Towards A Pan–Cultural Personality Structure: Input from 11 Psycholexical Studies

2014 ◽  
Vol 28 (5) ◽  
pp. 497-510 ◽  
Author(s):  
Boele De Raad ◽  
Dick P. H. Barelds ◽  
Marieke E. Timmerman ◽  
Kim De Roover ◽  
Boris Mlačić ◽  
...  
Keyword(s):  
Factor Model ◽  
Principal Component ◽  
Component Analysis ◽  
Personality Psychology ◽  
Component Solution ◽  
Simultaneous Component Analysis ◽  
Kernel Trait ◽  
The Common ◽  
The Individual ◽  
Taxonomic Studies

The purpose of the present study is to find the common kernel of different trait taxonomic studies and find out how the individual structures relate to this common kernel. Trait terms from 11 psycholexically based taxonomies were all translated into English. On the basis of the commonalities in English, the 11 matrices were merged into a joint matrix with 7104 subjects and 1993 trait terms. Untranslatable terms produced large areas with missing data. To arrive at the kernel structure of the joint matrix, a simultaneous component analysis was applied. In addition, the kernel structures were compared with the individual taxonomy trait structures, obtained via principal component analysis. The findings provide evidence of a structure consisting of three components to stand out as the core of the taxonomies included in this study; those components were named dynamism, affiliation, and order. Moreover, the relations between these three kernel components and those of a six–component solution (completing the six–factor model) are provided. Copyright © 2014 European Association of Personality Psychology

scholarly journals Characterization of Basil Volatile Fraction and Study of Its Agronomic Variation by ASCA

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3842
Author(s):  
Alessandro D’Alessandro ◽  
Daniele Ballestrieri ◽  
Lorenzo Strani ◽  
Marina Cocchi ◽  
Caterina Durante
Keyword(s):  
Food Industry ◽  
Principal Component ◽  
Component Analysis ◽  
Gas Chromatography Mass Spectrometry ◽  
Volatile Fraction ◽  
Combined Effects ◽  
A Posteriori ◽  
Aroma Profile ◽  
Simultaneous Component Analysis

Basil is a plant known worldwide for its culinary and health attributes. It counts more than a hundred and fifty species and many more chemo-types due to its easy cross-breeds. Each species and each chemo-type have a typical aroma pattern and selecting the proper one is crucial for the food industry. Twelve basil varieties have been studied over three years (2018–2020), as have four different cuts. To characterize the aroma profile, nine typical basil flavour molecules have been selected using a gas chromatography–mass spectrometry coupled with an olfactometer (GC–MS/O). The concentrations of the nine selected molecules were measured by an ultra-fast CG e-nose and Principal Component Analysis (PCA) was applied to detect possible differences among the samples. The PCA results highlighted differences between harvesting years, mainly for 2018, whereas no observable clusters were found concerning varieties and cuts, probably due to the combined effects of the investigated factors. For this reason, the ANOVA Simultaneous Component Analysis (ASCA) methodology was applied on a balanced a posteriori designed dataset. All the considered factors and interactions were statistically significant (p < 0.05) in explaining differences between the basil aroma profiles, with more relevant effects of variety and year.

Bankruptcy Prediction Using Principal Component Analysis Based Neural Network Models

2010 ◽  
pp. 138-156
Author(s):  
G. A. Rekha Pai ◽  
G. A. Vijayalakshmi Pai
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Prediction Models ◽  
Network Models ◽  
Principal Component ◽  
Component Analysis ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Neural Network Models ◽  
The Individual

Industrial bankruptcy is a rampant problem which does not occur overnight and when it occurs can cause acute financial embarrassment to Governments and financial institutions as well as threaten the very viability of the firms. It is therefore essential to help industries identify the impending trouble early. Several statistical and soft computing based bankruptcy prediction models that make use of financial ratios as indicators have been proposed. Majority of these models make use of a selective set of financial ratios chosen according to some appropriate criteria framed by the individual investigators. In contrast, this study considers any number of financial ratios irrespective of the industrial category and size and makes use of Principal Component Analysis to extract their principal components, to be used as predictors, thereby dispensing with the cumbersome selection procedures used by its predecessors. An Evolutionary Neural Network (ENN) and a Backpropagation Neural Network with Levenberg Marquardt’s training rule (BPN) have been employed as classifiers and their performance has been compared using Receiver Operating Characteristics (ROC) analyses. Termed PCA-ENN and PCA-BPN models, the predictive potential of the two models have been analyzed over a financial database (1997-2000) pertaining to 34 sick and 38 non sick Indian manufacturing companies, with 21 financial ratios as predictor variables.

