Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/261867 
Year of Publication: 
2019
Citation: 
[Journal:] BRQ Business Research Quarterly [ISSN:] 2340-9436 [Volume:] 22 [Issue:] 4 [Publisher:] Elsevier España [Place:] Barcelona [Year:] 2019 [Pages:] 275-293
Publisher: 
Elsevier España, Barcelona
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.
Subjects: 
Community Innovation Survey (CIS)
Dimension reduction
Innovation
MAR and MCAR
MEDA
Missing data
Multivariate analysis
OLS
ordered logistic and Tobit regression
JEL: 
M10
C18
C24
C55
O30
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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