The Decomposition of Correlation Coefficients into Causal Effects

1977 ◽  
Vol 9 (7) ◽  
pp. 805-812
Author(s):  
R N Davidson
Keyword(s):  
Correlation Coefficient ◽  
Indirect Effects ◽  
Correlation Matrix ◽  
Correlation Coefficients ◽  
Crime Rates ◽  
Causal Effects ◽  
Direct And Indirect Effects ◽  
Simple Correlation ◽  
Causal Inferences ◽  
Worked Example

The correlation coefficient is commonly used, yet its potential for drawing causal inferences is hardly tapped. Given even quite a simple correlation matrix, any coefficient may be readily decomposed into direct and indirect effects; joint effects with other specified variables; and coordinated error effects due to unspecified causes. A worked example relating to crime rates is given.

scholarly journals Gformula: Estimating Causal Effects in the Presence of Time-Varying Confounding or Mediation using the G-Computation Formula

2011 ◽  
Vol 11 (4) ◽  
pp. 479-517 ◽  
Author(s):  
Rhian M. Daniel ◽  
Bianca L. De Stavola ◽  
Simon N. Cousens
Keyword(s):  
Indirect Effects ◽  
Related Problem ◽  
Causal Effect ◽  
Causal Effects ◽  
Time Varying ◽  
Direct And Indirect Effects ◽  
Computation Procedure ◽  
Computation Formula ◽  
Intermediate Variables

This article describes a new command, gformula, that is an implementation of the g-computation procedure. It is used to estimate the causal effect of time-varying exposures on an outcome in the presence of time-varying confounders that are themselves also affected by the exposures. The procedure also addresses the related problem of estimating direct and indirect effects when the causal effect of the exposures on an outcome is mediated by intermediate variables, and in particular when confounders of the mediator–outcome relationships are themselves affected by the exposures. A brief overview of the theory and a description of the command and its options are given, and illustrations using two simulated examples are provided.

scholarly journals On the application of the averaged correlation matrix in craniometry

2021 ◽  
pp. 141-149
Author(s):  
Ivan Shirobokov ◽  
Keyword(s):  
Correlation Coefficient ◽  
Correlation Matrix ◽  
Correlation Coefficients ◽  
Mantel Test ◽  
Eastern European ◽  
Individual Data ◽  
Correlation Matrices ◽  
Mahalanobis Distances ◽  
Random Variability ◽  
The Relationship

The article presents an averaged correlation matrix for 20 craniometric features, calculated for 24 male Eurasian samples. In some cases, correlation matrices calculated for different samples vary significantly; however, the fluctuations in the coefficients are usually random. Most correlation coefficients between craniometric traits have low positive values. The Mantel test, often used to compare matrices, produces incorrect results, since it is insensitive to the relationship between the value of the correlation coefficient and its stability: the lower the value of the correlation coefficient, the higher its random variability. At the same time, averaged correlation matrix based on individual data is similar to the averaged correlation matrices based on worldwide data used by Russian anthropologists at present. The usefullness of averaged correlation matrices for intergroup comparisons has been tested. It was also shown that the use of the averaged matrix for calculating the Mahalanobis distances produces results comparable to the calculations based on individual data. The analyzed samples can represent populations both from the local settlements and territorial communities. That was confirmed by the results of a series of tests for the Eastern European cranial samples, which were not used in the calculation of the averaged matrix. In conclusion some biases in the analysis of correlation coefficients caused by false ideas about their properties are considered.

scholarly journals Pengaruh Kompetensi Manajerial Kepala Sekolah dan Budaya Kerja Terhadap Disiplin Guru di SD Islam Al-Ikhlas Cipete Jakarta Selatan

2020 ◽  
Vol 3 (1) ◽  
pp. 17
Author(s):  
Lia Riyani
Keyword(s):  
Correlation Coefficient ◽  
Positive Relationship ◽  
Correlation Coefficients ◽  
Coefficient Of Determination ◽  
Survey Method ◽  
Work Culture ◽  
Simple Correlation ◽  
Managerial Competencies ◽  
Determination Coefficient ◽  
Multiple Correlation Coefficients

This study aims to determine and examine empirical data related to the influence of principals' managerial competencies and work culture on teacher discipline separately or simultaneously. In this study, the authors used a survey method with a correlational and regression approach to the quantitative data obtained from the object of research, namely teachers at SD Islam Al-Ikhlas Cipete South Jakarta. The sample of this study was 66 respondents from 66 populations. The data collection is done by using questionnaire / questionnaire techniques, observation, and documentation. The type of analysis used is the regression analysis which is described descriptively. The results of this study are: 1) There is a positive relationship, which is quite strong and significant between principals' managerial competencies and teacher discipline, which is shown by a simple correlation coefficient (ry.1) is 0.408 (the correlation is quite strong) and the determination coefficient (R2) = 0.166, which means that Principal's managerial competence contributes to teacher discipline by 16.6% and the remaining 84.4% is determined by other factors. 2) There is a positive relationship, quite strong and significant between work culture and teacher discipline, which is shown by a simple correlation coefficient (ry.2) is 0.321 (the correlation is quite strong) and the determination coefficient (R2) = 0.103, which means that work culture provides contribution to teacher discipline is 10.3% and the remaining 89.7% is determined by other factors. 3) There is a positive, strong and significant relationship between principals' managerial competencies and work culture together with teacher discipline, which is indicated by multiple correlation coefficients (Ry. 1.2) is 0.470 (quite strong correlation) and coefficient of determination (R2) = 0.221, which means that principals' managerial competencies and work culture together contribute to teacher discipline by 22.1% and the rest 77.9% is determined by other factors.

scholarly journals Bal Verimini Etkileyen Bazı Faktörlerin Path Analizi Yöntemi ile İncelenmesi

2016 ◽  
Vol 4 (10) ◽  
pp. 903 ◽  
Author(s):  
Melis Çelik Güney ◽  
Ulviye Kumova ◽  
Gökhan Tamer Kayaalp
Keyword(s):  
Correlation Coefficient ◽  
Indirect Effects ◽  
Partial Correlation ◽  
Path Coefficient ◽  
Direct And Indirect Effects ◽  
Animal Science ◽  
Pollen Collection ◽  
Brood Rearing ◽  
Partial Correlation Coefficient ◽  
Honey Yield

Path analysis is determinate that relationships among variables by using correlation coefficient, partial correlation coefficient and path coefficient. In this study, direct and indirect effects of honey yield between brood rearing area, flight activity, pollen collection, nectar collection and cleaning power were examined with these coefficients. Data which taken from C.U. Faculty of Agriculture, Department of Animal Science, Beekeeping Business were used. In the end of this research, the effect of brood rearing area on honey yield was found significant. In the colonies, brood rearing area has the highest direct effect of honey yield. Nectar collection has the highest indirect effect.

scholarly journals Use of S. Wright’s path coefficient method for statistical analysis of interrelated traits in rice

2019 ◽  
Vol 23 (4) ◽  
pp. 430-438 ◽  
Author(s):  
D. P. Palamarchuk ◽  
M. R. Kozachenko ◽  
S. I. Sviatchenko
Keyword(s):  
Path Analysis ◽  
Indirect Effects ◽  
Pair Correlation ◽  
Correlation Coefficients ◽  
Plant Productivity ◽  
Grain Weight ◽  
Direct And Indirect Effects ◽  
Spikelet Number ◽  
Rice Varieties ◽  
Panicle Weight

S. Wright’s analysis of plant productivity is of great current interest. The research objective was to determine the pair correlation coefficients and S. Wright’s path coefficients for rice varieties and, on their basis, to identify the contribution of each of them to the plant productivity. Ten rice varieties were taken as the test material. The experiments were conducted in the irrigated experimental field of theInstituteofRiceof the National Academy of Agrarian Sciences ofUkrainein 2013, 2014 and 2016. Seeds were sown with an SKS-6A manual seeder; the seeding rate was 7.0 mln germinable seeds per hectare. The predecessor was alfalfa. The plot area was5 m2; the sowing distance was15 cm. The plants were analyzed for the following traits: plant productivity (grain weight), panicle weight, grain weight from side stems, productive tillering capacity, grain number per panicle, spikelet number per panicle, 1000-grain weight, grain weight per panicle, plant height, panicle length and density, empty spikelet number per panicle, and incidence of blind seed disease. Pair correlation coefficients were determined by B.A. Dospekhov’s method; path analysis, by S. Wright’s method. The correlations of productivity with 12 quantitative traits of rice were determined: the correlation was close with the grain weight from side stems and medium with the panicle weight and with the grain weight per panicle. Path analysis of the plant productivity established that the correlations of plant traits with the productivity depended both on direct and indirect effects of each trait on the productivity. The relative contribution of each of the studied 12 traits to the rice productivity was determined; both direct and indirect effects of their interactions with other traits were evaluated. This made it possible to discover causes and consequences of interrelations between the traits and, as a result, to choose valuable-for-selection traits, such as panicle weight and productive tillering capacity, which had the greatest direct effects on the productivity and significant correlations with it. 

Direct and indirect effects of dynamic social impact

2005 ◽  
Author(s):  
Dana M. Binder ◽  
Martin J. Bourgeois ◽  
Christine M. Shea Adams
Keyword(s):  
Indirect Effects ◽  
Social Impact ◽  
Direct And Indirect Effects
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