scholarly journals The Translation and Validation of the Dutch Monash Dog–Owner Relationship Scale (MDORS)

Animals ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 249
Author(s):  
Emmy A.E. van Houtert ◽  
Nienke Endenburg ◽  
Joris J. Wijnker ◽  
T. Bas Rodenburg ◽  
Hein A. van Lith ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Factor Model ◽  
Principal Component ◽  
Component Analysis ◽  
Cronbach’S Alpha ◽  
Cronbach's Alpha ◽  
Dutch Translation ◽  
English Speaking ◽  
Translation And Validation ◽  
Three Factor

The Monash Dog–Owner Relationship Scale (MDORS) is a questionnaire that is used to evaluate the perceived relationship between humans and their dog. This questionnaire was originally only formulated and validated in English, which limits its use among non-English speaking individuals. Although a translation could be made, the translation of questionnaires without additional validation often impairs the reliability of that questionnaire. Therefore, the aim of this study was to validate a translation of the MDORS that is suitable for use among native Dutch speakers. To achieve this, a Dutch translation of the MDORS was made and checked for spelling/grammar mistakes, readability, feasibility, and clarity. A test–retest comparison was subsequently performed on the translation together with a calculation of Cronbach’s alpha score and principal component analysis (PCA). Through the PCA, we found that the three-factor model of the original MDORS was also largely present in the Dutch translation. However, deviations were also found, as several questions did not achieve high PCA scores in their original factor. Therefore, we propose that these questions are excluded from the Dutch MDORS.

Development of a New Tool for Managing Performance Nutrition: The Athlete Food Choice Questionnaire

2019 ◽  
Vol 29 (6) ◽  
pp. 620-627 ◽  
Author(s):  
Rachael L. Thurecht ◽  
Fiona E. Pelly
Keyword(s):  
Principal Component Analysis ◽  
Nutrition Education ◽  
Food Choice ◽  
Weight Control ◽  
Principal Component ◽  
Component Analysis ◽  
Key Factors ◽  
Dietary Interventions ◽  
Frequency Scale ◽  
The Individual

This study aimed to develop and refine an Athlete Food Choice Questionnaire (AFCQ) to determine the key factors influencing food choice in an international cohort of athletes. A questionnaire that contained 84 items on a 5-point frequency scale was developed for this study. Athletes at the 2017 Universiade, in Taiwan, were invited to participate. Principal component analysis was utilized to identify key factors and to refine the questionnaire. Completed questionnaires were received from 156 athletes from 31 countries and 17 sports. The principal component analysis extracted 36 items organized into nine factors explaining 68.0% of variation. The nine factors were as follows: nutritional attributes of the food, emotional influences, food and health awareness, influence of others, usual eating practices, weight control, food values and beliefs, sensory appeal, and performance. The overall Kaiser–Meyer–Olkin measure was 0.75, the Bartlett test of sphericity was statistically significant, χ2(666) =2,536.50, p < .001, and all of the communalities remained >0.5. Intercorrelations were detected between performance and both nutritional attributes of the food and weight control. The price of food, convenience, and situational influences did not form part of the factorial structure. This research resulted in an AFCQ that includes factors specific to athletic performance and the sporting environment. The AFCQ will enable researchers and sports dietitians to better tailor nutrition education and dietary interventions to suit the individual or team. The next phase will test the accuracy and reliability of the AFCQ both during and outside of competition. The AFCQ is a useful tool to assist with management of performance nutrition for athletes.

A strategy for selecting diverse accessions using Principal Component Analysis from a large germplasm collection of soybean

2003 ◽  
Vol 1 (2-3) ◽  
pp. 151-156 ◽  
Author(s):  
R. L Sapra ◽  
S. K. Lal
Keyword(s):  
Principal Component Analysis ◽  
Sample Size ◽  
Principle Component Analysis ◽  
Quantitative Traits ◽  
Germplasm Collection ◽  
Principal Component ◽  
Target Population ◽  
Component Analysis ◽  
Principle Component ◽  
The Individual

AbstractWe suggest a diversity-dependent strategy, based on Principle Component Analysis, for selecting distinct accessions/parents for breeding from a soybean germplasm collection comprising of 463 lines, characterized and evaluated for 10 qualitative and eight quantitative traits. A sample size of six accessions included all the three states, namely low, medium and high of the individual quantitative traits, while a sample of 16–19 accessions included all the 60–64 distinct states of qualitative as well as quantitative traits. Under certain assumptions, the paper also develops an expression for estimating the size of a target population for capturing maximum variability in a sample three accessions.

