategy is that I first analyze only 30-year-olds, then I extract ideal types representing each cluster, then I include these ideal types into dataset of only 25-year-olds and I rerun Optimal matching analysis. Then based on the shortest distance from each ideal type sequence to each participants' sequence I assign them to those clusters. Something similar was discussed by Martin, P., Schoon, I., Ross, A., Beyond Transitions: Applying Optimal Matching to Life Course Research Q2. Does this seem like a more valid strategy than the first one? Q3. Perhaps You could provide another option on how to do such assigning? Q4. Could anyone please specify on how do I actually find the "ideal types"? Are they the central sequences in the cluster? With smallest average distance? I look everywhere but I couldn't really find any "possible to understand" answer :( I would really appreciate any help on any of these questions. Sincerely, Rimantas Vosylis PhD student, lecturer Insitute of Psychology Faculty of Social Technologies Mykolas Romeris University e-mail: rimantasv at mruni.eu e-mail2: rvosylis at yahoo.co.uk -------------- next part -------------- An HTML attachment was scrubbed... URL: