scholarly journals Personality disorders in substance abusers: Validation of the DIP-Q through principal components factor analysis and canonical correlation analysis

2005 ◽  
Vol 5 (1) ◽  
Author(s):  
Morten Hesse
Keyword(s):  
Factor Analysis ◽  
Correlation Analysis ◽  
Personality Disorders ◽  
Canonical Correlation Analysis ◽  
Principal Components ◽  
Canonical Correlation ◽  
Substance Abusers

scholarly journals ANALISIS KORELASI KANONIK PADA HUBUNGAN ANTARA SOFT SKILLS TERHADAP PRESTASI BELAJAR MAHASISWA

2019 ◽  
Vol 8 (4) ◽  
pp. 267
Author(s):  
NI PUTU JULIANINGSIH ◽  
EKA N KENCANA ◽  
KOMANG GDE SUKARSA
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Soft Skills ◽  
Sampling Technique ◽  
Primary Data ◽  
Learning Achievement ◽  
Confirmatory Factor

The purpose of this study is to know the relationship between soft skills and learning achievement using canonical correlation analysis and to find out the dominant dimensions of soft skills and learning achievement using confirmatory factor analysis. The data used are primary data obtained by distributing questionnaires. The sampling technique used was proportional to size sampling with the number of respondents as many as 150 respondents. The results of the study with canonical correlation analysis showed that soft skills with learning achievement had a close relationship with canonical correlation values ??of 0.70397. The results of the study with confirmatory factor analysis showed that the dominant dimensions of soft skills and learning achievement were flexibility with the percentage of the variance of 69.081% and cognitive percentage of the variance of 71.706%.

Δυναμική μελέτη με χρήση μαγνητοεγκεφαλογραφίας - ηλεκτροεγκεφαλογραφίας ασθενών με διαταραχές των ανωτέρων γνωσιακών λειτουργιών

2015 ◽  
Author(s):  
Ελισσαίος Καραγεωργίου
Keyword(s):  
Factor Analysis ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Cross Correlations

Ο κύριος στόχος της παρούσας διδακτορικής διατριβής ήταν η διερεύνηση των χαρακτηριστικών των σύγχρονων νευρικών αλληλεπιδράσεων (synchronous neuralinteractions – SNI), όπως εκφράζονται στο εγκεφαλογραφικό σήμα μέσω των σύγχρονων μερικών ετεροσυσχετίσεων (partial cross-correlations – 0ij PCC ) μεταξύ αισθητήρων, σε ασθενείς με γνωσιακές διαταραχές. Η μελέτη χωρίστηκε σε δύο μέρη.Στο πρώτο μέρος της διατριβής, περιγράφεται μια δοκιμασία για την αξιολόγηση των δυναμικών λειτουργιών του εγκεφάλου μέσω της μαγνητοεγκεφαλογραφίας (ΜΕΓ)σε ασθενείς με νευρολογικές ή ψυχικές διαταραχές. Η δοκιμασία βασίζεται στη μέτρηση των δυναμικών SNI, μια ουσιαστική πτυχή της λειτουργίας του εγκεφάλου. Τα ΜΕΓ σήματα καταγράφηκαν από 248 αξονικά πρανήμετρα καθώς 142 εθελοντές εστίασαν το βλέμμα τους σε ένα φωτεινό σημείο για 45-60 s. Μετά από την εφαρμογή Αυτοπαλινδρομούμενου Διαφοροποιούμενου Κινητού Μέσου Όρου (Autoregressive υπολοιπόμενες ακολουθίες, υπολογίσθηκαν όλες οι σύγχρονες μερικές ετεροσυσχετίσεις και οι z-μετασχηματισμοί (z-transformations – ) μεταξύ των 248 αισθητήρων, παρέχοντας το μέγεθος και το πρόσημο (θετικό, αρνητικό) της άμεσης σύγχρονης συσχέτισης με χρονική ευκρίνεια 1 ms. Διαπιστώθηκε ότι SNI υποσύνολα επιτυχώς κατέταξαν εθελοντές στις αντίστοιχες ομάδες τους (σκλήρυνση κατά πλάκας, νόσος του Alzheimer, σχιζοφρένια, σύνδρομο Sjögren, χρόνιος αλκοολισμός, προσωπαλγία, υγιείς) και παρείχαν άριστα αποτελέσματα βάσει εξωτερικής διασταυρούμενης επιβεβαίωσης.Στο δεύτερο μέρος της διατριβής, γίνεται άμεση σύγκριση μεταξύ SNI και γνωσιακών λειτουργιών σε εθελοντές που πάσχουν από νόσο του Alzheimer. Οι γνωσιακές λειτουργίες αντιπροσωπεύθηκαν μέσω νευροψυχολογικών μετρήσεων (neuropsychological scores – NP). Κατ’ αρχάς, υπολογίσθηκαν όλες οι μεμονωμένες συσχετίσεις μεταξύ κάθε SNI και κάθε NP, επιτρέποντας μια αρχική κατανόηση των SNI μέσω των συγκεκριμένων γνωσιακών δοκιμασιών. Δεύτερον, εφαρμόζοντας Ανάλυση Κανονικών Συσχετίσεων (canonical correlation analysis – CCA) μεταξύ των δύο συνόλων μεταβλητών (SNI και NP), και μετά από ανάλυση παραγόντων (factor analysis) κάθε συνόλου, επιτεύχθηκε η βέλτιστη συσχέτιση μεταξύ ολόκληρου του ΜΕΓ σήματος και των γνωσιακών λειτουργιών. Τα αποτελέσματα δεικνύουν ότι οι SNI στο σύνολό τους σχετίζονται πιο ισχυρά με τη μνήμη και τη γλώσσα, και λιγότερο με την εκτελεστική λειτουργία. Ακόμη και κατά την ανάλυση μεμονωμένων συσχετίσεων υπήρχαν πολύ περισσότερες SNI που σχετίζονταν με τη μνήμη. Αυτά τα αποτελέσματα παρέχουν μια άμεση ερμηνεία της πληροφορίας που υπάρχει στις SNI και αποτελούν τη βάση για την αναγνώριση συγκεκριμένων νοσολογικών φαινοτύπων σύμφωνα με τις υποκείμενες γνωσιακές διαταραχές.

