Mostrar el registro sencillo del ítem
Multivariate exploratory data analysis for large databases: An application to modelling firms’ innovation using CIS data
dc.contributor.author | Bou-Llusar, Juan Carlos | |
dc.contributor.author | Satorra, Albert | |
dc.date.accessioned | 2019-04-02T07:28:55Z | |
dc.date.available | 2019-04-02T07:28:55Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | BOU-LLUSAR, Juan C.; SATORRA, Albert. Multivariate exploratory data analysis for large databases: An application to modelling firms’ innovation using CIS data. BRQ Business Research Quarterly, 2018 | ca_CA |
dc.identifier.issn | 2340-9436 | |
dc.identifier.uri | http://hdl.handle.net/10234/182121 | |
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. | ca_CA |
dc.format.extent | 19 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Elsevier | ca_CA |
dc.relation.isPartOf | BRQ Business Research Quarterly, 2018 | ca_CA |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Community Innovation Survey (CIS) | ca_CA |
dc.subject | MEDA | ca_CA |
dc.subject | innovation | ca_CA |
dc.subject | missing data | ca_CA |
dc.subject | MAR and MCAR | ca_CA |
dc.subject | dimension reduction | ca_CA |
dc.subject | multivariate analysis | ca_CA |
dc.subject | OLS | ca_CA |
dc.subject | ordered logistic and Tobit regression | ca_CA |
dc.title | Multivariate exploratory data analysis for large databases: An application to modelling firms’ innovation using CIS data | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1016/j.brq.2018.10.001 | |
dc.relation.projectID | Spanish MEC Grants: Grant Number ECO2015-66671-P (MINECO/FEDER), and ECO2014-59885-P; Generalitat Valenciana: Grant Number BEST/2018/209 | ca_CA |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://www.sciencedirect.com/science/article/pii/S2340943618301695#! | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
Ficheros en el ítem
Este ítem aparece en la(s) siguiente(s) colección(ones)
-
EMP_Articles [453]