business research unit (unide-iul ...€¦ · business research unit (unide-iul) /...

Business Research Unit (UNIDE-IUL) / Instituto Universitário de Lisboa

Ed. ISCTE-IUL, Av. Forças Armadas

1649-026 Lisbon – Portugal

Phone: 210 464 019

E-mail: [email protected]

Internet: http://www.bru-unide.iscte.pt/We acknowledge financial support from

FCT – Fundação para a Ciência e a Tecnologia

FCT Strategic Project UI 315

PEst-OE/EGE/UI03152

A Grade of Membership A Grade of Membership A Grade of Membership A Grade of Membership

Representation of Representation of Representation of Representation of

REFLEX Project DataREFLEX Project DataREFLEX Project DataREFLEX Project Data

Abdul Suleman, ISCTE-IUL, BRU, Portugal

Fátima Suleman, ISCTE-IUL, DINAMIA-CET, Portugal

DEHEMS Second International Conference,

Slovenia, Ljubljana, 27 & 28 of September, 2012


PEst-OE/EGE/UI03153

Motivation

— One of the goals of REFLEX Project is to examine whether higher

education institutions in Europe are up to the task of equipping

graduates with the competencies needed to meet the demands the

modern knowledge society places on higher education graduates

(Allen and von der Velden, 2011);

— Graduates from 15 European countries as well as from Japan

were asked to rate their own level of competence in different

dimensions of competencies.


PEst-OE/EGE/UI03154

Our Objective

— Quantification of the overall competence of individuals, based

on a list of 19 items of competence used in self-assessment

(dimension H of the REFLEX Questionnaire);

— Given that, how competencies are, on average, distributed

among different countries that have participated in the project?


PEst-OE/EGE/UI03155

The Data

— Dimension H of the master questionnaire referred to as

Competencies;

— Focus on the skills acquired at higher education institutions from

individuals’ own perspective;

— List of 19 dimensions of competencies (from a to s), measured in

a 7 points Likert scale: 1: very low; 7: very high (intermediate values

are not labelled);


PEst-OE/EGE/UI03156

REFLEX Master Questionnaire: H - Competencies

Item Description

a Mastery of your own field or discipline

b Knowledge of other fields or disciplines

c Analytical thinking

d Ability to rapidly acquire new knowledge

e Ability to negotiate effectively

.... ..........

s Ability to write and speak in a foreign language


PEst-OE/EGE/UI03157

Data Details

— 16 countries participated in the project; we received data from

14 countries only (Sweden and Switzerland data were not included

in the database provided to us);

— Initial sample size: 34,347;

— Missing data and the data from the graduates who answered to

have not even spent one hour a week studying were omitted from

the analysis (5,002 cases in the total);

— Final sample size: 29,345;

— The question is: how to analyse these data?


PEst-OE/EGE/UI03158

Statistical Methodology

— Data reduction due to correlation among variables (items);

— PCA is the common method but it is designed for real-valued

data;

— REFLEX data are categorical;

— Discrete version of PCA: GoM – Grade of Membership analysis to

identify competence related groups or fuzzy clusters;

—What is then a fuzzy clustering?


PEst-OE/EGE/UI03159

Fuzzy Clustering


PEst-OE/EGE/UI031510

Example of a Fuzzy Clustering (2 clusters)



Quantification

— Suppose the woman overall competence is worth 100 and the

one of the fish is worth 10. What is then the overall competence of

a mermaid? It depends on its grades of membership.

— (0.1; 0.9) -> 100 x 0.1 + 10 x 0.9 = 10 + 9 = 19;

— (0.5; 0.5) -> 100 x 0.5 + 10 x 0.5 = 50 + 5 = 55;

— (0.8; 0.2) -> 100 x 0.8 + 10 x 0.2 = 80 + 2 = 82.

— The key issue is to quantify each fuzzy cluster.



Example of a Fuzzy Clustering (3 clusters)

A1 (Red)

A2 (Green)A3 (Blue)

Examples:

= (1, 0, 0)

= (0, 1, 0)

= (0.5, 0.5, 0)

= (0.4, 0, 0.6)

= (1/3, 1/3, 1/3)

= (1/2, 1/4, 1/4)

= (1/5, 2/5, 2/5)



Application to REFLEX Data

— GoM model devised by Woodbury and Clive (1974);

— Allows the estimation of two kind of parameters:

—How likely a particular outcome is in each fuzzy cluster;

—For each individual: the vector of grades of membership

accounting for the degree of belongingness in fuzzy clusters.

— The number of fuzzy cluster was determined by a likelihood ratio

test which leads to 4 fuzzy clusters.



Empirical Findings

ItemCluster 1

(Low)

Cluster 2

(Medium)

Cluster 3

(High)

Cluster 4

(Very high)

a = 1 3.64 4.80 5.80 6.63

b = 2 2.97 4.12 5.03 5.69

c = 3 3.49 4.74 5.85 6.86

d = 4 3.85 5.17 6.00 7.00

e = 5 2.78 4.33 5.63 6.58

.... .... .... .... ....

s =19 2.26 4.04 4.75 5.91



Average Competencies in Fuzzy Clusters

Cluster 1

(Low)

Cluster 2

(Medium)

Cluster 3

(High)

Cluster 4

(Very high)

Average 3.28 4.74 5.78 6.74

Normalisation 0.38 0.62 0.80 0.96



Distribution of Individual Overall Competence



Comparing Countries by Competence



Duncan Post-hoc Analysis

Country N Group for α = 0.10

1 2 3 4 5 6 7 8 9

Japan 2148 .5448

France 1410

.6510

Finland 2313

.6727

Norway 1943

.6822

Estonia 815

.6853

Belgium 1201

.6880

Spain 3455

.6891 .6891

Netherlands 3003

.6958

Italy 2201

.7037

UK 1428

.7121

Czech 5896

.7137

Portugal 574

.7373

Germany 1453

.7419

Austria 1505

.7657

THANK YOU

Business Research Unit (UNIDE-IUL) / Instituto Universitário de Lisboa

Ed. ISCTE-IUL, Av. Forças Armadas

1649-026 Lisbon – Portugal

Phone: 210 464 019

E-mail: [email protected]

Internet: http://www.bru-unide.iscte.pt/

business research unit (unide-iul ...€¦ · business research unit (unide-iul) /...

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