The role of discriminant analysis in the refinement of customer satisfaction assessment

• Autor(a) principal: Verdessi,BD
• Data de Publicação: 2000
• Outros Autores: Jara,G, Fuentes,R, Gonzalez,JC, Espejo,F, Azevedo,AC de
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista de Saúde Pública
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0034-89102000000600010
• topic: Discriminant analysis; Customer satisfaction; Patient satisfaction; Total quality management; Quality assurance in health care; Quality assurance health care; Quality control; Quality of health care

OBJECTIVE: To test discriminant analysis as a method of turning the information of a routine customer satisfaction survey (CSS) into a more accurate decision-making tool. METHODS: A 7-question, 10-multiple choice, self-applied questionnaire was used to study a sample of patients seen in two outpatient care units in Valparaíso, Chile, one of primary care (n=100) and the other of secondary care (n=249). Two cutting points were considered in the dependent variable (final satisfaction score): satisfied versus unsatisfied, and very satisfied versus all others. Results were compared with empirical measures (proportion of satisfied individuals, proportion of unsatisfied individuals and size of the median). RESULTS: The response rate was very high, over 97.0% in both units. A new variable, medical attention, was revealed, as explaining satisfaction at the primary care unit. The proportion of the total variability explained by the model was very high (over 99.4%) in both units, when comparing satisfied with unsatisfied customers. In the analysis of very satisfied versus all other customers, significant relationship was identified only in the case of the primary care unit, which explained a small proportion of the variability (41.9%). CONCLUSIONS: Discriminant analysis identified relationships not revealed by the previous analysis. It provided information about the proportion of the variability explained by the model. It identified non-significant relationships suggested by empirical analysis (e.g. the case of the relation very satisfied versus others in the secondary care unit). It measured the contribution of each independent variable to the explanation of the variation of the dependent one.