scholarly journals The Antagonistic Dark Core

2019 ◽  
Author(s):  
Sujit Sekhar Maharana
Keyword(s):  
Linear Regression ◽  
Business School ◽  
Predictive Ability ◽  
Dark Triad ◽  
Common Variance ◽  
Primary Psychopathy ◽  
Relative Ability ◽  
Commonality Analysis ◽  
Suppressor Variable ◽  
Dark Core

The dark core represents the commonalities across the dark triad (Machiavellianism, Narcissism, and Psychopathy). This study contributes to the dark core literature by examining Antagonism as the prime member accounting for the dark core and tests its relative ability in explaining the dark core as compared to other such factors namely, Primary Psychopathy, Honesty-Humility and Agreeableness. A pool of 270 prospective managers (157 males, 113 females, Mage = 25.7 yrs., SDage = 3.2 years) from a leading business school of India were selected for this study. The obtained data was subjected to Hierarchical Linear Regression, Relative Weights Analysis and Commonality Analysis in a bid to flesh out unique and common variance attributed to each predictor for comparing the ability of Antagonism in explaining the dark core in relation to other variables. Antagonism was found to outperform other predictors in accounting for the dark core and explaining variance associated with Narcissism. Honesty-Humility was found to be acting as a suppressor variable, thus increasing the predictive ability of Antagonism and Primary Psychopathy in accounting for the dark core. In short, dark traited people are antagonistic at core.

Antagonism and Dark Triad (Core) : A facet level examination

2019 ◽  
Author(s):  
Sujit Sekhar Maharana
Keyword(s):  
Business School ◽  
Dark Triad ◽  
Partial Support ◽  
Unique Role ◽  
Shared Variance ◽  
Variance Structure ◽  
Dark Core

Due to the growing importance of Antagonism as the dark core, we examined facet level associations between the antagonistic facets of deceitfulness, manipulation, and grandiosity with the dark triad. A sample pool of 270 prospective managers (Mage = 25.7 yrs., SDage = 3.2 years) from a leading business school of India was selected for the study. It was hypothesised that the facets of antagonism possess a shared variance structure among them (hypothesis 1), machiavellianism will be significantly explained by deceitfulness and manipulation (hypothesis 2), psychopathy will be significantly explained by deceitfulness and manipulation (hypothesis 3), and narcissism will be significantly explained by grandiosity and manipulation (hypothesis 4). Complete support was found for all the hypotheses except hypothesis 1, which received partial support. It was concluded that while each of the antagonistic facets have their unique role to play in individually they can’t account for the dark core.

Malevolent Forces

2016 ◽  
Vol 15 (2) ◽  
pp. 191-215 ◽  
Author(s):  
John Paul Wright ◽  
Mark Alden Morgan ◽  
Pedro R. Almeida ◽  
Nora F. Almosaed ◽  
Sameera S. Moghrabi ◽  
...  
Keyword(s):  
Dark Triad ◽  
Self Control ◽  
Personality Characteristics ◽  
Negative Outcomes ◽  
Potent Predictor ◽  
Violent Delinquency ◽  
Low Self Control ◽  
Dark Core

The Dark Triad is represented by three interrelated personality characteristics thought to share a “dark core”—that is, to be associated with a range of negative outcomes. We investigate this link alongside another potent predictor of crime, low self-control. Our analyses found the Dark Triad was strongly predictive of delinquency, especially violent delinquency, where it accounted for the effects of self-control. Yet it exerted no significant effect on drug-based delinquency. However, an interaction between the Dark Triad and low self-control remained substantive and predictive across all models, where low self-control amplified the effects of the Dark Triad on delinquency.

scholarly journals P112 Prediction of free Ceftriaxone concentration in children: disease and maturation do matter

2019 ◽  
Vol 104 (6) ◽  
pp. e64.2-e64
Author(s):  
H-Y Shi ◽  
X Huang ◽  
Q Li ◽  
Wu Y-E ◽  
MW Khan ◽  
...  
Keyword(s):  
Linear Regression ◽  
Mean Absolute Error ◽  
Predictive Ability ◽  
Predictive Performance ◽  
Absolute Error ◽  
Regression Equations ◽  
Free Concentration ◽  
In Vivo Binding ◽  
Mean Prediction Error

BackgroundTo evaluate the predictive ability of the existing formula to measure free ceftriaxone levels in children, and optimize the formula by adding disease and maturation factors.MethodsFifty children receiving ceftriaxone were evaluated, and the predictive performance of the different equations were assessed by mean absolute error (MAE), mean prediction error (MPE) and linear regression of predicted vs. actual free levels.ResultsThe average free ceftriaxone concentration was 2.11 ± 9.51µg/ml. The predicted free concentration was 1.15 ± 4.39µg/ml with the in vivo binding equation, which increased to 1.58 ± 7.73µg/ml and 2.01 ± 9.53µg/ml when adjusted for age (disease adapted equation), and age and albumin (disease-maturation equation) respectively. The average MAE values were 0.48 (in vivo banding equation), 0.34 (disease adapted equation) and 0.41 (disease maturation equation). The average MPE values were -0.41 (in vivo binding equation), 0.14 (disease adapted equation) and 0.09 (disease maturation equation). The respective linear regression equations and coefficients were y=1.8647x+1.0731(R2=0.7398), y=1.1455x+0.8414(R2=0.8674), and y=0.9664x(R2=0.8641) for the in vivo binding, disease adapted and disease maturation equations respectively.ConclusionCompared to the in vivo binding equation, the disease adapted and disease maturation equations showed lower MAE and MPE values, and the latter showed the lowest MPE value. In addition, the slope of the disease maturation equation was closer to 1 compared to the other two. Therefore, the optimized disease maturation equation should be used to measure free ceftriaxone levels in children.Disclosure(s)Nothing to disclose.

