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Data to assess the effect of moral identity on green consumption tendency.

ABSTRACT: This Data in Brief article is for Study 2 in the manuscript # JEVP-2018-140 ("The Impact of Moral Identity on Consumers' Green Consumption Tendency: The Role of Perceived Responsibility for Environmental Damage"). It examines the effect of moral identity on green consumption tendency. The data was collected using online experiment. Participants were randomly assigned to moral-identity-activated condition and moral-identity-not-activated condition. The choice of eco-friendly product relative to conventional counterpart was measured. 162 Americans took part in the experiment. Data was analyzed employing SPSS. Logistic regression analysis was used as statistical tool of analysis.

SUBMITTER: Wu B

PROVIDER: S-EPMC6197145 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Data to assess the effect of moral identity on green consumption tendency.

Wu Bo B Yang Zhiyong Z

Data in brief 20181002

This Data in Brief article is for Study 2 in the manuscript # JEVP-2018-140 ("The Impact of Moral Identity on Consumers' Green Consumption Tendency: The Role of Perceived Responsibility for Environmental Damage"). It examines the effect of moral identity on green consumption tendency. The data was collected using online experiment. Participants were randomly assigned to moral-identity-activated condition and moral-identity-not-activated condition. The choice of eco-friendly product relative to c ...[more]

PMID: 30364504

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Project description:Sorting spikes from extracellular recording into clusters associated with distinct single units (putative neurons) is a fundamental step in analyzing neuronal populations. Such spike sorting is intrinsically unsupervised, as the number of neurons are not known a priori. Therefor, any spike sorting is an unsupervised learning problem that requires either of the two approaches: specification of a fixed value k for the number of clusters to seek, or generation of candidate partitions for several possible values of c, followed by selection of a best candidate based on various post-clustering validation criteria. In this paper, we investigate the first approach and evaluate the utility of several methods for providing lower dimensional visualization of the cluster structure and on subsequent spike clustering. We also introduce a visualization technique called improved visual assessment of cluster tendency (iVAT) to estimate possible cluster structures in data without the need for dimensionality reduction. Experimental results are conducted on two datasets with ground truth labels. In data with a relatively small number of clusters, iVAT is beneficial in estimating the number of clusters to inform the initialization of clustering algorithms. With larger numbers of clusters, iVAT gives a useful estimate of the coarse cluster structure but sometimes fails to indicate the presumptive number of clusters. We show that noise associated with recording extracellular neuronal potentials can disrupt computational clustering schemes, highlighting the benefit of probabilistic clustering models. Our results show that t-Distributed Stochastic Neighbor Embedding (t-SNE) provides representations of the data that yield more accurate visualization of potential cluster structure to inform the clustering stage. Moreover, The clusters obtained using t-SNE features were more reliable than the clusters obtained using the other methods, which indicates that t-SNE can potentially be used for both visualization and to extract features to be used by any clustering algorithm.

Dataset Information

Data to assess the effect of moral identity on green consumption tendency.

Publications

Data to assess the effect of moral identity on green consumption tendency.

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