Fibtele

Description

Fibtele

Usage

fibtele

Format

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

• ima1MV:It is the best college to study IE

• ima2MV:It is internationally recognized

• ima3MV:It has a wide range of courses

• ima4MV:The Professors are good

• ima5MV:Facilities and equipment are good

• ima6MV:It is leader in research

• ima7MV:It is well regarded by the companies

• ima8MV:It is oriented to new needs and technologies

• quaf1MV:Basic skills

• quaf2MV:Specific Technic skills

• quaf3MV:Applied skills

• qutr1MV:Achieved abilities in solving problem

• qutr2MV:Training in business management

• qutr3MV:The written and oral communication skills

• qutr4MV:Planning and time management acquired

• qutr5MV:Team-work skills

• val1MV:It has allowed me to find a well paid job

• val2MV:I have good prospectives in improvement and promotion

• val3MV:It has allowed me to find a job that motivates me

• val4MV:The training received is the basis on which I will develope my career

• sat1MV:I am satisfied with the training received

• sat2MV:I am satisfied with my current situation

• sat3MV:I think I will have a good career

• sat4MV:What do you think is the prestige of your work

Segmentation Variables description

• Careera factor with levels EI ETS TEL

• Gendera factor with levels female male

• Agea factor with levels 25-26years 27-28years 29-30years 31years+

• Studyinga factor with levels no.stud yes.stud

• Contract a factor with levels fix.cont other.cont temp.cont

• Salarya factor with levels 18k >45k 25k 35k 45k

• Firmtypea factor with levels priva publi

• Accgradea factor with levels 7-8accnote accnote<7 accnote>8

• Gradea factor with levels <6.5note >7.5note 6.5-7note 7-7.5note

• Startworka factor with levels after.grad befor.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.

Results