Does the relationship between personality and divorce change over time? A cross-country comparison of marriage cohorts

Boertien, Diederik; Mortelmans, Dimitri

doi:10.1177/0001699317709048

Cited by 13 publications

(14 citation statements)

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“…In recent stud ies, asso ci a tions between PTs and union dis so lu tion have been sim i lar in the United Kingdom, Flanders, and Germany. In par tic u lar, a low level of con sci en tious ness and a high level of open ness are signif cant risk fac tors, although the rela tion ship between open ness and dis so lu tion has weak ened as the lat ter becomes more com mon and less costly socially and eco nom ically (Boertien and Mortelmans 2018;Boertien et al 2015).…”

Section: Previous Studies On Union Dissolutionmentioning

confidence: 99%

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What Tears Couples Apart: A Machine Learning Analysis of Union Dissolution in Germany

Arpino

Moglie

2021

Demography

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This study contributes to the literature on union dissolution by adopting a machine learning (ML) approach, specifically Random Survival Forests (RSF). We used RSF to analyze data on 2,038 married or cohabiting couples who participated in the German Socio-Economic Panel Survey, and found that RSF had considerably better predictive accuracy than conventional regression models. The man's and the woman's life satisfaction and the woman's percentage of housework were the most important predictors of union dissolution; several other variables (e.g., woman's working hours, being married) also showed substantial predictive power. RSF was able to detect complex patterns of association, and some predictors examined in previous studies showed marginal or null predictive power. Finally, while we found that some personality traits were strongly predictive of union dissolution, no interactions between those traits were evident, possibly reflecting assortative mating by personality traits. From a methodological point of view, the study demonstrates the potential benefits of ML techniques for the analysis of union dissolution and for demographic research in general. Key features of ML include the ability to handle a large number of predictors, the automatic detection of nonlinearities and nonadditivities between predictors and the outcome, generally superior predictive accuracy, and robustness against multicollinearity.

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Section: Previous Studies On Union Dissolutionmentioning

confidence: 99%

“…A last impor tant var i able, which is spe cifc to Germany and included in our anal ysis, refers to the East-West divide. East Ger mans, other fac tors being equal, are more likely than West Ger mans to divorce (e.g., Boertien and Mortelmans 2018).…”

Section: Corrected Proofsmentioning

confidence: 99%

What Tears Couples Apart: A Machine Learning Analysis of Union Dissolution in Germany

Arpino

Moglie

2021

Demography

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“…Other individual level factors are also relevant. Two of the big five personality traitslow conscientiousness and high neuroticism -have been related both to divorce (Boertien and Mortelmans 2017;Solomon and Jackson 2014) and to unemployment (Egan et al 2017;Uysal and Pohlmeier 2011). Parental divorce is a consistent predictor of marital dissolution (de Graaf and Kalmijn 2006), and the often-accompanying family conflict and singleparenthood increase children's risk of unemployment in adulthood (Caspi et al 1998).…”

Section: Expectations and Previous Researchmentioning

confidence: 99%

Divorced and Unemployed: the Declining Association between Two Critical Lifecourse States in the UK, 1984-2017

Anderson¹,

Monden²,

Bukodi³

2019

Preprint

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Individuals exposed to both job loss and marital dissolution are likely to be highly disadvantaged, having experienced stresses and losses in the two primary domains of life. Moreover, recent literature finds that exposure to one event tends to increase risk of the other. However, next to nothing is known about the size or composition -or changes thereinof the divorced/separated and unemployed (DSU) subpopulation. Using large, nationally representative, repeated cross-sectional datasets extending back to 1984, we aim to fill this gap for the UK. We give a descriptive account of the prevalence and social distribution of DSU, and of the cross-sectional association between its two component states: among which groups, by education and gender, does being either divorced/separated or unemployed most strongly imply a heightened risk of also being the other, and how has this changed over time? We find stable and strong educational inequality in DSU, while the gender gap has narrowed and recently closed. The association between the two states is stronger among men; has weakened strikingly over the time period we consider, for both men and, especially, women; and is educationally stratified among men but not women. Contrary to expectations, higher-educated men in one of the two states are most likely to also be in the other. Possible explanations and further questions are discussed. In particular, we highlight the possibility that over this time period the divorced/separated have become more like the general population, rather than a negatively selected subgroup among whom unemployment is a particular risk.

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“…Studies until now indicate different intra-personal and interpersonal or relational factors associated with marital stability and instability or divorce range personality factors (Boertien & Mortelmans, 2018;Lee & Martin, 2019) and attachment factors (Diamond, Brimhall & Elliot, 2018) to individualization (Strandell, 2018), relationship factors (Finkel, 2018;Gottman, 2015) couples' communication and interaction factors (Afrasiabi & Jafarizadeh, 2015;Mohlatlole, Sithole & Shirindi, 2018;Zaheri et al, 2016), spousal similarities in health behaviors, gene expression, immune profiles and the gut (Kiecolt-Glaser, 2018), social media, (McDaniel, Drouin & Cravens, 2017) and the minding model of love (Boniwell, 2012).…”

Section: Introductionmentioning

confidence: 99%

Perceived Relational Factors in Marital (In)stability Among Married and Divorced Bosnians: Do We Understand Each Other?

Draganović

2022

epiphany

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With an increase in divorce rates worldwide, it seems that marriage loses its importance and popularity. Still, many people seek romantic partners and marry for many reasons. Research points to the beneficial effects of marital and romantic relationships. What factors make a good and successful marital and romantic relationship and why marriages and relationships fall apart remains to be answered. Since the divorce rates in Bosnia and Herzegovina increase, the purpose of this study was to explore factors in good and successful marriages and reasons for divorce. This is a qualitative study in which a survey with open-ended questions was used to explore participants' responses. With the help of convenience sampling technique, 366 participants, N= 167 married and N=199 divorced with a mean age M= 43±11 and M= 41±10 respectively were recruited. A content analysis of participants' responses shows that both a good and successful marriage and reasons for divorce are mostly related to relational interpersonal and intrapersonal factors while external, social factors play a less important role. Married and divorced participants named understanding, love, respect, trust, tolerance, communication skills, agreement and compromise as the top qualities for a successful marriage. As the leading reasons for divorce, divorced participants listed adultery or affairs and other women, alcoholism, violence and aggression, arguments and conflict, and misunderstanding.

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Does the relationship between personality and divorce change over time? A cross-country comparison of marriage cohorts

Cited by 13 publications

References 58 publications

What Tears Couples Apart: A Machine Learning Analysis of Union Dissolution in Germany

What Tears Couples Apart: A Machine Learning Analysis of Union Dissolution in Germany

Divorced and Unemployed: the Declining Association between Two Critical Lifecourse States in the UK, 1984-2017

Perceived Relational Factors in Marital (In)stability Among Married and Divorced Bosnians: Do We Understand Each Other?

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