Multivariate exploratory data analysis for large databases: An application to modelling firms’ innovation using CIS data

Mostra el registre complet Registre parcial de l'ítem

• dc.contributor.author Bou, Juan Carlos
• dc.contributor.author Satorra, Albert
• dc.date.accessioned 2020-05-25T08:45:55Z
• dc.date.available 2020-05-25T08:45:55Z
• dc.date.issued 2019
• dc.description.abstract This paper argues that, when using a large database, organizational researchers would benefit from the use of specific multivariate exploratory data analysis (MEDA) before performing statistical modelling. Issues such as the representativeness of the database across domains (countries or sectors), assessment of confounding among categorical covariates, missing data, dimension reduction to produce performance indicators and/or remedy multicollinearity problems are addressed by specific MEDA. The proposed MEDA is applied to data from the Community Innovation Survey (CIS), a large database commonly used to analyse firms’ innovation activities, prior to fitting ordered logit and Tobit regression models. A set of recommended practices involving MEDA are proposed throughout the paper.en
• dc.description.sponsorship This work was supported by the Spanish MEC Grants [Grant Number ECO2015-66671-P (MINECO/FEDER), and ECO2014-59885-P] and Generalitat Valenciana [Grant Number BEST/2018/209].
• dc.format.mimetype application/pdf
• dc.identifier.citation Bou JC, Satorra A. Multivariate exploratory data analysis for large databases: An application to modelling firms’ innovation using CIS data. Business Research Quarterly. 2019 Oct;22(4):275-93. DOI: 10.1016/j.brq.2018.10.001
• dc.identifier.doi http://dx.doi.org/10.1016/j.brq.2018.10.001
• dc.identifier.issn 2340-9436
• dc.identifier.uri http://hdl.handle.net/10230/44671
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof Business Research Quarterly. 2019 Oct;22(4):275-93
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/ECO2015-66671-P
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/ECO2014-59885-P
• dc.rights This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
• dc.subject.keyword Community Innovation Survey (CIS)en
• dc.subject.keyword MEDAen
• dc.subject.keyword Innovationen
• dc.subject.keyword Missing dataen
• dc.subject.keyword MAR and MCARen
• dc.subject.keyword Dimension reductionen
• dc.subject.keyword Multivariate analysisen
• dc.subject.keyword OLSen
• dc.subject.keyword Ordered logistic and Tobit regressionen
• dc.title Multivariate exploratory data analysis for large databases: An application to modelling firms’ innovation using CIS dataen
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion