Análise do índice de alocação de recursos financeiros: um estudo de caso na UFSM

• Ano de defesa: 2016
• Autor(a) principal: Huppes, Jean Cauê
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; BR; Engenharia de Produção; UFSM; Programa de Pós-Graduação em Engenharia de Produção
• Palavras-chave em Português: Índice aluno equivalente; Análise de cluster; Análise fatorial; Resource allocation index; Cluster analysis; Factor analysis; CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO
• Link de acesso: http://repositorio.ufsm.br/handle/1/8414

This research sought to identify the variables of greater impact in shaping the Student Equivalent Index Nfte (G), which is used for the allocation of resources in the eight learning centers of the Federal University of Santa Maria, RS, in order to describe through a set of variables related to the budget through the main instruments of planning and control of resources made available to the Brazilian public universities. The data have been obtained from the data processing center of the Federal University of Santa Maria, in the period from 2010 to 2013. The results show that the variables that contribute most to the formation of the (Nfte (G)), are number of Graduates (Ndi) and number of Freshmen (Ni). The largest courses (Nfte (G)) are: Medicine, dentistry, veterinary medicine, Civil Engineering, electrical engineering and mechanical engineering. The courses less contribute in the formation of (Nfte (G)) are: Music and Spanish Course. The courses in a second moment, was applied to cluster analysis, which identified with a high degree of similarity between the Ndi and Ni throughout the analysis period. It should be noted, too, that these variables are the only ones that can be directly modified by means of the interaction of public policies. During all years analyzed, the variable retention R was the most inconclusive, corresponding to the lowest average. Also it was found that the clusters presented the same structure of agglomeration. In order to verify the more similar courses, cluster analysis, considering them as variables. Over the years, the more disparate courses were the same presented in descriptive analysis. They are: Medicine, animal science, dentistry, Civil Engineering and mechanical engineering. The more similar courses were bachelor courses. It was noted that, in the four years examined, the subdivision of the groups was also.