scholarly journals Sets of Environmentally Responsible Behaviors Among Residents of New Jersey

2019 ◽  
Vol 14 (3) ◽  
pp. 358-375
Author(s):  
Daniel George Clark ◽  
Rebecca Jordan
Keyword(s):  
New Jersey ◽  
Correlation Analysis ◽  
Canonical Correlation ◽  
Demographic Information ◽  
Human Behaviors ◽  
Energy Expenditures ◽  
Environmentally Responsible ◽  
Generalized Canonical Correlation Analysis ◽  
Environmentally Responsible Behaviors ◽  
Responsible Behaviors

There are many challenges facing humanity and the degradation of resources and natural spaces. One avenue for approaching these issues is through attempting to change human behaviors. Drawing on Stern’s Value-Behavior-Norm theory, we sought out to test the idea that these Environmentally Responsible Behaviors (ERBs) fell into well-established sets. In this research, we developed questionnaire that surveyed 290 residents on Central New Jersey. The questions included demographic information, as well as items gauging the type and extent of respondents’ engagement in ERBs. We used generalized canonical correlation analysis in order to sort the types of behaviors that respondents engaged in to distinct groups. The ERBs sorted into 3 canonical correlation variables that account for 53.7% of the variation in the data. Twenty-five ERBs that loaded highly on at least one of the three canonical correlation variables. The ERBs sorted into 3 groups that did not follow the expected pattern based on Stern’s research. Instead into three other groups suggesting alternative ways of conceptualizing pro-environmental behavior in this population. We found that ERBs tended to sort into those related to energy expenditures, identity as an environmentalist, and impact-oriented ERBs. This research helps to foster a greater understanding of individuals’ engagement in Environmentally Responsible Behaviors.

Green Building, Green Behavior? An Analysis of Building Characteristics that Support Environmentally Responsible Behaviors

2020 ◽  
pp. 001391652094260
Author(s):  
Erin M. Hamilton
Keyword(s):  
Online Survey ◽  
Green Building ◽  
Built Environments ◽  
Sustainable Buildings ◽  
Natural Materials ◽  
Environmentally Responsible ◽  
Building Characteristics ◽  
Green Behavior ◽  
Environmentally Responsible Behaviors ◽  
Responsible Behaviors

This study examines the environmentally responsible behaviors (ERBs) of undergraduates ( n = 575). ERBs were measured in an online survey and the influence of situational context on behavior was explored at two scales: 1) green versus non-green building and 2) building characteristics. The Positive Sustainable Built Environments model was used to analyze three building characteristics: Prime, Permit, and Invite. Prime refers to characteristics that prepare occupants to adopt ERBs via communicating a sustainable ethos or restoring attentional capacity (e.g., use of natural materials and views to nature). Permit refers to features that allow occupants to conserve resources (e.g., operable light switches). Invite pertains to features that explicitly encourage ERBs (e.g., signage prompting occupants to turn off lights). Regression results demonstrated that living in a green building had no significant impact on ERBs. However, the Prime and Invite building characteristics significantly predicted improved Energy, Water, and Materials conservation. Results yield implications for designers seeking to create sustainable buildings that promote ERBs.

Efficient and Distributed Generalized Canonical Correlation Analysis for Big Multiview Data

2019 ◽  
Vol 31 (12) ◽  
pp. 2304-2318 ◽  
Author(s):  
Xiao Fu ◽  
Kejun Huang ◽  
Evangelos E. Papalexakis ◽  
Hyun Ah Song ◽  
Partha Talukdar ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Generalized Canonical Correlation Analysis

scholarly journals Variable selection for generalized canonical correlation analysis

Biostatistics ◽  
2014 ◽  
Vol 15 (3) ◽  
pp. 569-583 ◽  
Author(s):  
A. Tenenhaus ◽  
C. Philippe ◽  
V. Guillemot ◽  
K.-A. Le Cao ◽  
J. Grill ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Variable Selection ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Generalized Canonical Correlation Analysis ◽  
Selection For

scholarly journals Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration

2019 ◽  
Vol 21 (6) ◽  
pp. 2011-2030 ◽  
Author(s):  
Morgane Pierre-Jean ◽  
Jean-François Deleuze ◽  
Edith Le Floch ◽  
Florence Mauger
Keyword(s):  
Correlation Analysis ◽  
Variable Selection ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Molecular Data ◽  
Heterogeneous Data ◽  
Data Availability ◽  
Omics Data ◽  
Generalized Canonical Correlation Analysis ◽  
Unsupervised Methods

Abstract Recent advances in NGS sequencing, microarrays and mass spectrometry for omics data production have enabled the generation and collection of different modalities of high-dimensional molecular data. The integration of multiple omics datasets is a statistical challenge, due to the limited number of individuals, the high number of variables and the heterogeneity of the datasets to integrate. Recently, a lot of tools have been developed to solve the problem of integrating omics data including canonical correlation analysis, matrix factorization and SM. These commonly used techniques aim to analyze simultaneously two or more types of omics. In this article, we compare a panel of 13 unsupervised methods based on these different approaches to integrate various types of multi-omics datasets: iClusterPlus, regularized generalized canonical correlation analysis, sparse generalized canonical correlation analysis, multiple co-inertia analysis (MCIA), integrative-NMF (intNMF), SNF, MoCluster, mixKernel, CIMLR, LRAcluster, ConsensusClustering, PINSPlus and multi-omics factor analysis (MOFA). We evaluate the ability of the methods to recover the subgroups and the variables that drive the clustering on eight benchmarks of simulation. MOFA does not provide any results on these benchmarks. For clustering, SNF, MoCluster, CIMLR, LRAcluster, ConsensusClustering and intNMF provide the best results. For variable selection, MoCluster outperforms the others. However, the performance of the methods seems to depend on the heterogeneity of the datasets (especially for MCIA, intNMF and iClusterPlus). Finally, we apply the methods on three real studies with heterogeneous data and various phenotypes. We conclude that MoCluster is the best method to analyze these omics data. Availability: An R package named CrIMMix is available on GitHub at https://github.com/CNRGH/crimmix to reproduce all the results of this article.

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