2011 Senthilkumar SQM-HEI-determination-of-service-quality-mesurement-of-HE-in-india

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Journal of Modelling in Management
SQM-HEI – determination of service quality measurement of higher education in India
N. Senthilkumar, A. Arulraj,
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JM2
6,1
SQM-HEI – determination
of service quality measurement
of higher education in India
60
N. Senthilkumar
Received 3 October 2009
Revised 5 May 2010
Accepted 20 July 2010
Department of Management Studies, Anna University, Chennai, India, and
A. Arulraj
Department of Economics, R.S. Government College, Thanjavur, India
Abstract
Journal of Modelling in Management
Vol. 6 No. 1, 2011
pp. 60-78
q Emerald Group Publishing Limited
1746-5664
DOI 10.1108/17465661111112502
Purpose – The purpose of this paper is to develop a new model, namely service quality measurement
in higher education in India (SQM-HEI) for the measurement of service quality in higher educational
institutions.
Design/methodology/approach – Data were collected by means of a structured questionnaire
comprising six sections. Section A consists of ten questions pertaining to teaching methodology (TM).
Sections B consists of five questions pertaining to environmental change in study factor (ECSF).
Section C consists of eight questions relating to disciplinary measures taken by the institutions. Section D
consists of five questions related to the placement-related activities and in part E two questions provide
an overall rating of the service quality, satisfaction level. Finally, in part F 13 questions pertaining to
student respondent’s demographic profile information were given. All the items in Sections A-E were
presented as statements on the questionnaire, with the same rating scale used throughout, and measured
on a seven-point, Likert-type. In addition to the main scale addressing individual items, respondents
were asked in Section E to provide an overall rating of the service quality, satisfaction level.
For conducting an empirical study, data were collected from final-year students of higher educational
institutions across Tamil Nadu. The sampling procedure used for the study was stratified random
sampling. The stratification has been done based on the region Chennai, Coimbatore, Madurai,
Tiruchirappalli, and nature of institution, government university, government college, aided college,
private university and self-financing college. While selecting the institutions from each category,
non-probabilistic convenience and judgmental sampling technique were used. However, within such
institutions, the respondents were selected by stratified random sampling.
Findings – The SQM-HEI-mediated model argued that the placement is the better interactions of the
quality of education in India. The model reveals that the quality of education is based on the best
faculty (TM), the excellent physical resources (ECSF), a wide range of disciplines (DA) which paved
the diverse student body and to improve the employability of the graduates (placement as mediating
factor) coming out of the higher educational institutions in India. The above model proves that the
placement is the mediated factor for various dimensions of quality education. SQM-HEI model would
help in identify three service areas to be focused in the higher educational institutions for improving
the quality of . These three dimensions of quality correlated between the sub-dimension variables
and it is very necessary for improving the quality of higher education in India. The educationist
says that, education is a change of behavior of students. Hence, the higher educational institutions
should come forward to adapt the sub-dimensions of quality variables to enhance the outcome of
education.
Originality/value – The model described in this paper will assist academic institutions when
mapping the level of service quality and thereby enhance the same.
Keywords Modelling, Higher education, India, Service quality assurance
Paper type Research paper
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Introduction
Among all service sectors, the education sector, particularly the higher education
system, has direct bearing on society for society’s growth and socio-economic
development. The proliferation of higher educational institutions, though a proud phase
in the economic regeneration of the country, brought in its trail innumerable traits; the
governments at the centre and state levels, through various regulatory bodies, monitor
the functioning of the higher educational institutions with a view to ensure high caliber
of educational service. Yet, the quality of higher education falls short of attaining the
global-level excellence. The limited number of state-funded institutions and diminishing
funding in higher education from the government caused the mushrooming of private
institutions in India. Therefore, the students have a wide range of options to choose from
which the institution to pursue their interests. As the students bear the complete
expenditure of education, they deserve the best education. Therefore, quality has
become a competitive weapon for the institutions to serve and attract their primary
customers (students).
It is understood from the literature that many educational institutions, when beset
with crisis, resorted to various quality assessment practices and succeeded in
overcoming the crisis. Therefore, the present study focuses the outcome as the adoption
of quality management practices model, to cleanse itself to all the ills vitiating the service
quality. Even though several models are available to measure the service quality, it
appears from the review of literature that no holistic model has been developed so far to
measure the service quality of higher educational service from the perception of students
in the Indian context. The development of a quality measurement instrument for the
educational setup and a methodology for the assessment of quality is of prime
importance for providing guidelines to the administrators of the institutions, especially
in the present Indian context.
Empirical studies of service quality in higher education
In higher education, quality measurement is intensifying with increased emphasis on
education accountability. Nonetheless, many researchers used the adapted version of
SERVQUAL to evaluate students’ course experience within a business school as part of the
quality assurance system (Rigotti and Pitt, 1992; McElwee and Redman, 1993; Hill, 1995;
Cuthbert, 1996; Oldfield and Baron, 2000). Ho and Wearn (1996) incorporated SERVQUAL
into HETQMEX, a higher education TQM excellence model. Whilst in nurse education,
Hill et al. (1996) devised a quality instrument for post-registration nurse education derived
from existing literature sources for module management. The conclusion appears to be
that many researchers are undertaking customization of established service quality
dimensions in higher education in their measurement instruments.
