| 1. |
- Helske, Jouni, et al.
(author)
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Can Visualization Alleviate Dichotomous Thinking? : Effects of Visual Representations on the Cliff Effect
- 2021
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In: IEEE Transactions on Visualization and Computer Graphics. - : IEEE COMPUTER SOC. - 1077-2626 .- 1941-0506. ; 27:8, s. 3397-3409
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Journal article (peer-reviewed)abstract
- Common reporting styles for statistical results in scientific articles, such as p-values and confidence intervals (CI), have been reported to be prone to dichotomous interpretations, especially with respect to the null hypothesis significance testing framework. For example when the p-value is small enough or the CIs of the mean effects of a studied drug and a placebo are not overlapping, scientists tend to claim significant differences while often disregarding the magnitudes and absolute differences in the effect sizes. This type of reasoning has been shown to be potentially harmful to science. Techniques relying on the visual estimation of the strength of evidence have been recommended to reduce such dichotomous interpretations but their effectiveness has also been challenged. We ran two experiments on researchers with expertise in statistical analysis to compare several alternative representations of confidence intervals and used Bayesian multilevel models to estimate the effects of the representation styles on differences in researchers subjective confidence in the results. We also asked the respondents opinions and preferences in representation styles. Our results suggest that adding visual information to classic CI representation can decrease the tendency towards dichotomous interpretations - measured as the cliff effect: the sudden drop in confidence around p-value 0.05 - compared with classic CI visualization and textual representation of the CI with p-values. All data and analyses are publicly available at https://github.com/helske/statvis.
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| 2. |
- Helske, Satu, et al.
(author)
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Analysing Complex Life Sequence Data with Hidden Markov Modelling
- 2016
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In: Proceedings of the International Con-ference on Sequence Analysis and Related Methods, Lausanne, June 8-10,2016, pp 209-240. - : LaCOSA II.
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Conference paper (peer-reviewed)abstract
- When analysing complex sequence data with multiple channels (dimen- sions) 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 used them as a starting point for the estimation of MHMMs. Even though the MHMM approach has advantages, we found it to be unfeasible in this type of complex setting. Instead, using separate HMMs for SA clusters was useful for finding and describing patterns in life courses.
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| 3. |
- Helske, Satu, 1985-, et al.
(author)
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Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data
- 2018
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In: Sequence Analysis and Related Approaches. - Switzerland : Springer. - 9783319954202 - 9783319954196 ; , s. 185-200
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Book chapter (peer-reviewed)abstract
- 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 empirical application to life course data but the proposed approach can be useful in various longitudinal problems.
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| 4. |
- Helske, Satu, et al.
(author)
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From Sequences to Variables: Rethinking the Relationship between Sequences and Outcomes
- 2024
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In: Sociological methodology. - : SAGE PUBLICATIONS INC. - 0081-1750 .- 1467-9531. ; 54:1, s. 27-51
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Journal article (peer-reviewed)abstract
- Sequence analysis is increasingly used in the social sciences for the holistic analysis of life-course and other longitudinal data. The usual approach is to construct sequences, calculate dissimilarities, group similar sequences with cluster analysis, and use cluster membership as a dependent or independent variable in a regression model. This approach may be problematic, as cluster memberships are assumed to be fixed known characteristics of the subjects in subsequent analyses. Furthermore, it is often more reasonable to assume that individual sequences are mixtures of multiple ideal types rather than equal members of some group. Failing to account for uncertain and mixed memberships may lead to wrong conclusions about the nature of the studied relationships. In this article, the authors bring forward and discuss the problems of the "traditional" use of sequence analysis clusters as variables and compare four approaches for creating explanatory variables from sequence dissimilarities using different types of data. The authors conduct simulation and empirical studies, demonstrating the importance of considering how sequences and outcomes are related and the need to adjust analyses accordingly. In many typical social science applications, the traditional approach is prone to result in wrong conclusions, and similarity-based approaches such as representativeness should be preferred.
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| 5. |
- Helske, Satu, 1985-, et al.
(author)
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Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R
- 2019
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In: Journal of Statistical Software. - Alexandria, VA, United States : American Statistical Association. - 1548-7660. ; 88:3, s. 1-32
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Journal article (peer-reviewed)abstract
- Sequence analysis is being more and more widely used for the analysis of social sequences and other multivariate categorical time series data. However, it is often complex to describe, visualize, and compare large sequence data, especially when there are multiple parallel sequences per subject. Hidden (latent) Markov models (HMMs) are able to detect underlying latent structures and they can be used in various longitudinal settings: to account for measurement error, to detect unobservable states, or to compress information across several types of observations. Extending to mixture hidden Markov models (MHMMs) allows clustering data into homogeneous subsets, with or without external covariates. The seqHMM package in R is designed for the efficient modeling of sequences and other categorical time series data containing one or multiple subjects with one or multiple interdependent sequences using HMMs and MHMMs. Also other restricted variants of the MHMM can be fitted, e.g., latent class models, Markov models, mixture Markov models, or even ordinary multinomial regression models with suitable parameterization of the HMM. Good graphical presentations of data and models are useful during the whole analysis process from the first glimpse at the data to model fitting and presentation of results. The package provides easy options for plotting parallel sequence data, and proposes visualizing HMMs as directed graphs.less thanbr /greater thanComment: 33 pages, 8 figures
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| 6. |
- Breen, Richard, et al.
