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Biplot Methods allow for the simultaneous representation of individuals andvariables of a data matrix. For Binary or Nominal data, Logistic biplots havebeen recently developed to extend the classical linear representations forcontinuous data. When data are ordinal, linear, binary or nominal logisticbiplots are not adequate and techniques as Categorical Principal ComponentAnalysis (CATPCA) or Item Response Theory (IRT) for ordinal items should beused instead.  In this paper we extend the Biplot to ordinal data. The resulting method istermed Ordinal Logistic Biplot (OLB). Row scores are computed to have ordinallogistic responses along the dimensions and column parameters produce logisticresponse surfaces that, projected onto the space spanned by the row scores,define a linear biplot. A proportional odds model is used, obtaining amultidimensional model known as graded response model in the Item ResponseTheory literature. We study the geometry of such a representation and constructcomputational algorithms for the estimation of parameters and the calculationof prediction directions. Ordinal Logistic Biplots extend both CATPCA and IRTin the sense that gives a graphical representation for IRT similar to thebiplot for CATPCA.  The main theoretical results are applied to the study of job satisfaction ofdoctorate (PhD) holders in Spain. Holders of doctorate degrees or otherresearch qualifications are crucial to the creation, commercialization anddissemination of knowledge and to innovation. The proposed methods are used toextract useful information from the Spanish data from the international 'Surveyon the careers of doctorate holders (CDH)', jointly carried out Eurostat, theOrganisation for Economic Co-operation and Development (OECD) and UNESCO'sInstitute for Statistics (UIS).

Logistic Biplots for Ordinal Data with an Application to Job Satisfaction of Doctorate Degree Holders in Spain