Faktor-Faktor Yang Mempengaruhi Pengungkapan Tanggung Jawab Sosial

Penelitian ini bertujuan untuk menentukan faktor-faktor yang mempengaruhi luasnya tingkat pengungkapan tanggung jawab sosial Perusahaan (Corporate Social Responsibility) dengan menguji pengaruh ukuran Perusahaan, profitabilitas, leverage, kepemilikan insti­tusional, ukuran dewan komisaris, ukuran dewan direksi, dan ukuran komite audit. Sampel yang digunakan adalah Perusahaan sektor pertambangan terdaftar di Bursa Efek Indonesia selama 2010-2012. Data diperoleh dari laporan keuangan auditan dan laporan tahunan serta laporan keberlanjutan (sustainability report) jika ada. Penelitian ini menggunakan pendekatan kuantitatif dengan analisis regresi linear berganda. Penelitian ini menunjukkan bahwa ukuran Perusahaan dan komite audit memiliki pengaruh positif terhadap peng­ungkapan tanggung jawab sosial. Tidak ditemukan bukti pengaruh profitabilitas, leverage, kepemilikan institusional, ukuran dewan komisaris, dan ukuran dewan direksi terhadap terhadap pengungkapan tanggung jawab sosial

The Last April

<p>Parameter settings of virtual datasets.</p

Weighted SNP Set Analysis in Genome-Wide Association Study

<div><p>Genome-wide association studies (GWAS) are popular for identifying genetic variants which are associated with disease risk. Many approaches have been proposed to test multiple single nucleotide polymorphisms (SNPs) in a region simultaneously which considering disadvantages of methods in single locus association analysis. Kernel machine based SNP set analysis is more powerful than single locus analysis, which borrows information from SNPs correlated with causal or tag SNPs. Four types of kernel machine functions and principal component based approach (PCA) were also compared. However, given the loss of power caused by low minor allele frequencies (MAF), we conducted an extension work on PCA and used a new method called weighted PCA (wPCA). Comparative analysis was performed for weighted principal component analysis (wPCA), logistic kernel machine based test (LKM) and principal component analysis (PCA) based on SNP set in the case of different minor allele frequencies (MAF) and linkage disequilibrium (LD) structures. We also applied the three methods to analyze two SNP sets extracted from a real GWAS dataset of non-small cell lung cancer in Han Chinese population. Simulation results show that when the MAF of the causal SNP is low, weighted principal component and weighted IBS are more powerful than PCA and other kernel machine functions at different LD structures and different numbers of causal SNPs. Application of the three methods to a real GWAS dataset indicates that wPCA and wIBS have better performance than the linear kernel, IBS kernel and PCA.</p></div

Test of Power for LKM, PCA and wPCA in Scenarios A4–A6.

<p>The plot shows the powers (y-axis) based on virtual datasets with single causal SNP of each method over the different LD and MAF structures (x-axis) with 20 SNPs. The first line of x-axis represents LD, and the bottom line is MAF.</p

Empirical type I error rates for LKM, PCA and wPCA in scenarios A1–A3.

<p>The plot shows the empirical type I error rates (y-axis) based on virtual datasets of each method over the different LD and MAF structures (x-axis) with 20 SNPs. The first line of x-axis represents LD, and the bottom line is MAF.</p

Parameter settings based on the <i>XRCC1</i> gene.

<p>Parameter settings based on the <i>XRCC1</i> gene.</p

Parameter settings based on the <i>CLPTM1L</i> gene.

<p>Parameter settings based on the <i>CLPTM1L</i> gene.</p

Empirical type I error rates for LKM, PCA and wPCA in Scenarios B1.

<p>Empirical type I error rates for LKM, PCA and wPCA in Scenarios B1.</p

Parameter settings of the <i>CLPTM1L</i> gene.

<p>Parameter settings of the <i>CLPTM1L</i> gene.</p

Results of four methods on the analysis of the SNP set from the GWAS study.

a<p>We extract the 1st PC to analyze.</p