scholarly journals Factores determinantes de la satisfacción laboral en España en la crisis de 2008

2016 ◽  
Vol 12 (5) ◽  
pp. 1192 ◽  
Author(s):  
María Carmen Sánchez-Sellero ◽  
Pedro Sánchez-Sellero
Keyword(s):  
Job Satisfaction ◽  
Principal Component Analysis ◽  
Economic Crisis ◽  
Stepwise Regression ◽  
Linear Models ◽  
Principal Component ◽  
Component Analysis ◽  
Stepwise Multiple Regression ◽  
Personal Variables ◽  
Degree Of Satisfaction

Purpose: We try to find out differences between personal and job-related features to know which better explain job satisfaction. This study is made in a year of economic growth and in two years of economic crisis, in order to determine if the economic crisis affects to previous results.Design/methodology: The data are from the Quality of Labour Life Survey by the Ministry of Employment and Social Security in Spain, in 2007, 2009 and 2010. We use linear models (ANOVA), principal component analysis and stepwise multiple regression. The variables are degree of satisfaction with the current job and a group of personal variables (gender, age and education level) and job-related variables (with a maximum of 14 variables depending on the method).Findings: Using linear models get the variables related to work which provide better results to explain job satisfaction, and after a stepwise regression made with factors of principal component analysis, we find out that salary is one of the last factors in this explanation. The variables that influence on job satisfaction do not depend on the economic cycle, although the hierarchies are different among them.Social implications: During the crisis, the demands of workers are lower because they prefer to have a job with low working conditions and low salary than lose their job. Reducing the degree of satisfaction with stability and wages is due to the economic situation, because labour contracts are less stable and remunerated.Originality/value: We have compared the results of stepwise regression made with the original variables and the factors of principal component analysis. The combination of these methodologies is new in studies of job satisfaction, as well as the original combination of 14 variables related to work.

Relations among Self-Talk, Self-Consciousness, and Self-Knowledge

2002 ◽  
Vol 91 (3) ◽  
pp. 807-812 ◽  
Author(s):  
Johann F. Schneider
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Self Report ◽  
Stepwise Multiple Regression ◽  
Stepwise Multiple Regression Analysis ◽  
Factor Solution ◽  
Self Talk ◽  
Undergraduate University Students ◽  
Self Knowledge

The aim of this study was to examine the relations among self-talk, self-consciousness, and self-knowledge through an exploratory principal component analysis and to test the hypothesis that only the functional and reflective aspects of self-consciousness contribute to self-knowledge. A self-report questionnaire including 6 scales assessing different aspects of self-talk, self-consciousness, and self-knowledge was administered to 203 German undergraduate university students. A principal component analysis of the scales yielded a two-factor solution, supporting the distinction between functional and dysfunctional self-consciousness. In a stepwise multiple regression analysis, only functional self-consciousness was a significant predictor of self-knowledge. Limitations of the present measures of inner speech are addressed.

scholarly journals Multivariate Analysis of Risk Factors of the COVID-19 Pandemic in the Community of Madrid, Spain

2021 ◽  
Vol 18 (17) ◽  
pp. 9227
Author(s):  
Víctor Pérez-Segura ◽  
Raquel Caro-Carretero ◽  
Antonio Rua
Keyword(s):  
Risk Factors ◽  
Principal Component Analysis ◽  
Linear Models ◽  
Principal Component ◽  
Scientific Work ◽  
Component Analysis ◽  
Incidence Rates ◽  
Transmission Risk ◽  
Linear Regression Models ◽  
Multivariate Statistical

It has been more than one year since Chinese authorities identified a deadly new strain of coronavirus, SARS-CoV-2. Since then, the scientific work regarding the transmission risk factors of COVID-19 has been intense. The relationship between COVID-19 and environmental conditions is becoming an increasingly popular research topic. Based on the findings of the early research, we focused on the community of Madrid, Spain, which is one of the world’s most significant pandemic hotspots. We employed different multivariate statistical analyses, including principal component analysis, analysis of variance, clustering, and linear regression models. Principal component analysis was employed in order to reduce the number of risk factors down to three new components that explained 71% of the original variance. Cluster analysis was used to delimit the territory of Madrid according to these new risk components. An ANOVA test revealed different incidence rates between the territories delimited by the previously identified components. Finally, a set of linear models was applied to demonstrate how environmental factors present a greater influence on COVID-19 infections than socioeconomic dimensions. This type of local research provides valuable information that could help societies become more resilient in the face of future pandemics.

scholarly journals Determinants of Job Satisfaction of Accounting Professionals in Tunisia

2021 ◽  
Author(s):  
Lassaad Abdelmoula ◽  
Sami Boudabbous
Keyword(s):  
Job Satisfaction ◽  
Principal Component Analysis ◽  
Data Analysis ◽  
Multiple Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Extrinsic Factors ◽  
Accounting Professionals ◽  
Intrinsic And Extrinsic Factors

This study aims to identify the factors that affect the job satisfaction of professional accountants. It examines the relative effects of intrinsic and extrinsic factors on job satisfaction among accounting professionals. Our methodology was applied to 232 accounting professionals working in Tunisia. The methods of data analysis are principal component analysis (PCA) and multiple regression. The results show that intrinsic and extrinsic factors have a positive and significant effect on job satisfaction. However, promotion, growth and recognition do not have effect on job satisfaction.

