The Association of Work Satisfaction and Burnout Risk in Endoscopy Nursing Staff—A Cross-Sectional Study Using Canonical Correlation Analysis

Background: Burnout is known to have detrimental effects on healthcare staff with regard to both personal and occupational matters. The association between burnout symptoms and work satisfaction in endoscopy nursing staff in Germany has not been studied previously. We aimed to investigate the association between work satisfaction and risk of burnout in endoscopy nursing staff in Germany and to extract predictors for burnout in the area of work satisfaction, which can inform the design of future interventions. Setting: All members of the German Association of Endoscopy Staff in Germany (Deutsche Gesellschaft für Endoskopiefachberufe e.V.—DEGEA) were invited to take part in an online survey. Methods: The total sample consisted of 674 endoscopy staff members. Of those, 579 were female (85.9%) and 95 were male (14.1%). The mean age of the participants was 44.3 years (SD 10.6), with a median age of 46 years, a minimum age of 20, and a maximum age of 64 years. We used confirmatory factor analyses to examine the Maslach burnout inventory (MBI) and, a questionnaire for assessing general and facet-specific job satisfaction (KAFA), regarding their postulated internal structure in our special sample. Canonical correlations were performed to examine the association between work satisfaction and burnout in endoscopy staff members. Results: We were able to replicate the factorial structures of the MBI and the KAFA, both showing an acceptable model fit. The canonical correlation analysis resulted in three canonical functions, with canonical correlations of 0.64 (p < 0.001), 0.32 (p < 0.001), and 0.17 (p < 0.001). The first canonical function revealed that KAFA scales for colleagues, professional development, payment, supervisor, and general job satisfaction were good predictors for less exhaustion, less depersonalization and lack of empathy, and higher personal accomplishment. Commonality analysis revealed that general job satisfaction was the most significant factor in explaining the squared canonical correlation. The second canonical function showed that occupational function and colleagues were good predictors for exhaustion and personal accomplishment. Conclusions: Interventions aimed at ameliorating symptoms of burnout in endoscopy staff should be tailored to address specific needs as experienced by the employees. Therefore, the results of this study could contribute to the design of various interventions, which could be employed to address the issue of work satisfaction and burnout in endoscopy staff most effectively.

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Kernel functional canonical correlation analysis

Acta Universitatis Lodziensis Folia oeconomica ◽

10.18778/0208-6018.325.12 ◽

2017 ◽

Vol 5 (325) ◽

Author(s):

Mirosław Krzyśko ◽

Łukasz Waszak

Keyword(s):

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Functional Data ◽

Canonical Correlation ◽

Multivariate Data ◽

Research Interest ◽

Canonical Correlations ◽

Canonical Variables ◽

Correlation Methods ◽

The Subject

Canonical correlation methods for data representing functions or curves have received much attention in recent years. Such data, known in the literature as functional data (Ramsay and Silverman, 2005), has been the subject of much recent research interest. Examples of functional data can be found in several application domains, such as medicine, economics, meteorology and many others. Unfortunately, the multivariate data canonical correlation methods cannot be used directly for functional data, because of the problem of dimensionality and difficulty in taking into account the correlation and order of functional data. The problem of constructing canonical correlations and canonical variables for functional data was addressed by Leurgans et al. (1993), and further developments were made by Ramsay and Silverman (2005). In this paper we propose a new method of constructing canonical correlations and canonical variables for functional data.

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On the Interpretation of Canonical Analysis

Journal of Marketing Research ◽

10.1177/002224377200900211 ◽

1972 ◽

Vol 9 (2) ◽

pp. 187-192 ◽

Cited By ~ 27

Author(s):

Mark I. Alpert ◽

Robert A. Peterson

Keyword(s):

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Canonical Analysis ◽

Canonical Correlations ◽

Independent Variables ◽

Shared Variation ◽

Marketing Problems

Canonical correlation analysis has been increasingly applied to marketing problems. This article presents some suggestions for interpreting canonical correlations, particularly for avoiding overstatement of the shared variation between sets of independent variables and for explicating relationships among variables within each set.