There are many gray areas in the debate over how to measure service quality. The
argument regarding the gaps (SERVQUAL), perceptions only (SERVPERF) and EP
approaches to measuring service quality is still unresolved as there are valid issues and
suggestions on either side of this debate. The general view appears to be that, although
SERVQUAL, SERVPERF and EP were designed as generic measures of service quality
that have cross-industry applicability, it is important to view the instruments as basic
“skeletons” that often require modification to fit the specific application situation and
supplemental context-specific items. Without doubt the use of these approaches as a
means of measuring service quality throughout the marketing sectors may have been
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tested with some degree of success, but this may not be the case for other service sectors,
namely, higher education.
With all these seemingly irreconcilable problems associated, perhaps the time has
come to “bury” the existing instruments and attempt to reconstruct or redefine service
quality from a new and different perspective. Thus, the general conclusion appears to
be that industry-specific service quality measures may be a more viable research
strategy to pursue (Zeithaml et al., 1985; Finn and Lamb, 1991; Cronin and Taylor, 1992;
Brown and Koenig, 1993). As it stands, the generic measures of service quality may not
be a totally adequate instrument by which to assess the perceived quality in higher
education, although their impact on the service quality domain is undeniable.
On the other hand, Higher Education PERFormance (HEdPERF), a new and more
comprehensive performance-based measuring scale attempts to capture the authentic
determinants of service quality within the higher education sector. The 41-item
instrument has been empirically tested for unidimensionality, reliability and validity
using both exploratory and confirmatory factor analysis (CFA). Therefore, the primary
issue addressed in that paper is about comparing different measures of the service
quality construct within a single, empirical study utilizing customers of a single industry
namely higher education (Firdaus, 2005b).
Firdaus (2005a) empirically explained that in terms of unidimensionality, reliability,
and validity, HEdPERF-explained variance within the higher education setting is better
in comparison to SERVPERF. The study only examined the respective utilities of each
instrument within a single industry, in only one national setting, any suggestion that the
HEdPERF is generally superior would still be premature. Service quality has attracted
considerable attention within the higher education sector, but despite this, little work
has been concentrated on identifying its determinants from the standpoint of students
being the primary customers. Thus, it would seem rational to develop a new
measurement scale that incorporates not only the academic components, but also
aspects of the total service environment as experienced by the student. Likewise, there
are many areas of disagreement in the debate over how to measure service quality, and
recent research has raised many questions over the principles on which the existing
instruments are founded. Although these generic instruments have been tested with
some degree of success in wide-ranging service industries, but their replication in higher
education sector is still hazy.
Mahapatra and Khan (2007) evolves a systematic integrated approach for modeling
customer evaluation of service quality applied to technical education through a survey
instrument known as EduQUAL, specifically proposed for the education sector,which is
used to measure the satisfaction level of different stakeholders.
From the existing literature summarized above, the researcher identified that
SERVQUAL, HEdPERF, EduQUAL and other similar studies are empirically tested on
academic and non-academic aspects. The researcher identified that there exist a gap in
the research pertaining to higher education service quality evaluation in India.
Customers of higher education
Sirvanci (1996) indicates that the students are generally assumed to be the principal
customers and take on different roles within the institution. They are the product of the
process, the internal customers for many campus facilities, the laborers of the learning
process and the internal customer of the delivery of the course material. Crawford (1991)
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argues that students are the primary customers in an educational setup. This is supported
strongly by Galloway (1998). Nevertheless, it is generally accepted that students are the
primary customers and the other potential customers such as alumni, parents, employers,
employees, government, industry and society may be considered secondary customers
(Owlia and Aspinwall, 1996). Hence, among all the stakeholders, the students of an
institute are to be considered the most important stakeholders, as they are the significant
customers of a higher educational institutions, compared to other stakeholders. Service
quality has attracted considerable attention within the higher education sector, but
despite this, little work has been concentrated on identifying its determinants from the
standpoint of students being the primary customers. Hence, this research work
considered the students as the primary customers and the research was carried out.
Service quality measurement in higher education in India model
The proposed service quality measurement in higher education in India (SQM-HEI)
model which is a 30-item instrument has been empirically tested for unidimensionality,
reliability and validity using both exploratory and CFA. Such valid and reliable
measuring scale would be a tool that higher educational institutions could use to
improve service performance in the light of increased competition with the development
of global education markets. The results from the current study are crucial because
previous studies have produced scales that bear a resemblance to the generic measures
of service quality, which may not be totally adequate to assess the perceived quality in
higher education. Furthermore, previous researches have been too narrow, with an
over-emphasis on the quality of academics and too little attention paid to the
non-academic aspects of the educational experience and placement.
Surveys generally fall into one of the two categories, descriptive or relational.
Descriptive surveys are designed to provide a snapshot of the current state of affairs while
relational surveys are deigned to empirically examine relationships among two or more
constructs either in an exploratory or in a confirmatory manner. The current study is a
relational survey that seeks to explore the relationship between teaching methodology
(TM), environmental change in study factor (ECSF), disciplinary action (DA), placement as
the mediating factor and the outcome as the quality education. The developed research
model SQM-HEI, specifically proposed for the higher education sector in India, is used to
measure the quality of higher education (as shown in Figure 1).
