Multivariate exploratory data analysis for large databases: An application to modelling firms’ innovation using CIS data
Metadata
Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/8645
comunitat-uji-handle3:10234/8646
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INVESTIGACIONMetadata
Title
Multivariate exploratory data analysis for large databases: An application to modelling firms’ innovation using CIS dataDate
2018Publisher
ElsevierISSN
2340-9436Bibliographic 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, 2018Type
info:eu-repo/semantics/articlePublisher version
https://www.sciencedirect.com/science/article/pii/S2340943618301695#!Version
info:eu-repo/semantics/publishedVersionSubject
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 ... [+]
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. [-]
Is part of
BRQ Business Research Quarterly, 2018Investigation project
Spanish MEC Grants: Grant Number ECO2015-66671-P (MINECO/FEDER), and ECO2014-59885-P; Generalitat Valenciana: Grant Number BEST/2018/209Rights
info:eu-repo/semantics/openAccess
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- EMP_Articles [463]
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