scholarly journals HEXACO Personality and Schwartz's Personal Values: A Facet-Level Analysis

2017 ◽  
Author(s):  
Jeromy Anglim ◽  
Emily R. V. Knowles ◽  
Patrick Damien Dunlop ◽  
Andrew Marty
Keyword(s):  
Data Analysis ◽  
Individual Differences ◽  
Regression Models ◽  
Personal Values ◽  
Materials Used ◽  
Basic Personal Values ◽  
Hexaco Model ◽  
Level Analysis

This study systematically examined the correlates of Schwartz’s basic values with the broad and narrow traits of the HEXACO model of personality. A sample of 1244 adults (53% male; M age=44, SD=12) completed the 200-item HEXACO-PI-R and the Portrait Values Questionnaire measuring Schwartz’s 10 basic personal values. Regression models predicting each of the ten basic values from personality revealed mean-adjusted multiple correlations of .39 for HEXACO factors without honesty-humility, .45 for all HEXACO factors, and .53 for HEXACO facets. The facet-level multiple correlations were particularly large (greater than .60) for power, universalism, and cooperation. Results suggest that individual differences in personality and values overlap to a greater extent than implied by past literature. OSF project materials includes data, analysis scripts, and materials used in the publication of the same name. For details and licensing information, see the Wiki page below.

Individual differences in multiple cues guiding flexible resource allocation in visual short-term memory.

2020 ◽  
Author(s):  
Holly Lockhart ◽  
Blaire Dube ◽  
Kevin John MacDonald ◽  
Naseem Al-Aidroos ◽  
Stephen Emrich
Keyword(s):  
Individual Differences ◽  
Short Term Memory ◽  
Spatial Cues ◽  
Short Term ◽  
Term Memory ◽  
Visual Short Term Memory ◽  
Presentation Time ◽  
Flexible Resource ◽  
Priority Levels ◽  
Level Analysis

Although recent evidence suggests that visual short-term memory (VSTM) is a continuous resource, little is known about how flexibly this resource can be allocated. Previous studies using probabilistic cues to indicate two different levels of probe probability have found that response precision can be predicted according to a continuous allocation of resources that depends on attentional priority. The current study used a continuous report procedure and attentional prioritization via simultaneous probabilistic spatial cues to address whether participants can use up to three levels of attentional priority to allocate VSTM resources. Three experiments were performed with differing priority levels, different cues, and cue presentation time. Although group level analysis demonstrated flexible allocation, there was limited evidence that participants were using three priority levels. An individual differences approach revealed that a minority of participants were using three levels of attentional priority, demonstrating that, while possible, it is not the predominant pattern of behavior.

scholarly journals Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant

2021 ◽  
Vol 11 (4) ◽  
pp. 1776
Author(s):  
Young Seo Kim ◽  
Han Young Joo ◽  
Jae Wook Kim ◽  
So Yun Jeong ◽  
Joo Hyun Moon
Keyword(s):  
Power Generation ◽  
Data Analysis ◽  
Power Plant ◽  
Relative Humidity ◽  
Regression Model ◽  
Regression Models ◽  
Solar Power ◽  
Meteorological Variables ◽  
Solar Power Plant ◽  
Solar Power Generation

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.

scholarly journals Physician-Related Variability in the Outcomes of an Invasive Treatment for Neck and Back Pain: A Multi-Level Analysis of Data Gathered in Routine Clinical Practice

2021 ◽  
Vol 18 (8) ◽  
pp. 3855
Author(s):  
Ana Royuela Vicente ◽  
Francisco M. Kovacs ◽  
Jesús Seco-Calvo ◽  
Borja M. Fernández-Félix ◽  
Víctor Abraira ◽  
...  
Keyword(s):  
Back Pain ◽  
Regression Models ◽  
Intraclass Correlation ◽  
Spinal Pain ◽  
Routine Clinical Practice ◽  
Invasive Treatment ◽  
Logistic Regression Models ◽  
Training Standards ◽  
Multi Level ◽  
Level Analysis

Neuro-reflexotherapy (NRT) is a proven effective, invasive treatment for neck and back pain. To assess physician-related variability in results, data from post-implementation surveillance of 9023 patients treated within the Spanish National Health Service by 12 physicians were analyzed. Separate multi-level logistic regression models were developed for spinal pain (SP), referred pain (RP), and disability. The models included all patient-related variables predicting response to NRT and physician-related variables. The Intraclass Correlation Coefficient (ICC) and the Median Odds Ratio (MOR) were calculated. Adjusted MOR (95% CI) was 1.70 (1.47; 2.09) for SP, 1.60 (1.38; 1.99) for RP, and 1.65 (1.42; 2.03) for disability. Adjusted ICC (95%CI) values were 0.08 (0.05; 0.15) for SP, 0.07 (0.03; 0.14) for RP, and 0.08 (0.04; 0.14) for disability. In the sensitivity analysis, in which the 6920 patients treated during the physicians’ training period were excluded, adjusted MOR was 1.38 (1.17; 1.98) for SP, 1.37 (1.12; 2.31) for RP, and 1.25 (1.09; 1.79) for disability, while ICCs were 0.03 (0.01; 0.14) for SP, 0.03 (0.00; 0.19) for RP, and 0.02 (0.00; 0.10) for disability. In conclusion, the variability in results obtained by different NRT-certified specialists is reasonable. This suggests that current training standards are appropriate.

