Fibtele
fibtele
A data frame with 147 observations on the following 35
variables. The first ten variables are segmentation variables.
The rest of the variables refer to five latent concepts: 1) Image
=Image, 2)
Qual.spec
=Specific Quality, 3) Qual.gen
=Generic Quality, 4)
Value
=Value, 5) Satis
=Satisfaction.
Variables description
Image
: Generic students perception of ICT schools: (internationally recognized,
ranges of courses, leader in research).
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.
Manifest variables description
ima1
MV:It is the best college to study IE
ima2
MV:It is internationally recognized
ima3
MV:It has a wide range of courses
ima4
MV:The Professors are good
ima5
MV:Facilities and equipment are good
ima6
MV:It is leader in research
ima7
MV:It is well regarded by the companies
ima8
MV:It is oriented to new needs and technologies
quaf1
MV:Basic skills
quaf2
MV:Specific Technic skills
quaf3
MV:Applied skills
qutr1
MV:Achieved abilities in solving problem
qutr2
MV:Training in business management
qutr3
MV:The written and oral communication skills
qutr4
MV:Planning and time management acquired
qutr5
MV:Team-work skills
val1
MV:It has allowed me to find a well paid job
val2
MV:I have good prospectives in improvement and promotion
val3
MV:It has allowed me to find a job that motivates me
val4
MV:The training received is the basis on which I will develope my career
sat1
MV:I am satisfied with the training received
sat2
MV:I am satisfied with my current situation
sat3
MV:I think I will have a good career
sat4
MV:What do you think is the prestige of your work
Segmentation Variables description
Career
a factor with levels EI
ETS
TEL
Gender
a factor with levels female
male
Age
a factor with levels 25-26years
27-28years
29-30years
31years+
Studying
a factor with levels no.stud
yes.stud
Contract
a factor with levels fix.cont
other.cont
temp.cont
Salary
a factor with levels 18k
>45k
25k
35k
45k
Firmtype
a factor with levels priva
publi
Accgrade
a factor with levels 7-8accnote
accnote<7
accnote>8
Grade
a factor with levels <6.5note
>7.5note
6.5-7note
7-7.5note
Startwork
a factor with levels after.grad
befor.grad
Lamberti, G. (2014) Modeling with Heterogeneity. PhD Dissertation.