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

  • Juan C. Bou [1] ; Albert Satorra [2] [3]
    1. [1] Universitat Jaume I

      Universitat Jaume I

      Castellón, España

    2. [2] Universitat Pompeu Fabra

      Universitat Pompeu Fabra

      Barcelona, España

    3. [3] BI Norwegian Business School

      BI Norwegian Business School

      Noruega

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  • Localización: Business Research Quarterly, ISSN 2340-9444, ISSN-e 2340-9436, Vol. 22, Nº. 4, 2019, págs. 275-293
  • Idioma: inglés
  • DOI: 10.1016/j.brq.2018.10.001
  • Títulos paralelos:
    • Análisis de datos exploratorios multivariados para grandes bases de datos: una aplicación para modelar la innovación de las empresas utilizando datos CIS
  • Enlaces
  • Resumen
    • español

      Este artículo sostiene que, cuando se usa una base de datos grande, los investigadores organizacionales se beneficiarían del uso de análisis de datos exploratorios multivariados específicos (MEDA) antes de realizar modelos estadísticos. Cuestiones como la representatividad de la base de datos en todos los dominios (países o sectores), la evaluación de la confusión entre las covariables categóricas, los datos faltantes, la reducción de dimensiones para producir indicadores de rendimiento y / o solucionar problemas de multicolinealidad son abordados por MEDA específico. El MEDA propuesto se aplica a los datos de la Encuesta de innovación comunitaria (CIS), una gran base de datos que se utiliza comúnmente para analizar las actividades de innovación de las empresas, antes de ajustar los modelos de regresión logit y Tobit ordenados. A lo largo del documento se propone un conjunto de prácticas recomendadas que involucran a MEDA.

    • English

      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 multi-collinearity 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.

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