Pilot study
Prior to beginning actual data collection with the procedure described above, the
researcher utilized similar procedures to conduct a pilot study to ensure that the survey
materials and procedure were clear and did not provoke any confusion or problems for
participants. The draft questionnaire was eventually subjected to pilot testing with a
total of 100 final-year students spread across the different regions and varied type of
institutions, and they were asked to comment on any perceived ambiguities, omissions
or errors concerning the draft questionnaire. The feedback received was rather
ambiguous thus only minor changes were made. For instance, technical jargon was
rephrased to ensure clarity and simplicity. The revised questionnaire was subsequently
submitted to three experts (an academician, a researcher and a National Assessment and
Accreditation Council (NAAC) peer team committee member) for feedback before being
administered for a full-scale survey. These experts indicated that the draft questionnaire
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Model 1: SQM-HEI mediated model
Demographic
variables
Mediating selfactualizationplacement
Quality education
Teaching methodology
Relevant curriculum
Teching and learning support
Theoretical and practical
knowledge of academic staff
Course material
Degreee to which exams are
representative of courses taught
Extent to which academic staff
are upto date in their subject
Environmental
change in study
factor
Disciplinary action
Figure 1.
was rather lengthy, which in fact coincided with the preliminary feedback from
students. Nevertheless, in terms of number of items in the questionnaire, the current
study conforms broadly with similar research work (Cronin and Taylor, 1992; Teas,
1993; Lassar et al., 2000; Mehta et al., 2000; Robledo, 2001) that attempted to compare
various instruments for measuring service quality.
Construct measures and data collection
Data were collected by means of a structured questionnaire comprising six sections
namely A, B, C, D, E and F. Section A consists of ten questions pertaining to TM. Sections B
consists of five questions pertaining to ECSF. Section C consists of eight questions relating
to disciplinary measures taken by the institutions (DA). Section D consists of five
questions related to the placement related activities and in the part E two questions
provide an overall rating of the service quality, satisfaction level. Finally, in the part
F 13 questions pertaining to student respondent’s demographic profile information were
given.
All the items in Sections A to E were presented as statements on the questionnaire,
with the same rating scale used throughout, and measured on a seven point, Likert-type
scale that varied from 1 highly dissatisfied to 7 highly satisfied. In addition to the main
scale addressing individual items, respondents were asked in Section E to provide an
overall rating of the service quality, satisfaction level.
For conducting an empirical study, data were collected from final-year students of
higher educational institutions across Tamil Nadu. Tamil Nadu is a front-line state in
India thriving for imparting quality education in the field of higher education. The state
has a good mix of private- and government-funded institutions. This is trend witnessed
all over India barring a couple of states. Hence, it is felt that trend observed in
Tamil Nadu/model supplied to the educational setup is equally applicable to other states
in India. The higher educational activities have increased manifold over the last few
years. Presently Tamil Nadu boosts of: 53 universities, 456 engineering colleges and
1,550 arts and science colleges. Also Tamil Nadu produces the highest number of
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engineering graduates in India (around 65,000) every year which attracts many software
companies to set up their shop in south India. One might be surprised to know this fact
that in most of the professional courses, out of the total students enrolled in the country,
approximately 28 percent are from Tamil Nadu. Hence, today Tamil Nadu is righteously
called as the “Oxford of the India” The heterogeneous scenario of higher education in
Tamil Nadu is considered to be the replica of the country’s scenario. The reason for
selecting the final-year students in Tamil Nadu is due to the reason that they could have
had more exposure to the education system in all its phases. Assurance was given to the
respondents that the information collected from them will be kept confidential and will
be used only for academic research purposes.
Data had been collected using the “personal-contact” approach as suggested by
Sureshchandar et al. (2001), whereby “contact persons” (Registrar or Assistant Registrar)
have been approached personally, and the survey was explained in detail. The final
questionnaire together with a cover letter was then handed personally or mailed to the
“contact persons”, who in turn distributed it randomly to students within their respective
institutions. A total of 2,000 nos. of questionnaire were circulated to four regions across
the length and breadth of the Tamil Nadu State comprising of 12 government universities,
16 government colleges, 12 aided colleges, ten deemed universities and 61 self-financing
colleges. Of these 1,749 were collected. Out of the questionnaires that were collected
149 were not usable due to insufficient and/or incomplete data. As a result, a total of
1,600 valid questionnaires were used for the analysis, leading to a response rate of
80 percent. The number of usable questionnaires were 1,600 for a population size of nearly
300,000 students in Tamil Nadu higher educational institutions was in line with the
generalized scientific guidelines for sample size decisions as proposed by Krejcie and
Morgan (1970). Hence, the sample size for the analysis is 1,600.
The sampling procedure used for the study was stratified random sampling.
The stratification has been done based on the region Chennai, Coimbatore, Madurai,
Tiruchirappalli, and nature of institution, government university, government college,
aided college, private university and self-financing college. While selecting the
institutions from each category, non-probabilistic convenience and judgmental sampling
technique were used. However, within such institutions, the respondents were selected
by stratified random sampling.
