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FIM Global Survey on Orchestras presentation and results Colin Marchika , EHESS

FIM Global Survey on Orchestras presentation and results Colin Marchika , EHESS Scientific direction: P.M. Menger. Introduction. A global approch 1) Multiple Correspondence Analysis (MCA)  discriminating factors  graphic representation 2) Hierarchical Clustering

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FIM Global Survey on Orchestras presentation and results Colin Marchika , EHESS

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  1. FIM Global Survey on Orchestraspresentation and results Colin Marchika, EHESS Scientific direction: P.M. Menger

  2. Introduction A global approch 1) Multiple CorrespondenceAnalysis (MCA)  discriminatingfactors  graphicrepresentation 2) HierarchicalClustering  creating classes 3) Discussing the meaning of the classes

  3. Sample description (1) From 231 orchestras to 105 usefull questionnaires

  4. Sample description (2)

  5. MCA - active variables Variables for managinghuman ressources : Wages-related variables : Number of wagecategories Differential in wagesbetweensoloists and tutti players Seniority – wagesincrease Audition-related variables : Proportion of orchestra membres on recruitment audition panels Proportion of union representatives on recruitment audition panels Existence of re-audition Organisational variables : Workinghours : Maximum number of workinghours per day Maximum number of workinghours per month Extra-orchestra activities : Autorisation for otheroccupationnalactivities Incentives for individualactivities

  6. MCA – axis description 3 main axes AXIS 1 : size of orchestras wageincrease (low vs high) Number of hours per day, per month Re-auditionning + budget, box office vs funding AXIS 2 : personnalcommitment wageincrease (veryhigh), differentiationof the soloist Involvment of musicians on audition panels AXIS 3 : commitment by salary vs commitment to the life of the orchestra

  7. MCA – factor plan

  8. MCA – nationalities in factor plan

  9. Clustering : 5 classes or 3 classes Verysmall orchestras small Small orchestras « old » orchestras old Large orchestras (budget) large Large orchestras (« hardworkers »)

  10. Clustering : classes in factor plan

  11. Clustering : describing the classes 1) with the help of actives MCA variables : managinghuman ressources & organisational variables 2)with the help of illustratives MCA variables : Size (budget, number of jobs, number of representation, institution, etc. 3) with all the others variables from the survey questionnaire : recordings, tours & travels, representationatwork place, health and safetyatwork, etc.

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