A Parsimonious Explanation of the Resilient, Undercontrolled, and Overcontrolled Personality Types

2017 ◽  
Vol 31 (6) ◽  
pp. 658-668 ◽  
Author(s):  
Tom Rosenström ◽  
Markus Jokela ◽  
Christian Kandler

Researchers applying clustering methods have found that the five most commonly studied personality traits (the ‘Big Five’) appear to form three prototypes, known as resilient, undercontrolled, and overcontrolled (RUO) personality types. The analysis has been replicated cross–nationally, and the results have been reasonably robust. However, these findings do not necessarily imply discontinuities or non–linearities in the Big Five data. We study whether the RUO types can arise from typical Big Five intercorrelations alone. We used data from a previous meta–analysis of inter–trait correlations ( N = 144 117 participants) and simulated normally distributed observations with this correlation structure. Applying three different clustering algorithms ( k–means, hierarchical agglomerative, and model based) with three–cluster solutions to the simulated data, we examined whether the known correlations alone can give rise to the RUO typology. The simulated results were compared with previous empirical findings. A simple multivariate normal distribution with the Big Five correlation structure was sufficient to generate the RUO typology in three–cluster solutions for all the three clustering methods. Contrary to the RUO typology ‘carving personality description at its joints’, linear correlations typical for correlations among Big Five traits can create RUO types even in the absence of any points of discontinuity. Copyright © 2017 European Association of Personality Psychology

2020 ◽  
Vol 34 (1) ◽  
pp. 8-28 ◽  
Author(s):  
Susanne Buecker ◽  
Marlies Maes ◽  
Jaap J. A. Denissen ◽  
Maike Luhmann

This preregistered meta–analysis ( k = 113, total n = 93 668) addressed how the Big Five dimensions of personality (extraversion, agreeableness, conscientiousness, neuroticism, and openness) are related to loneliness. Robust variance estimation accounting for the dependency of effect sizes was used to compute meta–analytic bivariate correlations between loneliness and personality. Extraversion ( r = −.370), agreeableness ( r = −.243), conscientiousness ( r = −.202), and openness ( r = −.107) were negatively related to loneliness. Neuroticism ( r = .358) was positively related to loneliness. These associations differed meaningfully in strength depending on how loneliness was assessed. Additionally, meta–analytic structural equation modelling was used to investigate the unique association between each personality trait and loneliness while controlling for the other four personality traits. All personality traits except openness remained statistically significantly associated with loneliness when controlling for the other personality traits. Our results show the importance of stable personality factors in explaining individual differences in loneliness. © 2020 European Association of Personality Psychology


2005 ◽  
Vol 19 (6) ◽  
pp. 475-499 ◽  
Author(s):  
Tatyana V. Avdeyeva ◽  
A. Timothy Church

Research on personality types was extended to a non‐Western culture, the Philippines. In two large samples of Filipino college students, cluster analyses of self‐rated trait adjectives revealed interpretable three‐cluster solutions (i.e. types) for each gender. The types differed on indigenous measures of ego resiliency and ego control and exhibited sensible configurations of Big Five traits, indigenous Filipino traits, and behavioural indicators. Most types were interpretable in terms of the concepts of ego resiliency and ego control of Block and Block (1980) and resembled types identified in other cultures. Two of three male and female types were fairly comparable and some types replicated across data sets. The results provided some support for the cross‐cultural comparability of personality types and for typological research in general. Copyright © 2005 John Wiley & Sons, Ltd.


2020 ◽  
Author(s):  
André Kerber ◽  
Marcus Roth ◽  
Philipp Herzberg

A new algorithmic approach to personality prototyping based on Big Five traits was applied to a large representative and longitudinal German dataset (N = 22,820) including behavior, personality and health correlates. We applied three different clustering techniques, latent profile analysis, the k-means method and spectral clustering algorithms. The resulting cluster centers, i.e. the personality prototypes, were evaluated using a large number of internal and external validity criteria including health, locus of control, self-esteem, impulsivity, risk-taking and wellbeing. The best-fitting prototypical personality profiles were labeled according to their Euclidean distances to averaged personality type profiles identified in a review of previous studies on personality types. This procedure yielded a five-cluster solution: resilient, overcontroller, undercontroller, reserved and vulnerable-resilient. Reliability and construct validity could be confirmed. We discuss wether personality types could comprise a bridge between personality and clinical psychology as well as between developmental psychology and resilience research.


