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This presentation examines the results of a global survey on orchestras, using multiple correspondence analysis and hierarchical clustering to identify key factors affecting orchestra management and organization. The survey data includes variables related to wages, auditions, organizational variables, and more. The analysis reveals three main axes of differentiation and classifies orchestras into five distinct classes. The presentation provides insights into the impact of these factors on the size, commitment, and performance of orchestras worldwide.
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FIM Global Survey on Orchestraspresentation and results Colin Marchika, EHESS Scientific direction: P.M. Menger
Introduction A global approch 1) Multiple CorrespondenceAnalysis (MCA) discriminatingfactors graphicrepresentation 2) HierarchicalClustering creating classes 3) Discussing the meaning of the classes
Sample description (1) From 231 orchestras to 105 usefull questionnaires
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
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
Clustering : 5 classes or 3 classes Verysmall orchestras small Small orchestras « old » orchestras old Large orchestras (budget) large Large orchestras (« hardworkers »)
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.