Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/261867 
Erscheinungsjahr: 
2019
Quellenangabe: 
[Journal:] BRQ Business Research Quarterly [ISSN:] 2340-9436 [Volume:] 22 [Issue:] 4 [Publisher:] Elsevier España [Place:] Barcelona [Year:] 2019 [Pages:] 275-293
Verlag: 
Elsevier España, Barcelona
Zusammenfassung: 
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.
Schlagwörter: 
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 der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by-nc-nd Logo
Dokumentart: 
Article

Datei(en):
Datei
Größe
2.54 MB





Publikationen in EconStor sind urheberrechtlich geschützt.