0000000000419707
AUTHOR
Mervi Eerola
showing 5 related works from this author
Mapping pathways to adulthood among Finnish university students: Sequences, patterns and variations of family- and work-related roles
2011
Abstract The present follow-up study examined the sequences, patterns, and variations in family- and work-related roles during the transition to adulthood among university students. Our aim was to identify typologies of transitional pathways to adulthood across their education, employment, residence, partnership/parenthood histories. The subjects were 182 first-year Finnish university students (mean age = 21) who were followed for 18 years. The Life History Calendar was used to collect data on their education, employment, residence, and partnership/parenthood histories. We also investigated the participants’ background variables (gender, age, parents’ education, school grades) and their lif…
Partnership formation and dissolution over the life course: applying sequence analysis and event history analysis in the study of recurrent events
2015
We present two types of approach to the analysis of recurrent events for discretely measured data, and show how these methods can complement each other when analysing co-residential partnership histories. Sequence analysis is a descriptive tool that gives an overall picture of the data and helps to find typical and atypical patterns in histories. Event history analysis is used to make conclusions about the effects of covariates on the timing and duration of the partnerships. As a substantive question, we studied how family background and childhood socio-emotional characteristics were related to later partnership formation and stability in a Finnish cohort born in 1959. We found that high se…
Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data
2018
Life course data often consists of multiple parallel sequences, one for each life domain of interest. Multichannel sequence analysis has been used for computing pairwise dissimilarities and finding clusters in this type of multichannel (or multidimensional) sequence data. Describing and visualizing such data is, however, often challenging. We propose an approach for compressing, interpreting, and visualizing the information within multichannel sequences by finding (1) groups of similar trajectories and (2) similar phases within trajectories belonging to the same group. For these tasks we combine multichannel sequence analysis and hidden Markov modelling. We illustrate this approach with an …
Statistical analysis of life history calendar data
2016
The life history calendar is a data-collection tool for obtaining reliable retrospective data about life events. To illustrate the analysis of such data, we compare the model-based probabilistic event history analysis and the model-free data mining method, sequence analysis. In event history analysis, we estimate instead of transition hazards the cumulative prediction probabilities of life events in the entire trajectory. In sequence analysis, we compare several dissimilarity metrics and contrast data-driven and user-defined substitution costs. As an example, we study young adults' transition to adulthood as a sequence of events in three life domains. The events define the multistate event…
Analysing Complex Life Sequence Data with Hidden Markov Modelling
2016
When analysing complex sequence data with multiple channels (dimensions) and long observation sequences, describing and visualizing the data can be a challenge. Hidden Markov models (HMMs) and their mixtures (MHMMs) offer a probabilistic model-based framework where the information in such data can be compressed into hidden states (general life stages) and clusters (general patterns in life courses). We studied two different approaches to analysing clustered life sequence data with sequence analysis (SA) and hidden Markov modelling. In the first approach we used SA clusters as fixed and estimated HMMs separately for each group. In the second approach we treated SA clusters as suggestive and …