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A Statistical Method for Correlating Paired Wave Spectra

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-15-0040.1 ◽

2015 ◽

Vol 32 (11) ◽

pp. 2130-2146 ◽

Cited By ~ 2

Author(s):

Clarence O. Collins ◽

C. Linwood Vincent ◽

Hans C. Graber

Keyword(s):

Correlation Analysis ◽

Statistical Method ◽

Canonical Correlation Analysis ◽

Standard Method ◽

Canonical Correlation ◽

Ocean Wave ◽

Correlation Structure ◽

Wave Spectra ◽

Canonical Correlations ◽

The Matrix

AbstractOcean wave spectra are complex. Because of this complexity, no widely accepted method has been developed for the comparison between two sets of paired wave spectra. A method for intercomparing wave spectra is developed based on an example paradigm of the comparison of model spectra to observed spectra. Canonical correlation analysis (CCA) is used to investigate the correlation structure of the matrix of spectral correlations. The set of N ranked canonical correlations developed through CCA (here termed the r-sequence) is shown to be an effective method for understanding the degree of correlation between sets of paired spectral observation. A standard method for intercomparing sets of wave spectra based on CCA is then described. The method is elucidated through analyses of synthetic and real spectra that span a range of correlation from random to almost equal.

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Canonical correlation analysis for studying the relationship between egg production traits and body weight, egg weight and age at sexual maturity in layers

Czech Journal of Animal Science ◽

10.17221/4010-cjas ◽

2011 ◽

Vol 50 (No. 4) ◽

pp. 163-168 ◽

Cited By ~ 16

Author(s):

Y. Akbaş ◽

Ç. Takma

Keyword(s):

Body Weight ◽

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Sexual Maturity ◽

Egg Production ◽

Production Traits ◽

Egg Weight ◽

Canonical Correlations ◽

The Relationship

In this study, canonical correlation analysis was applied to layer data to estimate the relationships of egg production with age at sexual maturity, body weight and egg weight. For this purpose, it was designed to evaluate the relationship between two sets of variables of laying hens: egg numbers at three different periods as the first set of variables (Y) and age at sexual maturity, body weight, egg weight as the second set of variables (X) by using canonical correlation analysis. Estimated canonical correlations between the first and the second pair of canonical variates were significant (P < 0.01). Canonical weights and loadings from canonical correlation analysis indicated that age at sexual maturity had the largest contribution as compared with body weight and egg weight to variation of the number of egg productions at three different periods.  

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Canonical correlation analysis for functional data

Biometrical Letters ◽

10.2478/bile-2013-0020 ◽

2013 ◽

Vol 50 (2) ◽

pp. 95-105 ◽

Cited By ~ 5

Author(s):

Mirosław Krzyśko ◽

Łukasz Waszak

Keyword(s):

Correlation Analysis ◽

Correlation Coefficient ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Data Sets ◽

Linear Combinations ◽

Canonical Correlations ◽

Canonical Variables ◽

Maximal Correlation ◽

Canonical Correlation Coefficient

Summary Classical canonical correlation analysis seeks the associations between two data sets, i.e. it searches for linear combinations of the original variables having maximal correlation. Our task is to maximize this correlation, and is equivalent to solving a generalized eigenvalue problem. The maximal correlation coefficient (being a solution of this problem) is the first canonical correlation coefficient. In this paper we propose a new method of constructing canonical correlations and canonical variables for a pair of stochastic processes represented by a finite number of orthonormal basis functions.

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Identificação de Preditores Para as Chuvas do Setor Leste do Nordeste do Brasil Utilizando Análise de Correlação Canônica

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v13.4.p1463-1482 ◽

2020 ◽

Vol 13 (4) ◽

pp. 1463

Author(s):

Geber Barbosa De Albuquerque Moura ◽

José Ivaldo Barbosa de Brito ◽

Francisco de Assis Salviano de Sousa ◽

Enilson Palmeira Cavalcanti ◽

Jhon Lennon Bezerra da Silva ◽

...