scholarly journals MEMONITOR KUALITAS PEMBELAJARAN DI FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM UNIVERSITAS UDAYANA

2021 ◽  
Vol 10 (3) ◽  
pp. 168
Author(s):  
RAHMAD RAHMAD WIDODO ◽  
I PUTU EKA NILA KENCANA ◽  
NI LUH PUTU SUCIPTAWATI
Keyword(s):  
Principal Component Analysis ◽  
Control Chart ◽  
Principal Component ◽  
Component Analysis ◽  
Natural Sciences ◽  
Quality Of Learning ◽  
Study Programs ◽  
The Individual ◽  
New Variables

Controlling the quality of learning is very important and influences the accreditation of study programs at the Faculty of Mathematics and Natural Sciences Udayana University, as a guarantor of the quality of graduates. Apply pricipal component analysis to reduce the number of determinant attributes of learning quality, with the aim of looking at the data structure with fewer variables. The control chart is a multivariate control chart that is used to view the potrait of the quality of learning in the Mathematics and Natural Sciences Faculty, using new variables obtained from principal component analysis. The results obtained from principal component analysis show that the contribution of the learning quality indicators is univen. The potrait of the quality of learning at the Faculty of Mathematics and Natural Sciences obtained from the individual-moving range (I-MR) and the control chart shows the need for corrective actions and monitor regularly to improve the quality of learning.

scholarly journals Dissecting individual differences in responses to naturalistic stimuli in functional MRI: effects of development and gender

2020 ◽  
Author(s):  
Xin Di ◽  
Bharat B. Biswal
Keyword(s):  
Principal Component Analysis ◽  
Individual Differences ◽  
Functional Mri ◽  
Principal Component ◽  
Component Analysis ◽  
Gender Effects ◽  
Delayed Response ◽  
List Type ◽  
And Gender ◽  
The Individual

AbstractFunctional MRI (fMRI) study of naturalistic conditions, e.g. movie watching, usually focuses on shared responses across subjects. However, individual differences in the responses have been attracting increasing attention in search of group differences or associations with behavioral outcomes. The individual differences have been studied by directly modeling the cross-subject correlation matrix or projecting the relations into a 1-D space. We contend that it is critical to examine whether there are single or multiple consistent components of responses underlying the whole population, because multiple components may undermine the individual relations using the previous methods. We use principal component analysis (PCA) to examine the heterogeneity of brain responses across subjects in terms of the eigenvalues of the covariance matrix, and utilize this approach to study developmental trajectories and gender effects in a movie watching dataset. We identified several brain networks in the parietal cortex that showed a significant second principal component (PC) of regional responses, which were mainly represented the younger children. The second PCs in some networks, i.e. the supramarginal network, resembled a delayed version of the first PCs for 4 seconds (2 TR), indicating delayed responses in the younger children than the older children and adults. However, no apparent gender effects were found in the first and second PCs. The analyses highlight the importance of identifying multiple consistent responses underlying individual differences in responses to naturalistic stimuli. And the PCA-based approach could be complementary to the commonly used intersubject correlation analysis.HighlightsThere may be multiple consistent responses among subjects during movie watchingPrincipal component analysis can be used to identify the multiple consistent responsesMany brain regions showed two principal components that were separated by ageYounger children showed delayed response in the supramarginal gyrus and precuneus

Studies on Performance Evaluation of Homeopathic Treatment for Essential Hypertension using Principle Component Analysis

2015 ◽  
Vol 10 (1) ◽  
Author(s):  
A. C. Mondal ◽  
Sumanta Ray ◽  
Kaberi Dey ◽  
A. Neogi
Keyword(s):  
Essential Hypertension ◽  
Clinical Medicine ◽  
Principal Component ◽  
Daily Practice ◽  
Component Analysis ◽  
Systemic Hypertension ◽  
The World ◽  
Effective Role ◽  
The Common ◽  
Homeopathic Remedies

Hypertension is one of the common diseases among people all over the world. Clinical Medicine Hypertension is mainly of two types-Essential Hypertension and Secondary or Systemic Hypertension. Here, we are working with Essential Hypertension. There are more than 200 remedies found under the rubric Hypertension in the latest homeopathic repertory but all of them are not equally important. Even some of them are not well, proved. drugs. For this reason we have chosen some most important polychrest (most commonly used broad spectrum homeopathic remedies) as well as the medicines that have effective role in controlling Hypertension in daily practice. In this paper, we will develop a computational model that retrieve more important and less important symptoms to assess a Hypertension patient. We also rank the medicines, used for Essential Hypertension as per importance by using Principal Component Analysis.

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