A data frame with 147 observations on the following 18 variables. The first ten variables
are segmentation variables. The rest of the variables refer to five variables 1)
Image = Image, 2) Exp.spec = Specific Expectation, 3) Exp.gen = Generic Expectation,
4)Qual.spec = Specific Quality, 5) Qual.gen = Generic Quality, 6) Value = Value, 7)
Satis = Satisfaction.
Variables description
Image: Generic students perception of ICT schools: (internationally recognized,
ranges of courses, leader in research).
Exp.spec: Specific Expectation on specific skills (technic or applied skills).
Exp.gen: Generic Expectation on generic skills (abilities in problem solving,
communication skills).
Qual.spec: Perception about the achieved quality on the specific skills in the school.
Qual.gen: Perception about achieved quality on the generic skills in
the school (abilities in solving problem, communication skills).
Value: The advantage or profit that the alumni may draw from the school
degree (well paid job, motivated job, prospectives in improvement and promotion).
Satis: Degree of alumni satisfaction about the formation in school respect to
their actual work conditions.
Segmentation Variables description
Careera factor with levels EIETSTEL
Gendera factor with levels femalemale
Agea factor with levels 25-26years27-28years29-30years31years+
Studyinga factor with levels no.studyes.stud
Contract a factor with levels fix.contother.conttemp.cont
Salarya factor with levels 18k>45k25k35k45k
Firmtypea factor with levels privapubli
Accgradea factor with levels 7-8accnote accnote<7accnote>8
Gradea factor with levels <6.5note>7.5note6.5-7note7-7.5note
Startworka factor with levels after.gradbefor.grad
Source
Laboratory of Information Analysis and Modeling (LIAM).
Facultat de Informatica de Barcelona, Universitat Politecnica de Catalunya.
References
Lamberti, G. (2014) Modeling with Heterogeneity. PhD Dissertation.
providing 
.