The Impact of Channel Leadership Behavior on Intrachannel Conflict

1983 ◽  
Vol 47 (3) ◽  
pp. 21-34 ◽  
Author(s):  
Patrick L. Schul ◽  
William M. Pride ◽  
Taylor L. Little
Keyword(s):  
Factor Analysis ◽  
Correlation Analysis ◽  
Leadership Style ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Leadership Behavior ◽  
Distribution Channel ◽  
Product Service ◽  
Different Types ◽  
The Impact

This study examines the impact of different types of channel leadership behavior on channel members' perceptions of intrachannel conflict in a franchise distribution channel. Factor analysis and canonical correlation analysis are used to explore the relationships between participative, supportive and directive leadership behavior and dependent measures of intrachannel conflict. The results indicate that conflict arising from both administrative and product-service issues diminishes when the franchisor is perceived to exhibit a leadership style emphasizing participation, support and direction in carrying out channel activities.

Structure of the Coolidge Axis II Inventory Personality Disorder Scales from the Five-Factor Model Perspective

1998 ◽  
Vol 83 (3) ◽  
pp. 947-952 ◽  
Author(s):  
Nerella V. Ramanaiah ◽  
J. Patrick Sharpe
Keyword(s):  
Correlation Analysis ◽  
Personality Disorders ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Simple Structure ◽  
Factor Model ◽  
Five Factor Model ◽  
Canonical Variate ◽  
Axis Ii ◽  
Canonical Variates

Coolidge, et al. in 1994 tested the generality and comprehensiveness of the five-factor model of personality as applied to personality disorders by performing a canonical correlation analysis for the scales from the Coolidge Axis II Inventory and the NEO Personality Inventory testing 178 undergraduates (106 men and 72 women). Their results did not support the generality and comprehensiveness of the five-factor model for interpreting the structure of personality disorders. A major problem with this study was that the data did not show good simple structure and meaningfulness because no rotation was performed for the canonical variates. The present study tested the hypothesis that the results of Coolidge, et al. might be attributed to the failure to rotate canonical variates to obtain good simple structure. For 220 students in introductory psychology (104 men and 116 women), canonical correlation analysis with varimax rotation was performed for scores on the Coolidge Axis II Inventory scales and the NEO Five-Factor Inventory scales. The analysis indicated five canonical variate pairs which were interpreted as Neuroticism, Extraversion, Openness, Disagreeableness, and Conscientiousness, supporting the tested hypothesis as well as the generality and comprehensiveness of this model for describing the structure of personality disorders.

scholarly journals Relationship between snow cover and atmospheric circulation, central North America, winter 1988