Personal Values Across Cultures

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Lilach Sagiv ◽  
Shalom H. Schwartz
Keyword(s):  
Personal Values ◽  
Well Being ◽  
Annual Review ◽  
Publication Date ◽  
Subjective Well Being ◽  
Value Differences ◽  
Many Instruments ◽  
Value Priorities ◽  
The Many ◽  
Basic Personal Values

Values play an outsized role in the visions, critiques, and discussions of politics, religion, education, and family life. Despite all the attention values receive in everyday discourse, their systematic study took hold in mainstream psychology only in the 1990s. This review discusses the nature of values and presents the main contemporary value theories, focusing on the theory of basic personal values. We review evidence for the content and the structure of conflict and compatibility among values found across cultures. We discuss the assumptions underlying the many instruments developed to measure values. We then consider the origins of value priorities and their stability or change over time. The remainder of the review presents the evidence for the ways personal values relate to personality traits, subjective well-being, and the implications of value differences for religiosity, prejudice, pro- and antisocial behavior, political and environmental behavior, and creativity, concluding with a discussion of mechanisms that link values to behavior. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Traits and values as predictors of the frequency of everyday behavior: Comparison between models and levels

2018 ◽  
Vol 40 (1) ◽  
pp. 133-153 ◽  
Author(s):  
Ewa Skimina ◽  
Jan Cieciuch ◽  
Włodzimierz Strus
Keyword(s):  
Personality Traits ◽  
Factor Model ◽  
Five Factor Model ◽  
Personal Values ◽  
Derived Categories ◽  
Higher Order ◽  
Behavioral Factors ◽  
Wide Range ◽  
Age Range ◽  
Hexaco Model

AbstractThe aims of this study were to compare (a) personality traits vs personal values, (b) Five-Factor Model (FFM) vs HEXACO model of personality traits, and (c) broad vs narrow personality constructs in terms of their relationship with the frequency of everyday behaviors. These relationships were analyzed at three organizational levels of self-reported behavior: (a) single behavioral acts, (b) behavioral components (empirically derived categories of similar behaviors), and (c) two higher-order factors. The study was conducted on a Polish sample (N = 532, age range 16–72). We found that (a) even the frequencies of single behavioral acts were related to various personality constructs instead of one narrow trait or value, (b) personality traits and personal values were comparable as predictors of a wide range of everyday behaviors, (c) HEXACO correlated with the frequency of behaviors slightly higher than FFM, and (d) narrow and broad personality constructs did not differ substantially as predictors of everyday behavior at the levels of acts and components, but at the level of higher-order behavioral factors, broad personality measures were better predictors than narrow ones.

scholarly journals The Effectiveness of 6M Implementation at Artebo MSME

2019 ◽  
Vol 6 (2) ◽  
pp. 249
Author(s):  
Sitti Hardiyanti Arhas ◽  
Suprianto Suprianto
Keyword(s):  
Data Analysis ◽  
Secondary Data ◽  
Primary Data ◽  
Analysis Model ◽  
Interactive Analysis ◽  
Analysis Technique ◽  
Materials Used ◽  
Main Instrument ◽  
Or Organization ◽  
A Company

Management is a tool to achieve the desired goals, with good management it will facilitate the realization of the goals of a company or organization. The effective and efficient use of elements in management owned by a business will be able to bring advantages to businesses and consumers. The management elements consist of Material, Method, Man, Machine, Money, and Market, known as 6M. This study aims to determine the effectiveness of the implementation of 6M at Artebo MSMEs. The research method used is a type of qualitative research, namely research in the form of words, sentences, schemes, and descriptions. The data sources consist of primary data and secondary data obtained from observations and interviews. Primary data comes from information, statements, and information from informants. Secondary data comes from documentation review. The main instrument in this study is the researcher himself with the aid of a mobile recorder; observation sheet; and interview sheets. The data collection techniques used were observation, interview, and documentation. The collected data is checked by triangulation, namely checking the validity of the data using something other than the data concerned for checking purposes or as a comparison. The data analysis technique used an interactive analysis model. The stages in data analysis taken in this study include data reduction; presentation of data; validation test; and verification. As for the results of the study, namely MSME Artebo has implemented the elements of 6M management effectively, this is adjusted to the conditions of the micro-business being run. The man that is owned is one person, namely the owner. Money used in production activities can be explored because the material is easy to use and affordable. The materials used mostly comes from nature, namely wood. The machine used is a simple machine because of the limited resources that can be utilized. The method used is a traditional method. And the market that is used to sell products is done in a Word of Mouth manner.

scholarly journals How to improve parameter estimates in GLM-based fMRI data analysis: cross-validated Bayesian model averaging

2016 ◽  
Author(s):  
Joram Soch ◽  
Achim Pascal Meyer ◽  
John-Dylan Haynes ◽  
Carsten Allefeld
Keyword(s):  
Data Analysis ◽  
Model Selection ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bayesian Model Selection ◽  
Fmri Data ◽  
Parameter Estimates ◽  
Fmri Data Analysis ◽  
Level Analysis

AbstractIn functional magnetic resonance imaging (fMRI), model quality of general linear models (GLMs) for first-level analysis is rarely assessed. In recent work (Soch et al., 2016: “How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection”, NeuroImage, vol. 141, pp. 469-489; DOI: 10.1016/j. neuroimage.2016.07.047), we have introduced cross-validated Bayesian model selection (cvBMS) to infer the best model for a group of subjects and use it to guide second-level analysis. While this is the optimal approach given that the same GLM has to be used for all subjects, there is a much more efficient procedure when model selection only addresses nuisance variables and regressors of interest are included in all candidate models. In this work, we propose cross-validated Bayesian model averaging (cvBMA) to improve parameter estimates for these regressors of interest by combining information from all models using their posterior probabilities. This is particularly useful as different models can lead to different conclusions regarding experimental effects and the most complex model is not necessarily the best choice. We find that cvBMS can prevent not detecting established effects and that cvBMA can be more sensitive to experimental effects than just using even the best model in each subject or the model which is best in a group of subjects.

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