scholarly journals A facet atlas: Visualizing networks that describe the blends, cores, and peripheries of personality structure

2020 ◽  
Author(s):  
Ted Schwaba ◽  
Mijke Rhemtulla ◽  
Christopher James Hopwood ◽  
Wiebke Bleidorn
Keyword(s):  
Big Five ◽  
Network Structure ◽  
Hierarchical Models ◽  
Simple Structure ◽  
Community Sample ◽  
Network Models ◽  
Personality Structure ◽  
The Core ◽  
Personality Inventories ◽  
Final Manuscript

FINAL MANUSCRIPT IS NOW PUBLICLY AVAILABLE AT https://doi.org/10.1371/journal.pone.0236893 We created a facet atlas that maps the interrelations between facet scales from 13 hierarchical personality inventories to provide a practically useful, transtheoretical description of lower-level personality traits. We generated the atlas by estimating a series of network models that visualize the correlations among 268 facet scales administered to the Eugene-Springfield Community Sample (N=1,134). As expected, most facets contained a blend of content from multiple Big Five domains and were part of multiple Big Five networks. We identified core and peripheral facets for each Big Five domain. Results from this study resolve some inconsistencies in facet placement across instruments and highlights the complexity of personality structure relative to the constraints of traditional hierarchical models that impose simple structure. The facet atlas (also available as an online point-and-click app) provides a guide for researchers who wish to measure a domain with a limited set of facets as well as information about the core and periphery of each personality domain. To illustrate the value of a facet atlas in applied and theoretical settings, we examined the network structure of scales measuring impulsivity and tested structural hypotheses from the Big Five Aspect Scales inventory.

Niche diversity influences psychometric properties of a brief personality scale – evidence from representative samples during the COVID-19 pandemic

2021 ◽  
Author(s):  
Ronald Fischer ◽  
Johannes Alfons Karl
Keyword(s):  
Big Five ◽  
Personality Structure ◽  
Death Rates ◽  
Personality Inventories ◽  
National Wealth ◽  
Niche Diversity ◽  
Per Capita ◽  
Survey Responses ◽  
Robust Measures ◽  
Representative Samples

Recent studies using more diverse samples have questioned the applicability of the Big Five. It needs to be shown how robust measures of the big five are and whether any deviations are systematic or random. We present validity information on a ten-item personality measure applied to population samples in 16 nations during the first wave of the COVID-19 pandemic (N=35,052). Overall, we found poor replicability and low reliability of the five-factor structure of personality. We then test whether variation is systematically related to ecological, economic or stress-related variables. Personality structure replicability measured via averaged Tucker’s Φ values was correlated with niche diversity data (Human development index, rate of urbanization, diversity of export goods) and national wealth, but not reliably related with COVID-19 (gross domestic product per capita). and death rates per million citizens during the study period. These patterns overall suggest that a) personality structure in brief measures need to be carefully tested prior to any substantive interpretations of the personality data and b) that systematic socioecological factors have an impact on survey responses to personality inventories.

Social Desirability in Personality Inventories

2014 ◽  
Vol 35 (3) ◽  
pp. 144-157 ◽  
Author(s):  
Martin Bäckström ◽  
Fredrik Björklund
Keyword(s):  
Social Desirability ◽  
Big Five ◽  
Work Performance ◽  
Factor Model ◽  
Five Factor Model ◽  
Peer Ratings ◽  
Personality Inventories ◽  
Model Study ◽  
The Difference ◽  
New Variables

The difference between evaluatively loaded and evaluatively neutralized five-factor inventory items was used to create new variables, one for each factor in the five-factor model. Study 1 showed that these variables can be represented in terms of a general evaluative factor which is related to social desirability measures and indicated that the factor may equally well be represented as separate from the Big Five as superordinate to them. Study 2 revealed an evaluative factor in self-ratings and peer ratings of the Big Five, but the evaluative factor in self-reports did not correlate with such a factor in ratings by peers. In Study 3 the evaluative factor contributed above the Big Five in predicting work performance, indicating a substance component. The results are discussed in relation to measurement issues and self-serving biases.

Assessing Personality With Multi-Descriptor Items

2019 ◽  
Vol 35 (1) ◽  
pp. 117-125
Author(s):  
Johannes Schult ◽  
Rebecca Schneider ◽  
Jörn R. Sparfeldt
Keyword(s):  
University Students ◽  
Construct Validity ◽  
Big Five ◽  
Reliability And Validity ◽  
Personality Inventory ◽  
The Other ◽  
Short Version ◽  
Big Five Inventory ◽  
Personality Inventories

Abstract. The need for efficient personality inventories has led to the wide use of short instruments. The corresponding items often contain multiple, potentially conflicting descriptors within one item. In Study 1 ( N = 198 university students), the reliability and validity of the TIPI (Ten-Item Personality Inventory) was compared with the reliability and validity of a modified TIPI based on items that rephrased each two-descriptor item into two single-descriptor items. In Study 2 ( N = 268 university students), we administered the BFI-10 (Big Five Inventory short version) and a similarly modified version of the BFI-10 without two-descriptor items. In both studies, reliability and construct validity values occasionally improved for separated multi-descriptor items. The inventories with multi-descriptor items showed shortcomings in some factors of the TIPI and the BFI-10. However, the other scales worked comparably well in the original and modified inventories. The limitations of short personality inventories with multi-descriptor items are discussed.

