A FACTOR ANALYSIS OF VARIABLES AFFECTING QUALITY OF
TECHNICAL EDUCATION SYSTEM
Sanjay Soni, Assistant Professor,
Department of Industrial & Production Engineering,
Jabalpur Engineering College, Jabalpur.
Dr. B. K. Chourasia, Associate Professor,
Department of Mechanical Engineering, Jabalpur Engineering College, Jabalpur.
ABSTRACT
This paper discusses about the study that was carried out to find the effect of various variables
on the quality of technical education system. A questionnaire comprising of various variables
which affect the quality of technical education like Total numbers of teaching Faculty in the
institute, Faculty performance, Faculty departure from the institute, Faculty satisfaction, Faculty
arrival in the institute, Placement ratio of the institute, Students arrival in the institute, Fund
availability for faculty training programs, Total number of classes conducted, Student
satisfaction etc are incorporated in the questionnaire. The variables incorporated in the
questionnaire are taken from various research studies that are been carried out by various
researchers and experts and Delphi study that was conducted in various Engineering colleges of
Jabalpur. Factor analysis approach was used for finding the impact of these variables on quality
of technical education. The rationale behind using this multivariate data analysis technique was
to reduce large sets of variables into smaller sets of factors and to find out the intercorelation
among them as it is easier to control smaller set of factors than to handle large number of
variables for controlling the quality of technical education.
Introduction:
The Major role of higher education is to cast students by uplifting their Knowledge, skills,
attitude and abilities and gradually empowering them as lifelong critical, reflective learners and
can be seen as a public Asset as it benefits the society as a whole [1], [2], [3].The higher
and 4738 colleges in the country enrolling around five percent of the eligible age group in higher
education. Today, in terms of enrolment, India is the third largest higher education system in the
world, behind China and the USA, with 17973 institutions (348 universities and
17625colleges).The number of institutions is more than four times the number in United States
and entire Europe. Higher education in China having enrolment in a higher education institution
in India is about 600-700 students, a higher education institution in United States and Europe
would have 3000-4000 students and in China this would be about 8000-9000 students (Source,
AICTE). This makes the system of highly fragmented one that is far more difficult to manage
than any other system of higher education in world. But it is accepted and unfortunate facts that
accept few premiere Institutes of national importance providing high quality higher education
rest are substandard. Irony is that all premier Institutes get the creamy layer of intakes.
Meritorious students getting admission in pioneer Institutes are natural professional.
Unfortunately substandard Institutes get non creamy layer of intakes of are just producing
Technical graduates having certificate but not required skills because of non quality practices.
There is a need to find out the factors which affect the quality of the Technical education system.
Literature review:
Education in general and Technical education in particular represents too-process-oriented,
Intangible and multiple-stakeholder situations. Most of the performance measurement systems of
higher educational institutions do not reflect the full range of interested stakeholders and are not
closely linked to the strategic management. Therefore, Cullen et al [3] propose the use of a
balanced scorecard approach in order to reinforce the importance of managing rather than just
monitoring performance. Garretson [5] confirms the importance of the expectation of key
stakeholders in the educational process while exploring the meaning of quality through students’
evaluation of an MBA programme using a combination of qualitative and quantitative
approaches. Temponi [9] analyses the main elements of continuous improvement in higher
education that Address the concerns of academia’s stakeholders during the process of its
such as European Forum for Quality Management (EFQM) and Total Quality Management
(TQM) for promoting continuous improvement of quality in education. In addition, a few studies
highlight the method of pedagogy and selection of institutes of higher learning [4].
Challenges before Indian Higher Education:
Government failure to impart education to all has led private participation in higher which has
turned education sector into an investment business which has created a quality failure in
technical education. Government and Private sectors are in acute shortage of qualified faculty.
This is due to the fact that merit students are not joining technical education system in all if poor
quality of teaching and learning is promoted it will produce poor quality of stuff therefore efforts
should be made to improve overall quality of technical education.
Specific Objectives of Study:
To find out correlation between various parameters of technical education system which affects
the quality of technical education system and to reduce them in smaller numbers as it is easy to
control small number of factors than to control large number of factors.
Methodology:
Multivariate Data Analysis Method (Factor Analysis)
Factor analysis is a data reduction technique that uses correlations between data variables. It
assumes that some underlying factors exist that explains the correlations or interrelationships
among observed variables [1].It has been used extensively in various fields of social sciences.