Procedure for data analysis
The data collected were analyzed for the entire sample. Data analyses were performed
with Statistical Package for Social Sciences (SPSS) using techniques that included
descriptive statistics, correlation analysis and AMOS package for structural equation
modeling (SEM) and Bayesian estimation and testing.
Structural equation modeling
The main study used SEM because of two advantages:
(1) estimation of multiple and interrelated dependence relationships; and
(2) the ability to represent unobserved concepts in these relationships and account
for measurement error in the estimation process (Hair et al., 1998, p. 584).
In other words, a series of split but independent multiple regressions were simultaneously
estimated by SEM. Therefore, the direct and indirect effects were identified (Tate, 1998).
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However, a series of separate multiple regressions had to be established based on “theory,
prior experience, and the research objectives to distinguish which independent variables
predict each dependent variable” (Hair et al., 1998, p. 584). In addition, because SEM
considers a measurement error, the reliability of the predictor variable was improved.
AMOS 7.0 (Arbuckle and Wothke, 2006), a computer programme for formulating, fitting
and testing SEM to observed data, was used for SEM and the data preparation was
conducted with SPSS 13.0.
Linear SEMs are widely used in sociology, econometrics, management, biology, and
other sciences. An SEM (without free parameters) has two parts: a probability
distribution (in the normal case specified by a set of linear structural equations and a
covariance matrix among the “error” or “disturbance” terms), and an associated path
diagram corresponding to the causal relations among variables specified by the
structural equations and the correlations among the error terms. It is often thought that
the path diagram is nothing more than a heuristic device for illustrating the assumptions
of the model.
SEM with latent variables are more and more often used to analyze relationships
among variables in marketing and consumer research (see for instance Bollen, 1989;
Schumacker and Lomax, 1996, or Batista-Foguet and Coenders, 2000, for an introduction
and Bagozzi, 1994 for applications to marketing research). Some reasons for the
widespread use of these models are their parsimony (they belong to the family of linear
models), their ability to model complex systems (where simultaneous and reciprocal
relationships may be present, such as the relationship between quality and satisfaction),
and their ability to model relationships among non-observable variables (such as the
domains in the SQM-HEI model) while taking measurement errors into account (which
are usually sizeable in questionnaire data and can result in biased estimates if ignored).
As is usually recommended, a CFA model is first specified to account for the
measurement relationships from latent to observable variables. In our case, the latent
variables are the four perception dimensions and the observed variables the
30 perception items. The relationships among latent variables cannot be tested until a
well-fitting CFA model has been reached. In our case, the relationships among overall
quality of education, the mediating impact of placement with the TM, ECSF and DA
dimensions are of interest. This modeling sequence stresses the importance of the
goodness-of-fit assessment. As a combination of regression, path and factor analyses, in
SEM, each predictor is used with its associated uncontrolled error and, unlike regression
analyses, predictor multicollinearity does not affect the model results.
Evaluation of model fit
According to the usual procedures, the goodness of fit is assessed by checking the
statistical and substantive validity of estimates, the convergence of the estimation
procedure, the empirical identification of the model, the statistical significance of the
parameters, and the goodness of fit to the covariance matrix. Since complex models are
inevitably misspecified to a certain extent, the standard test of the hypothesis of perfect
fit to the population covariance matrix is given less importance than measures of the
degree of approximation between the model and the population covariance matrix.
The root mean squared error of approximation (RMSEA) is selected as such a measure.
Values equal to 0.05 or lower are generally considered to be acceptable (Browne and
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Cudeck, 1993). The sampling distribution for the RMSEA can be derived, which makes it
possible to compute confidence intervals.
These intervals allow researchers to test for close fit and not only for exact fit, as the
x2 does. If both extremes of the confidence interval are below 0.05, then the hypothesis of
close fit is rejected in favor of the hypothesis of better than close fit. If both extremes
of the confidence interval are above 0.05, then the hypothesis of close fit is rejected in
favor of the hypothesis of bad fit.
Several well-known goodness-of-fit indices (GFI) were used to evaluate model fit: the
chi-square x2, the comparative fit index, the unadjusted GFI, the normal fit index (NFI),
the Tucker-Lewis index (TLI), the RMSEA and the standardized root mean square error
residual.
Bayesian estimation and testing in SEM
With modern computers and software, a Bayesian approach to SEM is now possible.
Posterior distributions over the parameters of a SEM can be approximated to arbitrary
precision with AMOS, even for small samples. Being able to compute the posterior over
the parameters allows us to address several issues of practical interest. First, prior
knowledge about the parameters may be incorporated into the modeling process in
AMOS. Second, we need not rely on asymptotic theory when the sample size is small, a
practice which has been shown to be misleading for inference and goodness-of-fit tests in
SEM (Boomsma, 1983). Third, the class of models that can be handled is no longer
restricted to just-identified or over-identified models. Whereas, each identifying
assumption must be taken as given in the classical approach, in a Bayesian approach
some of these assumptions can be specified with perhaps more realistic uncertainty.
Hypotheses development
Mediation refers to a process or mechanism through which one variable (i.e. exogenous)
causes variation in another variable (i.e. endogenous). Studies designed to test for
moderation may provide stronger tests of mediation than the partial and whole
covariance approaches typically used (e.g. Baron and Kenny, 1986; Bing et al., 2002;
James and Brett, 1984). It is useful to distinguish between moderation and mediation.