(author)
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Educational reproduction in Europe: A descriptive account
- 2019
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In: Demographic Research. - : MAX PLANCK INST DEMOGRAPHIC RESEARCH. - 1435-9871. ; 41, s. 1373-1400
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Journal article (peer-reviewed)abstract
- BACKGROUND Conventional studies of intergenerational social reproduction are based on a retrospective design, sampling adults and linking their status to that of their parents. This approach yields conditional estimates of intergenerational relationships. Recent studies have taken a prospective approach, following a birth cohort forward to examine how it is socially reproduced. This permits the estimation of relationships of social reproduction that do not condition on the existence of at least one child. OBJECTIVE We examine whether the relationship between conditional and unconditional estimates found for the United States and Great Britain also holds for a diverse range of European countries. METHODS We examine educational reproduction among men and women born 1930-1950 in 12 countries using data from the Survey of Health, Ageing and Retirement in Europe (SHARE) and compare unconditional and conditional estimates. RESULTS We find striking similarities in the relationship between unconditional and conditional estimates throughout Europe. Among women, the difference between conditional and unconditional estimates generally increased with education. Women with more education were less likely to reproduce themselves educationally because they were less likely to marry. The educational gradient, in terms of the probability of having a child who attained a tertiary degree, was more pronounced in the South and East of Europe than in the North and West. CONCLUSIONS The gap between conditional and unconditional estimates indicates that the more common retrospective approach tends to overstate the extent of educational reproduction. CONTRIBUTION This is the first comparative study adopting a prospective approach to intergenerational social reproduction.
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| 7. |
- Eerola, Mervi, et al.
(author)
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Analysis of Life History Calendar Data
- 2018
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In: Wiley StatsRef: Statistics Reference Online. - : John Wiley & Sons. - 9781118445112 ; , s. 1-8
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Book chapter (other academic/artistic)abstract
- The life history calendar (LHC) is a data‐collection tool for obtaining reliable retrospective data on several life domains. LHC data can be analyzed either with probabilistic modeling of transitions between the life states or with sequence analysis, a data‐mining method that requires minimal simplification of the original data. The life events define the multistate model and its event‐specific hazards and the parallel life domains in multidimensional sequence analysis. These two approaches complement each other, and recently also several ways to combine them have been suggested.
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| 8. |
- Eerola, Mervi, et al.
(author)
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Statistical analysis of life history calendar data
- 2016
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In: Statistical Methods in Medical Research. - : SAGE Publications Ltd STM. - 0962-2802 .- 1477-0334. ; 25:2, s. 571-597
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Journal article (peer-reviewed)abstract
- 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 history model and the parallel life domains in multidimensional sequence analysis. The relationship between life trajectories and excess depressive symptoms in middle age is further studied by their joint prediction in the multistate model and by regressing the symptom scores on individual-specific cluster indices. The two approaches complement each other in life course analysis; sequence analysis can effectively find typical and atypical life patterns while event history analysis is needed for causal inquiries.
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| 9. |
- Helske, Satu, et al.
(author)
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Citizens' candidates? Labour market experiences and radical right-wing candidates in the 2014 Swedish municipal elections
- 2023
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In: Acta Politica. - : PALGRAVE MACMILLAN LTD. - 0001-6810 .- 1741-1416.
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Journal article (peer-reviewed)abstract
- This article uses Swedish register data to study the labour market experiences of radical right-wing candidates standing in local elections. We look at different measures of economic insecurity (labour market participation trajectories, experience of unemployment in social networks and relative growth in the number of jobs for foreign-born workers vis-a-vis natives) and examine whether they are predictors of candidates running for the Sweden Democrats, the main radical right-wing party in Sweden, as opposed to running for mainstream political parties. We find that the labour market trajectories of such candidates are markedly different from those of mainstream party candidates. Those with turbulent or out-of-labour market trajectories are much more likely to run for the Sweden Democrats, as opposed to other parties. The same is also true for candidates embedded in social networks with higher levels of unemployment, while working in a high-skilled industry markedly lowers the probability of running for the Sweden Democrats, especially for male candidates with low educational attainment. We find mixed results for the ethnic threat hypothesis.
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| 10. |
- Helske, Satu, et al.
(author)
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Partnership formation and dissolution over the life course : applying sequence analysis and event history analysis in the study of recurrent events
- 2015
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In: Longitudinal and Life Course Studies. - London, United Kingdom : Society for Longitudinal and Life Course Studies. - 1757-9597. ; 6:1, s. 1-25
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Journal article (peer-reviewed)abstract
- 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 self-control of emotions at age 8 was related to a lower risk of partnership dissolution and for women a lower probability of repartnering. Child-centred parenting practices during childhood were related to a lower risk of dissolution for women. Socially active boys were faster at forming partnerships as men.
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