scholarly journals Deep Phenotyping of Headache in Hospitalized COVID-19 Patients via Principal Component Analysis

2020 ◽  
Vol 11 ◽  
Author(s):  
Álvaro Planchuelo-Gómez ◽  
Javier Trigo ◽  
Rodrigo de Luis-García ◽  
Ángel L. Guerrero ◽  
Jesús Porta-Etessam ◽  
...  
Keyword(s):  
Physical Activity ◽  
Principal Component Analysis ◽  
Linear Models ◽  
Principal Component ◽  
Component Analysis ◽  
Cross Sectional Study ◽  
Tension Type Headache ◽  
Common Symptom ◽  
Cross Sectional ◽  
Novel Coronavirus

Objectives: Headache is a common symptom in systemic infections, and one of the symptoms of the novel coronavirus disease 2019 (COVID-19). The objective of this study was to characterize the phenotype of COVID-19 headache via machine learning.Methods: We performed a cross-sectional study nested in a retrospective cohort. Hospitalized patients with COVID-19 confirmed diagnosis who described headache were included in the study. Generalized Linear Models and Principal Component Analysis were employed to detect associations between intensity and self-reported disability caused by headache, quality and topography of headache, migraine features, COVID-19 symptoms, and results from laboratory tests.Results: One hundred and six patients were included in the study, with a mean age of 56.6 ± 11.2, including 68 (64.2%) females. Higher intensity and/or disability caused by headache were associated with female sex, fever, abnormal platelet count and leukocytosis, as well as migraine symptoms such as aggravation by physical activity, pulsating pain, and simultaneous photophobia and phonophobia. Pain in the frontal area (83.0% of the sample), pulsating quality, higher intensity of pain, and presence of nausea were related to lymphopenia. Pressing pain and lack of aggravation by routine physical activity were linked to low C-reactive protein and procalcitonin levels.Conclusion: Intensity and disability caused by headache attributed to COVID-19 are associated with the disease state and symptoms. Two distinct headache phenotypes were observed in relation with COVID-19 status. One phenotype seems to associate migraine symptoms with hematologic and inflammatory biomarkers of severe COVID-19; while another phenotype would link tension-type headache symptoms to milder COVID-19.

scholarly journals Predictable Components and Singular Vectors

2008 ◽  
Vol 65 (5) ◽  
pp. 1666-1678 ◽  
Author(s):  
Timothy DelSole ◽  
Michael K. Tippett
Keyword(s):  
Principal Component Analysis ◽  
Linear Models ◽  
Singular Vector ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Component Analysis ◽  
Variance Analysis ◽  
Affine Transformations ◽  
Forecast Uncertainty ◽  
Value Decomposition

Abstract This paper shows that if a measure of predictability is invariant to affine transformations and monotonically related to forecast uncertainty, then the component that maximizes this measure for normally distributed variables is independent of the detailed form of the measure. This result explains why different measures of predictability such as anomaly correlation, signal-to-noise ratio, predictive information, and the Mahalanobis error are each maximized by the same components. These components can be determined by applying principal component analysis to a transformed forecast ensemble, a procedure called predictable component analysis (PrCA). The resulting vectors define a complete set of components that can be ordered such that the first maximizes predictability, the second maximizes predictability subject to being uncorrelated of the first, and so on. The transformation in question, called the whitening transformation, can be interpreted as changing the norm in principal component analysis. The resulting norm renders noise variance analysis equivalent to signal variance analysis, whereas these two analyses lead to inconsistent results if other norms are chosen to define variance. Predictable components also can be determined by applying singular value decomposition to a whitened propagator in linear models. The whitening transformation is tantamount to changing the initial and final norms in the singular vector calculation. The norm for measuring forecast uncertainty has not appeared in prior predictability studies. Nevertheless, the norms that emerge from this framework have several attractive properties that make their use compelling. This framework generalizes singular vector methods to models with both stochastic forcing and initial condition error. These and other components of interest to predictability are illustrated with an empirical model for sea surface temperature.

Use of advanced statistical learning methods and principal component analysis in quantitative structure–genotoxicity relationship study of amines

2011 ◽  
Vol 76 (4) ◽  
pp. 243-264 ◽  
Author(s):  
Yueying Ren ◽  
Baowei Zhao ◽  
Xiaojun Yao
Keyword(s):  
Principal Component Analysis ◽  
Statistical Learning ◽  
Linear Models ◽  
Nonlinear Models ◽  
Nonlinear Modeling ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Learning Methods ◽  
Test Set

The paper highlighted the use of advanced nonlinear modeling and subset selection techniques in the construction of a good, predictive model for genotoxicity study of amines. Essentials accounting for a reliable model were all considered carefully. Chemicals were represented by a large number of CODESSA descriptors. Division of a whole sample into the training set and the test set was performed by principal component analysis (PCA). Six descriptors selected by the best multi-linear regression (BMLR) method in CODESSA program were used as inputs to build nonlinear models, using advanced statistical learning methods such as support vector machine (SVM) and projection pursuit regression (PPR). The models were validated through three ways, i.e. internal cross-validation (CV), a test set and an independent validation set. Analysis shows that nonlinear models produced better results than linear models and PPR model outperforms the rest in the following order: PPR > SVM > linear SVM ≥ BMLR. In addition, the relationships between the descriptors and the mutagenic behavior of compounds are well discussed.

Sign in / Sign up

Export Citation Format

Share Document