Moderation carries with it no connotation of causality, unlike mediation which implies a
causal order.
Based on the arguments discussed above the researcher formulated the following
hypothesis:
H1. Including the interaction between dimensions of the service quality and
placement will explain more of the variance in overall service quality than the
direct influence of dimensions of service quality or placement on their own.
A mediator hypothesis is supported if the interaction path (TM, ECSF, DA £ placement)
are significant. There may also be significant main effects for the predictor (service
quality) and mediator (placement). Therefore, this research seeks to explore whether the
relationship between service quality and TM, ECSF and DA are fully or partially
mediated by placement. The analysis was done with SPSS 13.0 and AMOS 7.0 software
packages. The following sections present the construction and validation of SEM of
SQM-HEI-mediated model with the dimensions TM, ECSF, and DA, the mediating
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parameter placement and the outcome of the quality education. And also, the
SQM-HEI-mediated model is tested with Bayesian testing and estimation.
Regression model of the SQM-HEI-mediated SEM
In hierarchical regression, the predictor variables are entered in sets of variables
according to a pre-determined order that may infer some causal or potentially mediating
relationships between the predictors and the dependent variable (Francis, 2003).
Such situations are frequently of interest in the social sciences. The logic involved in
hypothesizing mediating relationships is that “the independent variable influences the
mediator which, in turn, influences the outcome” (Holmbeck, 1997). However, an
important pre-condition for examining mediated relationships is that the independent
variable is significantly associated with the dependent variable prior to testing any
model for mediating variables (Holmbeck, 1997). Of interest is the extent to which the
introduction of the hypothesized mediating variable reduces the magnitude of any direct
influence of the independent variable on the dependent variable.
Hence, the researcher empirically tested the hierarchical regression for the model
conceptualized in Figure 2, with in the AMOS graphics environment. The path diagram
for the hypothesized mediated model is given in the path diagram.
The analyses conducted, the parameter estimates are then viewed within AMOS
graphics. Figure 2 shows the standardized parameter estimates.
The regression analysis revealed that the student’s perception on the various
dimensions of service quality, ECSF explained 0.45 of the quality education, followed by
TM which explains 0.41 of the quality education. The R 2 value of 0.58 is displayed above
the box quality education in the AMOS graphics output. The visual representation of
results suggest that the relationships between the dimensions of quality education and
the mediated factor. The TM resulted a significant impact on the mediated factor,
placement. The DA resulted very limited influence on the quality education. It shows
that the student’s perception towards the disciplinary measures taken by the
management towards the outcome of education quality is insignificant, where as the
impact of the same is very high on the mediating variable. Very high covariance between
DA and TM reveals that students have a high regard on the teachers in shaping their
career. The covariance between TM and ECSF is very high, which means that the TM
and ECSF play a indispensable role in the outcome of the quality education. According
to Hoyle (1995) a model is a statistical statement about the relation among variables, in
TM
0.41
0.45
ECSF
0.20
0.60
0.39
0.58
0.07
Quality education
Placement
0.52
Figure 2.
Standardized parameter
estimates for mediated
SQM-HEI model
e1
0.29
0.58
0.25
DA
0.07
e2
SQM of higher
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69
Bayesian estimation and testing for regression model of
SQM-HEI-mediated SEM
The research model is an SEM, while many management scientist are most familiar with
the estimation of these models using software that analyses covariance matrix of the
observed data (e.g. LISREL, AMOS, EQS), the researcher adopt a Bayesian approach for
estimation and inference in AMOS 7.0 environment (Arbuckle and Wothke, 2006). Since, it
offers numerous methodological and substantive advantages over alternative approaches.
The Bayesian convergence distribution of the SQM-HEI-mediated regression model.
In this research the researcher has adopted for the procedure of assessing convergence of
Markov chain Monte Carlo (MCMC) algorithm of maximum likelihood. To estimate the
MCMC convergence the researcher has adopted two methods namely, convergence
in distribution, convergence of posterior summaries. The values of posterior means
accurately estimate the SQM-HEI mediated-SEM model. From the above table the
highest value of convergence statistics is 1.001 which is less than the 1.002 conservative
measure (Gelman et al., 2004).
Posterior diagnostic plots of SQM-HEI-mediated regression model
To check the convergence of the Bayesian MCMC method the posterior diagnostic plots
are analyzed. Figure 3 shows the posterior frequency polygon of the distribution of the
parameters across the 70,000 samples. The Bayesian MCMC diagnostic plots reveals
that for all the figures the normality is achieved, so the SEM fit is accurately estimated.
SQM-HEI Model
Frequency
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the present study reveals the relationship among the various dimensions of quality
and the outcome of the quality education. The value of RMSEA and other GFI and NFI
are 0.049, 0.987, and 0.928, respectively. Hence, the hypothesized mediated SQM-HEI
mediated model is empirically proved. Even though several studies have been
undertaken to identify the sustainability of the higher education institutions, this study
concludes that the sustainability can be attained in concentrating on the dimensions of
quality rather than taking strenuous efforts to cut down the cost of service delivery.