Keyword(s):

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Atmospheric Pressure ◽

Canonical Correlation ◽

Equatorial Pacific ◽

Northeastern Brazil ◽

Equatorial Pacific Ocean ◽

Trade Winds ◽

Homogeneous Groups ◽

Canonical Correlations

O objetivo deste trabalho foi encontrar as melhores variáveis preditoras através de análise de correlação canônica nos ventos alísios, Temperatura da Superfície do Mar (TSM), Pressão atmosférica à superfície no Oceano Pacífico Equatorial e TSM no Atlântico Tropical (área do Dipolo), de forma que se possam elaborar modelos de previsão da precipitação pluvial (período chuvoso) do setor leste do Nordeste do Brasil para os quatro meses mais chuvosos dos três grupos homogêneos, com antecedência de três meses. Os grupos foram escolhidos a partir de análise de agrupamento utilizando o método hierárquico. Para estudar as correlações canônicas entre a precipitação dos grupos com os dados padronizados de TSM, vento e pressão atmosférica, as análises fundamentaram-se na série dos totais de precipitação de abril a julho e dados defasados de médias de três meses (média de Novembro a Janeiro) de TSM, vento em 850 hPa no Pacífico Equatorial e pressão da atmosfera em Tahiti e Darwin para o período de 1986 a 2017. Percebe-se que os principais preditores para os grupos homogêneos foram, por ordem de maior importância: Média de três meses de atraso do índice de ventos alísios Equatorial central (MedWC), Média da pressão atmosférica à superfície em Darwin (Mdarwin), Média do EN 34 (MEN34), Média da pressão atmosférica à superfície em Tahiti (Mtahiti) e Média de índice de ventos alísios leste (MedWE). Nota-se deste atraso que a principal influência está no Pacífico, no ENOS. Predictors identification for rain in the east sector of the Northeast Brazil using canonical correlation analysis A B S T R A C TThe objective of this work was to find the best predictor variables through canonical correlation analysis in trade winds, Sea Surface Temperature (SST), Atmospheric pressure at the surface in the Equatorial Pacific Ocean and SST in the Tropical Atlantic (Dipole area), that models for forecasting rainfall (rainy season) in the eastern sector of northeastern Brazil can be developed for the four rainiest months of the three homogeneous groups, three months in advance. The groups were chosen from the cluster analysis using the hierarchical method. To study the canonical correlations between the precipitation of the groups with the standardized data of SST, wind and atmospheric pressure, the analyzes were based on the series of precipitation totals from April to July and lagged data of three-month averages (average from November to July). January) of SST, wind at 850 hPa in the Equatorial Pacific and atmospheric pressure in Tahiti and Darwin for the period from 1986 to 2017. It can be seen that the main predictors for homogeneous groups were, in order of greatest importance: Average of three months delay of the central Equatorial trade winds index (MedWC), mean of the atmospheric pressure at the surface in Darwin (Mdarwin), mean of the EN 34 (MEN34), mean of the atmospheric pressure at the surface in Tahiti (Mtahiti) and mean of the east trade winds (MedWE). It is noted from this delay that the main influence is in the Pacific, in the ENSO.Keywords: wind, SST, precipitation.

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A Jacobi–Davidson Method for Large Scale Canonical Correlation Analysis

Algorithms ◽

10.3390/a13090229 ◽

2020 ◽

Vol 13 (9) ◽

pp. 229

Author(s):

Zhongming Teng ◽

Xiaowei Zhang

Keyword(s):

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Large Scale ◽

Numerical Examples ◽

Canonical Correlations ◽

Generalized Eigenvalue ◽

Convergence Results ◽

Davidson Method ◽

Canonical Weight

In the large scale canonical correlation analysis arising from multi-view learning applications, one needs to compute canonical weight vectors corresponding to a few of largest canonical correlations. For such a task, we propose a Jacobi–Davidson type algorithm to calculate canonical weight vectors by transforming it into the so-called canonical correlation generalized eigenvalue problem. Convergence results are established and reveal the accuracy of the approximate canonical weight vectors. Numerical examples are presented to support the effectiveness of the proposed method.

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Conditional canonical correlation estimation based on covariates with random forests

Bioinformatics ◽

10.1093/bioinformatics/btab158 ◽

2021 ◽

Author(s):

Cansu Alakuş ◽

Denis Larocque ◽

Sébastien Jacquemont ◽

Fanny Barlaam ◽

Charles-Olivier Martin ◽

...

