UNCORRECTED PROOF 191533 Chapter ID 4 October 19, 2010 Time: 08:03pm spr-t1-v1.7 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 Chapter 4 Structural Equation Models and Student Evaluation of Teaching: A PLS Path Modeling Study Simona Balzano and Laura Trinchera 4.1 Introduction In Italian universities, teaching evaluation is in part based on students judgments concerning aspects related to courses and considered of preeminent interest for uni- versity management. A questionnaire is generally used to collect such data. The students judgments are expressed as a score on an ordinal scale. Even if a synthetic measure of quality is required, there is no single methodolog- ical solution for aggregating individual scores. Until now several approaches have been proposed in order to define a synthetic measure of teaching quality by using student evaluations, see among others [1, 5, 18]. A possible solution is to use Structural Equation Models (SEM) [3, 14] that are used for describing and estimating conceptual structures where some latent vari- ables, linked by linear relationships, are measured by sets of manifest variables. A double level of relationships characterizes each SEM: the first involves relation- ships among the latent variables (structural model), while the other considers the links between each latent variable and its own block of manifest variables (mea- surement model). Given that both the quality of teaching and student satisfaction cannot be observed directly but can be measured through several real indicators, they can be treated as latent variables. SEM applications in both evaluation and teaching quality measurement have been widely used [6, 11, 12, 15, 16]. Several techniques can be used to estimate model parameters in SEMs, which can be grouped under two different approaches. The first is the so-called covariance- based approach, based on the search for the best parameters in reconstructing the observed covariance matrix of manifest variables. A number of estimation tech- niques are used to estimate model parameters, including the maximum likelihood S. Balzano (B) Dipartimento di Scienze Economiche, Università degli Studi di Cassino, 03043 Cassino, Italy e-mail: s.balzano@unicas.it M. Attanasio, V. Capursi (eds.), Statistical Methods for the Evaluation of University Systems, Contributions to Statistics, DOI 10.1007/978-3-7908-2375-2_4, C  Springer-Verlag Berlin Heidelberg 2011 55