Predicting Sexual Offenders’ Specialization/Versatility: The Role of Impulsivity and Moral Reasoning

Sexual Abuse ◽  
2019 ◽  
Vol 32 (8) ◽  
pp. 986-1011
Author(s):  
Mariana A. Saramago ◽  
Jorge Cardoso ◽  
Isabel Leal
Keyword(s):  
Linear Regression ◽  
Moral Reasoning ◽  
Sexual Offenders ◽  
Exploratory Study ◽  
Diversity Index ◽  
Predictive Ability ◽  
Criminal History ◽  
Regression Analyses ◽  
Sexual Offenses

The main purpose of this exploratory study was to examine the predictive ability of impulsivity and moral reasoning on offending specialization/versatility. The latter was measured using the diversity index which calculates the amount of variation within an individual’s criminal history. The sample consisted of 88 individuals convicted of sexual offenses incarcerated in a Portuguese prison. Group comparisons and multiple linear regression analyses on untransformed and corrected versions of the diversity index were conducted. Overall, the different versions of the diversity index presented disparate results. Individuals were found to be generally alike, but those convicted of rape tended to be more versatile than those who molested extrafamilial children. Moral reasoning was the strongest predictor of offending specialization/versatility, while impulsivity was mostly not statistically significant. A better understanding of these predictors’ roles on offending specialization/versatility, as it relates to recidivism, is important to tailor successful interventions.

More or less than the sum of its parts? Mapping the Dark Triad of personality onto a single Dark Core

2017 ◽  
Vol 114 ◽  
pp. 140-144 ◽  
Author(s):  
Bianca Bertl ◽  
Jakob Pietschnig ◽  
Ulrich S. Tran ◽  
Stefan Stieger ◽  
Martin Voracek
Keyword(s):  
Dark Triad ◽  
Dark Core

“I'll teach you differences”: Taxometric analysis of the Dark Triad, trait sadism, and the Dark Core of personality

2018 ◽  
Vol 126 ◽  
pp. 19-24 ◽  
Author(s):  
Ulrich S. Tran ◽  
Bianca Bertl ◽  
Michael Kossmeier ◽  
Jakob Pietschnig ◽  
Stefan Stieger ◽  
...  
Keyword(s):  
Dark Triad ◽  
Taxometric Analysis ◽  
Dark Core

Modeling vegetative development of berseem clover (Trifolium alexandrinum L.) as a function of growing degree days using linear regression and neural networks

2004 ◽  
Vol 84 (2) ◽  
pp. 511-517 ◽  
Author(s):  
W. M. Clapham ◽  
J. M. Fedders
Keyword(s):  
Neural Networks ◽  
Linear Regression ◽  
Plant Development ◽  
Predictive Ability ◽  
Growing Degree Days ◽  
Trifolium Alexandrinum ◽  
Neural Models ◽  
Degree Days ◽  
Threshold Temperatures ◽  
Berseem Clover

Accurate models of berseem clover (Trifolium alexandrinum L.) development in relation to growing degree-days (GDD) would be useful to both producers and researchers. Predictive ability of linear regression models of plant development may be limited by choice of threshold temperature and the non-linear nature of plant development. Neural networks provide a robust approach to dealing with non-linearity, and may therefore be useful for modeling plant development. In exp.1, a numerical scale of plant development was created and used to describe growth of four cultivars of berseem clover (Bigbee, Joe Burton, Saidi and Tabor) under controlled environmental conditions (constant temperature of 12, 18 or 24°C per 12-h photoperiod) for up to 18 wk of vegetative growth. Simple linear regression and neural networks were used to model plant development in relation to GDD using a range of threshold temperatures. Predictive ability of the models was compared with the results from a second controlled environment study (exp. 2). The r2 of the linear and neural models produced in exp. 1 were maximized at GDD threshold temperatures of 0 to 2°C. Results from exp. 2 indicated that the predictive ability of neural models matched or exceeded that of the linear models for all threshold temperatures evaluated. Results of the current study suggests that neural network models are relatively insensitive to base temperatures across the range tested and may therefore be preferable when a priori knowledge of temperature thresholds is not available. Key words: Berseem clover, plant development, phenology modeling, growing degree days, base temperature, neural network modeling

scholarly journals Corneal Elevation Asymmetry Vector as a Predictor of One-year-averaged Orthokeratology Lens Decentration in Low Astigmats