Keyword(s):

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

R Package ◽

Significance Test ◽

Supplementary Information ◽

Canonical Correlations ◽

Correlation Estimation ◽

The Individual ◽

Individual Trees

Abstract Motivation Investigating the relationships between two sets of variables helps to understand their interactions and can be done with canonical correlation analysis (CCA). However, the correlation between the two sets can sometimes depend on a third set of covariates, often subject-related ones such as age, gender or other clinical measures. In this case, applying CCA to the whole population is not optimal and methods to estimate conditional CCA, given the covariates, can be useful. Results We propose a new method called Random Forest with Canonical Correlation Analysis (RFCCA) to estimate the conditional canonical correlations between two sets of variables given subject-related covariates. The individual trees in the forest are built with a splitting rule specifically designed to partition the data to maximize the canonical correlation heterogeneity between child nodes. We also propose a significance test to detect the global effect of the covariates on the relationship between two sets of variables. The performance of the proposed method and the global significance test is evaluated through simulation studies that show it provides accurate canonical correlation estimations and well-controlled Type-1 error. We also show an application of the proposed method with EEG data. Availability and implementation RFCCA is implemented in a freely available R package on CRAN (https://CRAN.R-project.org/package=RFCCA). Supplementary information Supplementary data are available at Bioinformatics online.

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Orthonormal Canonical Correlation Analysis

Open Statistics ◽

10.1515/stat-2020-0104 ◽

2021 ◽

Vol 2 (1) ◽

pp. 24-36

Author(s):

Stan Lipovetsky

Keyword(s):

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Data Sets ◽

Multivariate Statistical ◽

Canonical Correlations ◽

Robust Version ◽

Multiple Variables ◽

Value Decomposition ◽

Further Development

Abstract Complex managerial problems are usually described by datasets with multiple variables, and in lack of a theoretical model, the data structures can be found by special multivariate statistical techniques. For two datasets, the canonical correlation analysis and its robust version are known as good working research tools. This paper presents their further development via the orthonormal approximation of data matrices which corresponds to using singular value decomposition in the canonical correlations. The features of the new method are described and applications considered. This type of multivariate analysis is useful for solving various practical problems of applied statistics requiring operating with two data sets, and can be helpful in managerial estimations and decision making.

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Sinc-Windowing and Multiple Correlation Coefficients Improve SSVEP Recognition Based on Canonical Correlation Analysis

Computational Intelligence and Neuroscience ◽

10.1155/2018/4278782 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Valeria Mondini ◽

Anna Lisa Mangia ◽

Luca Talevi ◽

Angelo Cappello

Keyword(s):

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Classification Accuracy ◽

Canonical Correlation ◽

Positive Impact ◽

Low Cost ◽

Correlation Coefficients ◽

Canonical Correlations ◽

User Training ◽

The Impact

Canonical Correlation Analysis (CCA) is an increasingly used approach in the field of Steady-State Visually Evoked Potential (SSVEP) recognition. The efficacy of the method has been widely proven, and several variations have been proposed. However, most CCA variations tend to complicate the method, usually requiring additional user training or increasing computational load. Taking simple procedures and low computational costs may be, however, a relevant aspect, especially in view of low-cost and high-portability devices. In addition, it would be desirable that the proposed variations are as general and modular as possible to facilitate the translation of results to different algorithms and setups. In this work, we evaluated the impact of two simple, modular variations of the classical CCA method. The variations involved (i) the number of canonical correlations used for classification and (ii) the inclusion of a prefiltering step by means of sinc-windowing. We tested ten volunteers in a 4-class SSVEP setup. Both variations significantly improved classification accuracy when they were used separately or in conjunction and led to accuracy increments up to 7-8% on average and peak of 25–30%. Additionally, variations had no (variation (i)) or minimal (variation (ii)) impact on the number of algorithm steps required for each classification. Given the modular nature of the proposed variations and their positive impact on classification accuracy, they might be easily included in the design of CCA-based algorithms that are even different from ours.

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