Comparison of two statistical methods to identify groups in a student satisfaction survey

This work studies the main characteristics of the construction of Student Satisfaction in the undergraduate courses of the Facultad de Ciencias Económicas y de Administración from Universidad de la República, Uruguay, by using and comparing two multivariate data analysis techniques: Cluster Analysis and Latent Classes Analysis. Data used for the implementation presented in this work originate from a survey applied on a sample of undergraduate students in 2009. This questionnaire is structured in sets of questions: the first set contains variables which allow characterize students sociodemographically. On the other hand, variables from the ECSI (European Customer Satisfaction Index) model which shall be used to characterize Student Satisfaction are presented. The 6 observed/manifest variables considered as input for the construction and characterization of Student Satisfaction are: student expectations (E) when they begin studying in the institution, the image (I) they have of the institution, the quality of education received (CSA) and of services provided (CSF), the personal needs and desires with regards to College (ND), and the perceived value (VP). On the one hand, results presented arise from considering that there is an actual hidden/latent variable referred to Student Satisfaction and that this variable is defined from the interaction of the 6 manifest variables, by 4 latent classes. On the other hand, with reference to clusters, it is proposed to group students into 3 groups based on the analysis of results arising from clustering using Ward's method. Additionally, the comparison of results obtained using both techniques is presented.