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

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), a...

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Bibliographic Details
Published inBRQ Business Research Quarterly Vol. 22; no. 4; pp. 275 - 293
Main Authors Bou-Llusar, Juan Carlos, Satorra, Albert
Format Journal Article
LanguageEnglish
Published Barcelona Elsevier España 01.10.2019
SAGE Publications
Subjects
Community Innovation Survey (CIS)
Dimension reduction
Innovation
MAR and MCAR
MEDA
Missing data
Multivariate analysis
OLS
ordered logistic and Tobit regression
MEDA
Missing data
Dimension reduction
Innovation
OLS
Community Innovation Survey (CIS)
Multivariate analysis
MAR and MCAR
ordered logistic and Tobit regression
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Summary: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.
ISSN:2340-9436
2340-9444
DOI:10.1016/j.brq.2018.10.001