0.08
0.09
0.1
QE <– tm (W1)
0.11
0.12
0.13
Figure 3.
Posterior frequency
polygon distribution of the
quality education and TM,
regression weight (W1)
JM2
6,1
Frequency
Figure 4.
Posterior frequency
polygon distribution of the
first and last third of the
samples of the SQM-HEI
regression model for the
mediated factor placement
and ECSF
0
0.1
0.2
Placemen <– ecsf (W3)
0.3
0.4
To ensure that Amos has converged to the posterior distribution is a simultaneous
display of two estimates of the distribution, one obtained from the first third of the
accumulated samples and another obtained from the last third. Figure 4 shows the
simultaneous display of two estimates of the distribution for the mediated factor
placement with the other dimensions across 55,000 samples. From the three figures it is
observed that the distributions of the first and last thirds of the analysis samples are
almost identical, which suggests that Amos has successfully identified the important
features of the posterior distribution of the relationship between the mediated factor
placement and other quality dimensions.
The trace plot also called as time-series plot that shows the sampled values of a
parameter over time. This plot helps to judge how quickly the MCMC procedure
converges in distribution. Figure 5 shows the trace plot of the SQM-HEI model for
the mediated factor placement with other dimensions across 70,000 samples. All the
three figures exhibits rapid up-and-down variation with no long-term trends or drifts.
If we mentally break up this plot into a few horizontal sections, the trace within any
0.3
Placemen <– tm (W2)
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70
SQM-HEI Model
Figure 5.
Posterior trace plot of the
SQM-HEI regression
model for the mediated
factor placement and TM
0.2
0.1
0
10,000
20,000
30,000
40,000
Iteration
50,000
60,000
70,000
SQM of higher
education in India
71
SQM-HEI Model, placemen <– ecsf (W3)
1
0.9
0.8
0.7
Correlation
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section would not look much different from the trace in any other section. This indicates
that the convergence in distribution takes place rapidly. Hence, the SQM-HEI MCMC
procedure very quickly forget its starting values.
To determine how long it takes for the correlations among the samples to die down,
autocorrelation plot which is the estimated correlation between the sampled value at any
iteration and the sampled value k iterations later for k ¼ 1, 2, 3,. . . is analyzed for the
SQM-HEI regression model. Figure 6 shows the correlation plot of the SQM-HEI model
for the mediated factor placement with other dimensions across 70,000 samples. The
three figures exhibits that at lag 90 and beyond, the correlation is effectively 0. This
indicates that by 90 iterations, the MCMC procedure has essentially forgotten its starting
position. Forgetting the starting position is equivalent to convergence in distribution.
Hence, it is ensured that convergence in distribution was attained, and that the analysis
samples are indeed samples from the true posterior distribution.
Even though marginal posterior distributions are very important, they do not reveal
relationships that may exist between the two parameters. Hence, to visualize the
relationships among pairs of parameters in three dimensional Figure 7 shows bivariate
marginal posterior plots of the SQM-HEI model for the mediated factor placement with
other dimensions across 70,000 samples. From the three figures it reveals that the
three-dimensional surface plots also signifies the interrelationship between the
mediating variable placement with the other dimensions TM, ECSF and DA.
Figure 8 shows the two-dimensional plot of the bivariate posterior density across
50,000 samples. Ranging from dark to light, the three shades of gray represent 50, 90, and
95 percent credible regions, respectively. From the three figures, it reveals that the
sample respondent’s responses are normally distributed.
The various diagnostic plots featured from figure of the Bayesian estimation of
convergence of MCMC algorithm confirms the fact that the convergence takes place and
the normality is attained. Hence, absolute fit of the SQM-HEI regression model. From the
SQM-HEI regression model which is empirically tested with mediating factor placement
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
0
10
20
30
40
50
Lag
60
70
80
90
100
Figure 6.
Posterior correlation plot
of the SQM-HEI regression
model for the mediated
factor placement and
ECSF
JM2
6,1
)
W2
(
– tm
n<
e
m
e
0.2
Plac
Pla
0.3
cem
en <
0.3
–e
0.2
csf
(W
3)
0.1
0.1
ency
2)
tm
2
0.
<–
3
2
0. f (W
s
ec
Pl
0.
)
1
0.
1
ac
em
en
–
0.1
Placemen <– tm (W2)
0.2
0.3
0.3
0.2
0.2
0.2
Placemen <– tm (W2)
0.1
0.1
0.1
Placemen <– ecsf (W3)
0.3
Placemen <– ecsf (W3)
Figure 8.
Two-dimensional plot of
the bivariate posterior
density for the regression
weights placement to TM
and placement to ECSF
(W
ac
<
3
0. en
em
Pl
Figure 7.
Three-dimensional surface
plot of the marginal
posterior distribution of
the mediating factor
placement with the TM
and ECSF
0.
0
3
Freq
u
y
uenc
Freq
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72
0.3
with the dimensions TA, ECSF, and DA and the overall service quality it is evident that
the higher educational institutions should concentrate on the placement as the
mandatory aspect of higher education which is not the case in developing countries.