2020 ◽  
Author(s):  
Nimrod Rappoport ◽  
Roy Safra ◽  
Ron Shamir

AbstractRecent advances in experimental biology allow creation of datasets where several genome-wide data types (called omics) are measured per sample. Integrative analysis of multi-omic datasets in general, and clustering of samples in such datasets specifically, can improve our understanding of biological processes and discover different disease subtypes. In this work we present Monet (Multi Omic clustering by Non-Exhaustive Types), which presents a unique approach to multi-omic clustering. Monet discovers modules of similar samples, such that each module is allowed to have a clustering structure for only a subset of the omics. This approach differs from most extant multi-omic clustering algorithms, which assume a common structure across all omics, and from several recent algorithms that model distinct cluster structures using Bayesian statistics. We tested Monet extensively on simulated data, on an image dataset, and on ten multi-omic cancer datasets from TCGA. Our analysis shows that Monet compares favorably with other multi-omic clustering methods. We demonstrate Monet’s biological and clinical relevance by analyzing its results for Ovarian Serous Cystadenocarcinoma. We also show that Monet is robust to missing data, can cluster genes in multi-omic dataset, and reveal modules of cell types in single-cell multi-omic data. Our work shows that Monet is a valuable tool that can provide complementary results to those provided by extant algorithms for multi-omic analysis.


PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244849
Author(s):  
André Kerber ◽  
Marcus Roth ◽  
Philipp Yorck Herzberg

A new algorithmic approach to personality prototyping based on Big Five traits was applied to a large representative and longitudinal German dataset (N = 22,820) including behavior, personality and health correlates. We applied three different clustering techniques, latent profile analysis, the k-means method and spectral clustering algorithms. The resulting cluster centers, i.e. the personality prototypes, were evaluated using a large number of internal and external validity criteria including health, locus of control, self-esteem, impulsivity, risk-taking and wellbeing. The best-fitting prototypical personality profiles were labeled according to their Euclidean distances to averaged personality type profiles identified in a review of previous studies on personality types. This procedure yielded a five-cluster solution: resilient, overcontroller, undercontroller, reserved and vulnerable-resilient. Reliability and construct validity could be confirmed. We discuss wether personality types could comprise a bridge between personality and clinical psychology as well as between developmental psychology and resilience research.


2019 ◽  
Vol 33 (2) ◽  
pp. 176-196 ◽  
Author(s):  
Johannes Stricker ◽  
Susanne Buecker ◽  
Michael Schneider ◽  
Franzis Preckel

Multidimensional perfectionism includes the dimensions perfectionistic concerns and perfectionistic strivings. Many studies have investigated the nomological network of multidimensional perfectionism by relating perfectionistic concerns and perfectionistic strivings to the Big Five personality traits. Results from these studies were largely inconsistent. In the present study, we meta–analytically integrated 672 effect sizes from 72 samples ( N = 21 573) describing relations between multidimensional perfectionism and the Big Five personality traits. Perfectionistic concerns correlated positively with Neuroticism ( r = 0.383) and negatively with Extraversion ( r = −0.198), Agreeableness ( r = −0.198), Conscientiousness ( r = −0.111), and Openness ( r = −0.087). Perfectionistic strivings correlated positively with Conscientiousness ( r = 0.368), Openness ( r = 0.121), Neuroticism ( r = 0.090), and Extraversion ( r = 0.067) and were unrelated to Agreeableness ( r = 0.002). The measures of perfectionistic concerns and perfectionistic strivings moderated most of these relations. Meta–analytic structural equation modelling allowed controlling each perfectionism dimension for the respective other. This partialling increased all correlations with the exception of the previously positive correlation between perfectionistic strivings and Neuroticism, which ceased to be significant. The findings support the distinction between perfectionistic strivings and perfectionistic concerns and demonstrate how multidimensional perfectionism is situated in the context of broader personality traits. © 2019 European Association of Personality Psychology


2018 ◽  
Vol 22 (2) ◽  
Author(s):  
Xiaohui Li ◽  
Ping Yao ◽  
Katharine Didericksen ◽  
Juyoung Jang ◽  
David Olson

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Stefan Poier

AbstractThis study among owners of photovoltaic systems investigates whether users' Big Five personality traits derived from their Facebook likes contribute to whether or not they adopt an electricity storage. It is based on the finding that the digital footprint, especially the Facebook likes, can in part predict the personality of users better than friends and family. The survey was conducted among 159 Facebook users in Germany who owned a photovoltaic system. For comparison, a control sample with data from the German Socio-Economic Panel with 425 photovoltaic owners among 7286 individuals was used. The results show that, for extraversion, agreeableness, and neuroticism, the mean scores could be sufficiently predicted. However, a positive correlation could only be detected for extraversion. The comparison of the user groups could not provide satisfying results. None of the Big Five personality traits could be used to distinguish the two user groups from each other. Although the results did not support the hypotheses, this study offers insights into the possibilities of combining data mining, personality psychology, and consumer research.


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