Comparison of categorical and dimensional approaches to the diagnosis of personality disorders

2020 ◽  
pp. 15-25
Author(s):  
T. G. Gadisov ◽  
A. A. Tkachenko
Keyword(s):  
Comparative Study ◽  
Personality Disorders ◽  
Big Five ◽  
Factor Model ◽  
Five Factor Model ◽  
Personality Structure ◽  
Categorical Approach ◽  
Personal Traits ◽  
Clinical Method ◽  
Neo Five Factor Inventory

Summary. Objective: A comparative study of the personality structure from the perspective the Five-factor personality model (“Big Five”) in mentally healthy and in people with personality disorders depending on the leading radical determined by the clinical method.Materials and methods: a comparative study of personality structures in the mentally healthy (13 people) and in individuals with personality disorders (47 people) was carried out. To assess the personality structure, the NEO-Five Factor Inventory questionnaire was used. Persons with personality disorders were divided into groups in accordance with the leading radical: 24 — with emotionally unstable; 13 — with a histrionic; 6 — with schizoid; 4 — with paranoid radicals.Results: There were no differences in the values of the domains of the Five-Factor personality model between a group of individuals with personality disorders and the norm. The features of domain indicators of the Five-factor personality model were revealed in individuals with personality disorder depending on theradical.Conclusion: The NEO-Five Factor Inventory questionnaire, like most other tools from the perspective of the Five-Factor Model, is not suitable for assessing a person in terms of assigning it to variants of a mental disorder. When comparing the categorical and dimensional approaches to assessing the structure of personality disorders, it was found that the obligate personality traits identified using the categorical approach are fully reflected in the «Big Five» in individuals with a leading schizoid radical. The relations of obligate personal traits with the domains of the Five-factor model of personality in individuals with other (paranoid, histrionic,and emotionally unstable) radicals are less clear.

Weak-Tie Confidants

2017 ◽  
Author(s):  
Mario Luis Small
Keyword(s):  
Social Interaction ◽  
Graduate Students ◽  
Network Structure ◽  
Relative Importance ◽  
The Core ◽  
Structure Versus

This chapter examines the “core discussion networks” of graduate students in three departments and shows that, contrary to traditional expectations, many of the ties appear to be weak rather than strong. It considers how the students relate to those they have considered their confidants after six months, and more specifically whether they would as a whole report the same confidants. Three perspectives on the relative importance of network structure versus social interaction are discussed based on the students’ different experiences: the students will keep most confidants, they will drop many of their confidants, or they will drop many confidants but quickly replace them. In general, the students replaced their confidants often.

Psychological networks in clinical populations: investigating the consequences of Berkson's bias

2019 ◽  
pp. 1-9 ◽  
Author(s):  
Jill de Ron ◽  
Eiko I. Fried ◽  
Sacha Epskamp
Keyword(s):  
Network Structure ◽  
Binary Data ◽  
Ordinal Data ◽  
Graphical Model ◽  
Rating Scale ◽  
Network Models ◽  
Simulation Studies ◽  
Potential Threat ◽  
Sum Score ◽  
Research Populations

Abstract Background In clinical research, populations are often selected on the sum-score of diagnostic criteria such as symptoms. Estimating statistical models where a subset of the data is selected based on a function of the analyzed variables introduces Berkson's bias, which presents a potential threat to the validity of findings in the clinical literature. The aim of the present paper is to investigate the effect of Berkson's bias on the performance of the two most commonly used psychological network models: the Gaussian Graphical Model (GGM) for continuous and ordinal data, and the Ising Model for binary data. Methods In two simulation studies, we test how well the two models recover a true network structure when estimation is based on a subset of the data typically seen in clinical studies. The network is based on a dataset of 2807 patients diagnosed with major depression, and nodes in the network are items from the Hamilton Rating Scale for Depression (HRSD). The simulation studies test different scenarios by varying (1) sample size and (2) the cut-off value of the sum-score which governs the selection of participants. Results The results of both studies indicate that higher cut-off values are associated with worse recovery of the network structure. As expected from the Berkson's bias literature, selection reduced recovery rates by inducing negative connections between the items. Conclusion Our findings provide evidence that Berkson's bias is a considerable and underappreciated problem in the clinical network literature. Furthermore, we discuss potential solutions to circumvent Berkson's bias and their pitfalls.

scholarly journals Modelling community structure and temporal spreading on complex networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Vesa Kuikka
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Structure ◽  
Network Connectivity ◽  
Network Models ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

Can Network Models Represent Personality Structure and Processes Better than Trait Models Do?