The SQM-HEI-mediated model argued that the placement is the better interactions of
the quality of education in India. The model reveals that the quality of education is based
on the best faculty (TM), the excellent physical resources (ECSF), a wide range of
disciplines (DA) which paved the diverse student body and to improve the employability
of the graduates (placement as mediating factor) coming out of the higher educational
institutions in India. The above model proves that the placement is the mediated factor
SQM of higher
education in India
73
Policy formulation for enhancing the quality of higher education with
SQM-HEI model
There is every reason to invest further in improving the quality of higher education.
However, it is not an investment than can be borne easily by those who stand to benefit
most. Hence, the role of government, as the actor most able to transcend short-term
realities and interests and invest in quality, becomes crucial. It has been argued that
governments should invest at least 6 percent of gross national product in education
(Delors et al., 1996). While this level of investment is not itself a guarantor of quality, the
idea of a benchmark has considerable political value and in many countries meeting such a
target would be a boost to the level of available resources. For each country there is clearly
a minimum level below which government expenditure cannot sink without serious
consequences for quality. Recognizing the importance of contextual circumstances and
employing evidence from earlier stages this section provides a framework for improving
the quality of higher education, shown in Figure 9. The policy framework places students
at the heart of the teaching and learning process, emphasizing that, from the outset, policy
must acknowledge their diverse characteristics, circumstances and learning needs. This
emphasis is important in establishing objectives for better quality and defining strategies
to improve quality of higher education. The central role of students, therefore, is the
starting point for this framework. It leads to a consideration of the ways in which teaching
Quality education
Tea
D is
ciplinary action
mehtod
ing
ol
ch
Student
Placement
o
nm
fa c
vir
En
tor
Placement
y
og
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for various dimensions of quality education. SQM-HEI model would help in identify
three service areas to be focused in the higher educational institutions for improving the
quality of education – namely TM, ECSF and DA. These three dimensions of quality
correlated between the sub-dimension variables and it is very necessary for improving
the quality of higher education in India. The educationist says that education is a change
of behavior of students. Hence, the higher educational institutions should come forward
to adapt the sub-dimensions of quality variables to enhance the outcome of education.
ent
al change in stu
Quality education
dy
Figure 9.
Policy framework for
improving the quality of
higher education in India
JM2
6,1
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74
and learning in the classroom can be genuinely responsive to learners through curriculum
development and application (TM). This ring of Figure 9 also covers both placement
and quality education as envisaged in SQM-HEI model and realized through the teaching
and learning process (TM). Beyond the classroom, there are many ways to create an
enabling environment that is conducive to teaching and learning (ECSF and DA).
Better teachers, better colleges and universities and a strong knowledge infrastructure can
all make a considerable difference to the quality of higher education. Finally, in this
framework comes the umbrella of a coherent education sector policy and the reforms that
governments can initiate at the national and state level.
Teaching methodology
Quality in higher education is a holistic concept. Thanks to NAAC for the awakening it
has brought about among more than a 1,000 higher institutions of learning in our
country which have reset their goals, diversified curriculum and improved methods of
teaching and learning after the first round of institutional assessments made. From the
findings with regard to the dimensions TM and the sub-dimensions the following
outlines are given with respect to TM:
.
Teaching in higher education is a profession. It is a form of public service that
require expert knowledge and specialized skills acquired and maintained through
rigorous and lifelong study and research. It calls for a sense of personal and
institutional responsibility for the education and welfare of students and of the
community at large and for a commitment to high professional standards in
scholarship and research.
.
Higher educational personnel should maintain and develop knowledge of their
subject through scholarship and improved pedagogical skills, possibly with
latest technological aids.
.
Working conditions for highereducation teaching personnel should be such that it
will best promote effective teaching scholarships, research and extension work and
enable highereducation teaching personnel to carry out their professional tasks.
.
Making use of the libraries which have up-to-date collections, computer systems,
satellite programmes and databases required for their teaching, scholarship and
research.
.
The publication and dissemination of the research results obtained by higher
education teaching personnels should be encouraged and facilitated with a view to
assisting them to acquire the reputation which they merit as well as with a view
programme providing for the broadest exchange of higher education personnel
between institutions both nationally and to promoting the advancement of skill
technology, education and culture. By and large the teachers were free to do this
but the initiative from teaching personnel should be more forthcoming.
.
The interplay of ideas and information among higher education teaching
personnel throughout the work is vital to the healthy development of higher
education and research and should be actively promoted. They should be enabled
throughout their careers to participate in international seminars on higher
education or research, to travel abroad and to use the internet or video
conferencing for these purposes.
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.
Programme providing for the broadest exchange of higher education personnel
between institutions both nationally and internationally including the organization
of symposiums, seminars and collaborative projects and the exchange of
educational and scholarly information should be developed and encouraged. The
extension of communication and direct contacts between universities, research
institutions and associations as well as among scientists and research workers
should be facilitated as should access by higher education teaching personnel from
other states, to open information materials in public activities, libraries of research
institutions. Peer evaluation of teachers would be successful in improving teachers
skills and competence only if teachers are assured that the results will not be used
immediately for promotion or career advancement. Infact, it should be used to
change their own behavior without any external threat.
Environmental change in the study factor
.
Effective curricular transaction depends on the extent and quality of institutional
infrastructure, learning resources like library, laboratory and access to computer
facilities. Along with these basic facilities, academic activities like workshops,
conferences, overseas collaborations and seminars enrich the learning ambience.