Data Collection:
Data was collected with administration of questionnaire prepared with the Delphi study
conducted in 20 engineering colleges of Jabalpur region. The 34 variables data sheet was
Results
Communalities
Initial Extraction
VAR00001 1.000 .971
VAR00002 1.000 .969
VAR00003 1.000 .981
VAR00004 1.000 .977
VAR00005 1.000 .960
VAR00006 1.000 .980
VAR00007 1.000 .974
VAR00008 1.000 .553
VAR00009 1.000 .664
VAR00010 1.000 .650
VAR00011 1.000 .716
VAR00012 1.000 .914
VAR00013 1.000 .611
VAR00014 1.000 .914
VAR00015 1.000 .487
VAR00016 1.000 .454
VAR00017 1.000 .586
VAR00018 1.000 .535
VAR00019 1.000 .494
VAR00020 1.000 .612
VAR00021 1.000 .508
VAR00022 1.000 .977
VAR00023 1.000 .981
VAR00025 1.000 .971
VAR00026 1.000 .977
VAR00027 1.000 .969
VAR00028 1.000 .960
VAR00030 1.000 .974
VAR00031 1.000 .981
VAR00032 1.000 .977
VAR00033 1.000 .960
VAR00034 1.000 .980
VAR00035 1.000 .974
Extraction Method: Principal Component Analysis.
Total Variance Explained
Component
Initial Eigenvalues Extraction Sums of Squared Loadings
Total
% of
Variance Cumulative % Total
% of
Variance Cumulative %
1 5.949 17.496 17.496 5.949 17.496 17.496
2 4.306 12.665 30.161 4.306 12.665 30.161
3 3.417 10.051 40.213 3.417 10.051 40.213
4 2.966 8.724 48.937 2.966 8.724 48.937
5 2.539 7.467 56.403 2.539 7.467 56.403
6 2.099 6.172 62.576 2.099 6.172 62.576
7 1.685 4.955 67.531 1.685 4.955 67.531
8 1.579 4.644 72.174 1.579 4.644 72.174
9 1.384 4.069 76.244 1.384 4.069 76.244
10 1.226 3.605 79.849 1.226 3.605 79.849
11 1.019 2.998 82.847 1.019 2.998 82.847
12 .961 2.826 85.673
13 .874 2.570 88.243
14 .789 2.320 90.563
15 .701 2.061 92.624
16 .678 1.995 94.619
17 .532 1.565 96.185
18 .466 1.369 97.554
19 .423 1.243 98.797
21 4.505 E-16
1.325E-15 100.000
22 2.205
E-16
6.485E-16 100.000
23 1.849
E-16
5.438E-16 100.000
24 8.943
E-17
2.630E-16 100.000
25 4.883
E-17
1.436E-16 100.000
26 3.429
E-17
1.009E-16 100.000
27 7.117
E-18
2.093E-17 100.000
28
-8.246 E-18
-2.425E-17 100.000
29
-1.617 E-17
-4.756E-17 100.000
30
-3.337 E-17
-9.813E-17 100.000
31
-7.153 E-17
-2.104E-16 100.000
32
-9.492 E-17
-2.792E-16 100.000
33
-1.442 E-16
-4.240E-16 100.000
34
-2.532 E-16
Extraction Method: Principal Component Analysis.