2012 ◽  
Vol 26 (4) ◽  
pp. 444-445 ◽  
Author(s):  
Tobias Rothmund ◽  
Anna Baumert ◽  
Manfred Schmitt
Keyword(s):  
Network Model ◽  
Explanatory Power ◽  
Network Models ◽  
Personality Structure ◽  
Psychological Processes ◽  
Target Article ◽  
Flexible Framework ◽  
Trait Model ◽  
Better Than

We argue that replacing the trait model with the network model proposed in the target article would be immature for three reasons. (i) If properly specified and grounded in substantive theories, the classic state–trait model provides a flexible framework for the description and explanation of person × situation transactions. (ii) Without additional substantive theories, the network model cannot guide the identification of personality components. (iii) Without assumptions about psychological processes that account for causal links among personality components, the concept of equilibrium has merely descriptive value and lacks explanatory power. Copyright © 2012 John Wiley & Sons, Ltd.

scholarly journals Mutualistic strategies minimize coextinction in plant–disperser networks

2017 ◽  
Vol 284 (1854) ◽  
pp. 20162302 ◽  
Author(s):  
Evan C. Fricke ◽  
Joshua J. Tewksbury ◽  
Elizabeth M. Wandrag ◽  
Haldre S. Rogers
Keyword(s):  
Network Structure ◽  
Field Experiments ◽  
Empirical Relationship ◽  
Network Models ◽  
Mutualistic Interactions ◽  
Simulated Network ◽  
Direct And Indirect Impacts ◽  
Indirect Impacts ◽  
The Stability ◽  
Seed Dispersal Mutualism

The global decline of mutualists such as pollinators and seed dispersers may cause negative direct and indirect impacts on biodiversity. Mutualistic network models used to understand the stability of mutualistic systems indicate that species with low partner diversity are most vulnerable to coextinction following mutualism disruption. However, existing models have not considered how species vary in their dependence on mutualistic interactions for reproduction or survival, overlooking the potential influence of this variation on species' coextinction vulnerability and on network stability. Using global databases and field experiments focused on the seed dispersal mutualism, we found that plants and animals that depend heavily on mutualistic interactions have higher partner diversity. Under simulated network disruption, this empirical relationship strongly reduced coextinction because the species most likely to lose mutualists depend least on their mutualists. The pattern also reduced the importance of network structure for stability; nested network structure had little effect on coextinction after simulations incorporated the empirically derived relationship between partner diversity and mutualistic dependence. Our results highlight a previously unknown source of stability in mutualistic networks and suggest that differences among species in their mutualistic strategy, rather than network structure, primarily accounts for stability in mutualistic communities.

scholarly journals Prediction Power of Logistic Regression (LR) and Multi-Layer Perceptron (MLP) Models in Exploring Driving Forces of Urban Expansion to Be Sustainable in Estonia

2021 ◽  
Vol 14 (1) ◽  
pp. 160
Author(s):  
Najmeh Mozaffaree Pour ◽  
Tõnu Oja
Keyword(s):  
Land Cover ◽  
Driving Forces ◽  
Urban Expansion ◽  
Network Models ◽  
Influential Factors ◽  
Residential Areas ◽  
Neural Network Models ◽  
Independent Variables ◽  
The Core ◽  
Mlp Model

Estonia mainly experienced urban expansion after regaining independence in 1991. Employing the CORINE Land Cover dataset to analyze the dynamic changes in land use/land cover (LULC) in Estonia over 28 years revealed that urban land increased by 33.96% in Harju County and by 19.50% in Tartu County. Therefore, after three decades of LULC changes, the large number of shifts from agricultural and forest land to urban ones in an unplanned manner have become of great concern. To this end, understanding how LULC change contributes to urban expansion will provide helpful information for policy-making in LULC and help make better decisions for future transitions in urban expansion orientation and plan for more sustainable cities. Many different factors govern urban expansion; however, physical and proximity factors play a significant role in explaining the spatial complexity of this phenomenon in Estonia. In this research, it was claimed that urban expansion was affected by the 12 proximity driving forces. In this regard, we applied LR and MLP neural network models to investigate the prediction power of these models and find the influential factors driving urban expansion in two Estonian counties. Using LR determined that the independent variables “distance from main roads (X7)”, “distance from the core of main cities of Tallinn and Tartu land (X2)”, and “distance from water land (X11)” had a higher negative correlation with urban expansion in both counties. Indeed, this investigation requires thinking towards constructing a balance between urban expansion and its driving forces in the long term in the way of sustainability. Using the MLP model determined that the “distance from existing residential areas (X10)” in Harju County and the “distance from the core of Tartu (X2)” in Tartu County were the most influential driving forces. The LR model showed the prediction power of these variables to be 37% for Harju County and 45% for Tartu County. In comparison, the MLP model predicted nearly 80% of variability by independent variables for Harju County and approximately 50% for Tartu County, expressing the greater power of independent variables. Therefore, applying these two models helped us better understand the causative nature of urban expansion in Harju County and Tartu County in Estonia, which requires more spatial planning regulation to ensure sustainability.

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