2021 ◽  
Author(s):  
Qiong Xu ◽  
Kai Wang ◽  
Yan Li ◽  
Jia Qu ◽  
Mingwei Zhao
Keyword(s):  
Linear Regression ◽  
Prospective Study ◽  
Predictive Ability ◽  
Practical Method ◽  
Common Type ◽  
Objective Method ◽  
One Year ◽  
The Relationship

Abstract Significance: This study establishes a novel and practical method for locating orthokeratology lens decentration. The general lens decentration over a one-year period could be partially predicted using the corneal elevation asymmetry vector.Purpose: The predictive ability of the pretreatment corneal elevation asymmetry vector for the average orthokeratology lens decentration in Chinese myopic adolescents with low astigmatism was explored.Methods: Thirty-five myopic participants (35 eyes) were enrolled in this prospective study. The follow-up time was up to one year. Various pretreatment corneal parameters were analyzed to identify potential candidates for lens decentration prediction. An accurate and objective method was developed in MATLAB to calculate the corneal asymmetry vector, which was defined as the sum of the elevation vector at the boundary between the reverse curve and the alignment curve. The program also locates a precise lens decentration vector. The relationship between pretreatment corneal parameters and lens decentration was analyzed using linear regression.Results: Lens decentration stabilized after 1 month. The averaged lens decentration vector was calculated to indicate the general decentration situation after stabilization. The magnitude of the averaged decentration varied from 0.07 mm to 1.35 mm, with 91% measuring less than 1.0 mm. Inferotemporal decentration was the most common type (66%). Among all the pretreatment parameters, only the corneal asymmetry vector significantly correlated with the averaged lens decentration in horizontal coordinates (R=0.374, p=.027). The vertical asymmetry vector was close to significance when correlated with the vertical averaged lens decentration vector (R=0.327, p=.055). The angle of the corneal elevation asymmetry vector significantly contributed to the averaged lens decentration angle (R= 0.344, p= .043).Conclusions: The association between pretreatment corneal elevation asymmetry vector and one-year-averaged orthokeratology lens decentration may assist in fitting and understanding the performance of orthokeratology lenses.

scholarly journals PENGARUH KURS VALUTA ASING DAN TINGKAT BAGI HASIL TERHADAP VOLUME DEPOSITO MUDHARABAH USD PADA PERBANKAN SYARIAH (PERIODE JANUARI 2011- MARET 2015)

2020 ◽  
Vol 3 (1) ◽  
pp. 55-68
Author(s):  
Rianti Pratiwi ◽  
Asshiddiqi Lukmana
Keyword(s):  
Regression Analysis ◽  
Exchange Rate ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Predictive Ability ◽  
Microsoft Excel ◽  
Monthly Data ◽  
Significant Negative Effect ◽  
Negative Effect ◽  
Rate Of Profit

ABSTRAK: Tujuan penelitian ini adalah untuk menganalisis pengaruh kurs USD dan tingkat bagi hasil deposito mudharabah USD terhadap volume deposito mudharabah USD pada perbankan syariah. Data yang digunakan pada penelitian ini adalah data bulanan dari Januari 2011 sampai Maret 2015. Penelitian ini menggunakan metode analisis linier regresi berganda dengan menggunakan program komputer SPSS versi 22.0 dan Microsoft Excel 2010. Hasil analisis menunjukkan bahwa secara parsial, kurs USD berpengaruh positf signifikan terhadap volume deposito mudharabah USD, sedangkan tingkat bagi hasil deposito mudharabah USD berpengaruh negatif tidak signifikan. Secara simultan variabel kurs USD dan tingkat bagi hasil deposito mudharabah USD berpengaruh signifikan terhadap volume deposito mudharabah USD. Hal ini dibuktikan dengan nilai sig-F 0,000 yang lebih kecil dari signifikansi 5%. Kemampuan prediksi dari kedua variabel tersebut terhadap volume deposito mudharabah USD adalah 88,4%. sebagaimana ditunjukkan oleh besarnya adjusted R square, sedangkan sisanya 11,6% dipengaruhi oleh faktor lain.Kata Kunci : Kurs, Tingkat Bagi Hasil Deposito Mudharabah, Volume Deposito Mudharabah dan Analisis Regresi linierABSTRACT: The purpose of this study was to analyze the influence of the USD exchange rate and the rate for the mudharabah deposit for USD toward the volume of deposit of mudharabah. The data used in this study is the monthly data from January 2011 to March 2015. This study uses linear regression analysis using SPSS version 22.0 and Microsoft Excel 2010. The analysis showed that partially, the USD exchange rate impact positf significantly to the volume of deposits mudaraba USD, while the rate for the deposits of USD mudharabah significant negative effect. Simultaneously, USD rate and deposits of USD mudharabah significantly affect the volume of deposits mudarabah USD. This is evidenced by the value of sig-F 0,000 less than the 5% significance. The predictive ability of the variables on the volume of deposits was 88.4% mudharabah USD. As indicated by the adjusted R-square, while the remaining 11.6% is influenced by other factors.Keywords: kurs, Rate of Profit of Mudharabah Deposit, volume of Deposit, and regresseion analisys

Sign in / Sign up

Export Citation Format

Share Document