Component Matrixa Component
1 2 3 4 5 6 7 8
VAR00001 -.027 -.381 .346 .374 -.226 -.091 .678 .057
VAR00002 -.014 -.018 .408 -.295 .371 .581 .162 .453
VAR00003 -.035 .629 .549 .403 .221 -.043 -.007 -.158
VAR00004 .750 .545 -.267 .034 -.008 .008 .087 .147
VAR00005 -.662 .252 -.317 .138 -.398 .400 .063 .024
VAR00006 -.614 .211 -.375 -.018 .543 -.198 .237 -.032
VAR00007 .517 -.527 -.221 .484 .217 .204 .022 -.095
VAR00008 .055 -.348 -.256 -.254 .320 -.161 -.056 .157
VAR00009 .010 -.177 -.492 -.116 .251 -.148 .220 .068
VAR00010 -.060 -.046 .230 -.170 -.194 -.375 .007 .433
VAR00011 -.099 -.117 .258 -.100 -.224 -.375 -.027 .288
VAR00012 .349 -.105 .178 -.657 -.011 .094 .210 -.418
VAR00013 .249 .343 .041 -.389 .092 .211 .291 -.034
VAR00014 .349 -.105 .178 -.657 -.011 .094 .210 -.418
VAR00015 -.031 .000 .189 -.301 -.088 .003 -.066 -.323
VAR00016 -.022 -.058 .170 -.112 -.132 -.413 .211 .201
VAR00017 -.283 -.187 .352 .054 .256 .170 -.089 -.134
VAR00018 -.182 -.307 .188 -.016 .345 .144 -.211 .003
VAR00019 -.004 .197 -.076 -.075 -.086 .039 .306 -.380
VAR00020 .065 -.019 .005 .013 -.051 -.237 .034 .032
VAR00021 -.084 -.050 .251 .123 .177 -.097 -.283 .104
VAR00022 .750 .545 -.267 .034 -.008 .008 .087 .147
VAR00023 -.035 .629 .549 .403 .221 -.043 -.007 -.158
VAR00025 -.027 -.381 .346 .374 -.226 -.091 .678 .057
VAR00026 .750 .545 -.267 .034 -.008 .008 .087 .147
VAR00028 -.662 .252 -.317 .138 -.398 .400 .063 .024
VAR00029 -.614 .211 -.375 -.018 .543 -.198 .237 -.032
VAR00030 .517 -.527 -.221 .484 .217 .204 .022 -.095
VAR00031 -.035 .629 .549 .403 .221 -.043 -.007 -.158
VAR00032 .750 .545 -.267 .034 -.008 .008 .087 .147
VAR00033 -.662 .252 -.317 .138 -.398 .400 .063 .024
VAR00034 -.614 .211 -.375 -.018 .543 -.198 .237 -.032
VAR00035 .517 -.527 -.221 .484 .217 .204 .022 -.095
Extraction Method: Principal Component Analysis.
a. 11 components extracted.
Component Matrixa Component
9 10 11
VAR00001 -.081 .009 -.191
VAR00002 .094 -.033 .000
VAR00003 .126 -.062 .157
VAR00004 -.044 .094 -.070
VAR00005 .059 -.025 .112
VAR00006 -.151 -.047 -.028
VAR00007 -.061 -.033 .204
VAR00008 .335 .165 -.050
VAR00009 .339 -.063 .347
VAR00010 -.254 .100 .350
VAR00011 -.237 -.076 .529
VAR00012 .039 -.286 .080
VAR00013 -.290 .065 .227
VAR00014 .039 -.286 .080
VAR00015 -.265 .416 -.006
VAR00017 .062 .459 -.096
VAR00018 -.035 .421 .094
VAR00019 .077 .415 .137
VAR00020 .733 -.096 -.002
VAR00021 -.277 -.387 -.249
VAR00022 -.044 .094 -.070
VAR00023 .126 -.062 .157
VAR00025 -.081 .009 -.191
VAR00026 -.044 .094 -.070
VAR00027 .094 -.033 .000
VAR00028 .059 -.025 .112
VAR00029 -.151 -.047 -.028
VAR00030 -.061 -.033 .204
VAR00031 .126 -.062 .157
VAR00032 -.044 .094 -.070
VAR00033 .059 -.025 .112
VAR00034 -.151 -.047 -.028
VAR00035 -.061 -.033 .204
Extraction Method: Principal Component Analysis.
a. 11 components extracted.
Reliability
Reliability Statistics
Cronbach's Alpha N of Items
.258 34
Conclusion
With the present investigation we find that large number of variables can be reduced to small
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Appendix 1.Variables used for factor analysis
S.NO Variables for factor analysis
1 Total Faculty
2 Total Registered students
3 Total students in P.G
4 Drop out Faculty
5 Drop out Students
6 Drop out Students in P.G
7 Faculty arrival
8 Faculty Departure
9 Higher studies student departure
10 Student arrival
11 Students arrival for higher studies
12 Students Departure
13 Admission in Post graduation courses
14 Admission per year
15 Ineligible students per year
16 Admission Cancellation per year
17 Delay in readmission
18 Delay in readmission in PG due to late result declaration
19 Faculty appointment
20 Faculty drop out ratio
21 Faculty on study leave
22 Faculty growth factor
23 Faculty Recruitment
24 Faculty satisfaction
25 Guest Faculty appointment
26 Infrastructure growth factor
27 Institute growth factor
28 Placement Ratio
29 Potential applicant for admission
30 Quality of Technical education
31 Ratio of classes conducted
32 Ratio of fund expenditure on faculty welfare
33 Ratio of students scoring first division