1997 ◽  
Vol 25 ◽  
pp. 347-352 ◽  
Author(s):  
Chris Derksen ◽  
Kkevin Misurak ◽  
Ellsworth Ledrew ◽  
Joe Piwowar ◽  
Barry Goodison
Keyword(s):  
Correlation Analysis ◽  
Snow Cover ◽  
Atmospheric Circulation ◽  
Canonical Correlation Analysis ◽  
Principal Components ◽  
Canonical Correlation ◽  
Snow Water Equivalent ◽  
Principal Component ◽  
Original Data ◽  
Snow Cover Extent

The stochastic relationships between terrestrial snow water equivalent (SWE) and measures of the atmospheric circulation were investigated for the Canadian Prairies and the American Great Plains for the winter of 1988. Snow-cover extent, derived from EASE-grid SSM/I satellite data, and griddcd atmospheric data from the National Meteorological Center were averaged at five day intervals. Principal components analysis (PCA) were performed for the time series of SSM/I snow-cover imagery as well as for 700 mb geopotential height and temperature, 500 mb height and 700–500 mb thickness. Canonical correlation analysis of the derived principal component weights was used to identify relationships between atmospheric variables and SWE. Results of the PCA indicate that a high degree of variance in upper air variables (>75%) can be explained by the first three principal components, while the first three SWE components account for over 90% of the variance in the original data. Results of the canonical correlation analysis show positive relationships between snow-cover accumulation and a meridional pressure distribution pattern, while snow ablation is linked to a zonal atmospheric pressure pattern.

scholarly journals Expression reflects population structure

2018 ◽  
Author(s):  
Brielin C Brown ◽  
Nicolas L. Bray ◽  
Lior Pachter
Keyword(s):  
Gene Expression ◽  
Population Structure ◽  
African Americans ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Gene Expression Data ◽  
Principal Components ◽  
Canonical Correlation ◽  
Principal Component ◽  
Expression Data

AbstractPopulation structure in genotype data has been extensively studied, and is revealed by looking at the principal components of the genotype matrix. However, no similar analysis of population structure in gene expression data has been conducted, in part because a naïve principal components analysis of the gene expression matrix does not cluster by population. We identify a linear projection that reveals population structure in gene expression data. Our approach relies on the coupling of the principal components of genotype to the principal components of gene expression via canonical correlation analysis. Futhermore, we analyze the variance of each gene within the projection matrix to determine which genes significantly influence the projection. We identify thousands of significant genes, and show that a number of the top genes have been implicated in diseases that disproportionately impact African Americans.Author SummaryHigh dimensional, multi-modal genomics datasets are becoming increasingly common, which warrants investigation into analysis techniques that can reveal structure in the data without over-fitting. Here, we show that the coupling of principal component analysis to canonical correlation analysis offers an efficient approach to exploratory analysis of this kind of data. We apply this method to the GEUVADIS dataset of genotype and gene expression values of European and Yoruban individuals, finding as-of-yet unstudied population structure in the gene expression values. Moreover, many of the top genes identified by our method have been previously implicated in diseases that disproportionately impact African Americans.

scholarly journals Relationship between snow cover and atmospheric circulation, central North America, winter 1988

1997 ◽  
Vol 25 ◽  
pp. 347-352 ◽  
Author(s):  
Chris Derksen ◽  
Kkevin Misurak ◽  
Ellsworth Ledrew ◽  
Joe Piwowar ◽  
Barry Goodison
Keyword(s):  
Correlation Analysis ◽  
Snow Cover ◽  
Atmospheric Circulation ◽  
Canonical Correlation Analysis ◽  
Principal Components ◽  
Canonical Correlation ◽  
Snow Water Equivalent ◽  
Principal Component ◽  
Original Data ◽  
Snow Cover Extent

The stochastic relationships between terrestrial snow water equivalent (SWE) and measures of the atmospheric circulation were investigated for the Canadian Prairies and the American Great Plains for the winter of 1988. Snow-cover extent, derived from EASE-grid SSM/I satellite data, and griddcd atmospheric data from the National Meteorological Center were averaged at five day intervals. Principal components analysis (PCA) were performed for the time series of SSM/I snow-cover imagery as well as for 700 mb geopotential height and temperature, 500 mb height and 700–500 mb thickness. Canonical correlation analysis of the derived principal component weights was used to identify relationships between atmospheric variables and SWE. Results of the PCA indicate that a high degree of variance in upper air variables (>75%) can be explained by the first three principal components, while the first three SWE components account for over 90% of the variance in the original data. Results of the canonical correlation analysis show positive relationships between snow-cover accumulation and a meridional pressure distribution pattern, while snow ablation is linked to a zonal atmospheric pressure pattern.

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