Any quality enhancing effort should ensure these facilities and activities.
.
The new forms of education require skills of a different order that include the
facile use of information technology, mainly computers and the internet. Hence,
the higher educational institutions should ensure that the proper infrastructural
facilities discussed in this study are provided to the students.
Disciplinary action
The disciplinary measures taken by the management should ensure that all the
measures are carried out with an ultimate objective of guiding the student to attain the
outcome of education.
Placement
The apex bodies UGC, AICTE and the governing bodies should take initiatives to
promote the concept of placement as the mediating factor for the higher education in
India. Industry institute interactions should be given top most priority from the design
of curriculum stage to the final recruitment stage. SQM-HEI regression model
empirically proved that placement as a mediating factor for service quality in higher
educational institutions. Hence, this model could be adopted in the higher educational
institutions for measuring the service quality in higher education.
Quality of education
The quality of education depends on the dimensions TM, ECSF and DA taken by the
management and their sub-dimensions with placement as the mediated factor. SQM-HEI
SEM will assist the academic institutions for mapping the level of service quality and
hence to enhance the same.
Conclusion
Parents are investing money on their children’s higher education, in anticipating immediate
return on their investment as the immediate placement from the higher education. In the
SQM of higher
education in India
75
JM2
6,1
study area, the mindset of the people is not towards the entrepreneurship, but towards an
immediate employability. There are many educational institutions in the study area
concentrating their efforts towards achieving a very high level of on campus placement
as the ultimate objective. They never fail to quote the same in all their promotional
campaigns. The mediated SQM-HEI model empirically proved that the placement is the
mediated factor for the quality higher education.
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76
References
Arbuckle, J.L. and Wothke, W. (2006), Amos 7.0 User’s Guide, SmallWaters Corporation, Chicago, IL.
Bagozzi, R.P. (1994), “Structural equation models in marketing research: basic principles”,
in Bagozzi, R.P. (Ed.), Principles of Marketing Research, Basil Blackwell, Cambridge, MA,
pp. 317-85.
Baron, R.M. and Kenny, D.A. (1986), “The moderator-mediator variable distinction in social
psychological research: conceptual, strategic, and statistical considerations”, Journal of
Personality and Social Psychology, Vol. 51, pp. 1173-82.
Batista-Foguet, J.M. and Coenders, G. (2000), Modelos de Ecuaciones Estructurales (Structural
Equation Models), La Muralla, Madrid.
Bing, M.N., Davison, H.K., LeBreton, D.L. and LeBreton, J.M. (2002), “Issues and improvements in
tests of mediation”, poster presented at the Annual Meeting of the Society for Industrial
and Organizational Psychology, Toronto.
Bollen, K.A. (1989), Structural Equations with Latent Variables, Wiley, New York, NY.
Boomsma, A. (1983), “On the robustness of LISREL (maximum likelihood estimation) against
small sample size and nonnormality”, doctoral dissertation, Rijksuniversiteit Groningen,
Groningen, Sociometric Research Foundation, Amsterdam.
Brown, D.J. and Koenig, H.F. (1993), “Applying total quality management to business education”,
Journal of Education for Business, Vol. 68, p. 329.
Browne, M.W. and Cudeck, R. (1993), “Alternative ways of assessing model fit”, in Bollen, K.A.
and Long, J.S. (Eds), Testing Structural Equation Models, Sage, Thousand Oaks, CA,
pp. 136-62.
Crawford, F. (1991), “Total quality management”, Committee of Vice-Chancellors and Principals
Occasional Paper, London, December.
Cronin, J.J. and Taylor, S.A. (1992), “Measuring service quality: a re-examination and extension”,
Journal of Marketing, Vol. 56, July, pp. 55-68.
Cuthbert, P.F. (1996), “Managing service quality in HE: is SERVQUAL the answer? Part 1”,
Managing Service Quality, Vol. 6 No. 2, pp. 11-16.
Delors, J., Al Mufti, I., Amagi, I., Carneiro, R., Chung, F., Geremek, B., Gorham, W., Kornhauser, A.,
Manley, M., Padrón Quero, M., Savané, M.-A., Singh, K., Stavenhagen, R., WonSuhr, M. and
Nanzhao, Z. (1996), Learning: The Treasure Within: Report to UNESCO of the International
Commission on Education for the Twenty-first Century, UNESCO, Paris, available at: www.
unesco.org/delors/
Finn, D.W. and Lamb, C.W. (1991), “An evaluation of the SERVQUAL scale in retail setting”,
in Holman, R.H. and Solomon, M.R. (Eds), Advances in Consumer Research, Association
for Consumer Research, Ann Arbor, MI, p. 18.
Firdaus, A. (2005a), “HEdPERF versus SERVPERF: the quest for ideal measuring instrument of
service quality in higher education sector”, Quality Assurance in Education, Vol. 13 No. 4,
pp. 305-28.
Downloaded by Universidad Autonoma de Baja California At 11:14 11 December 2018 (PT)
Firdaus, A. (2005b), “The development of HEdPERF: a new measuring instrument of service
quality for higher education”, International Journal of Consumer Studies, 20 October,
online publication.
Francis, G. (2003), Multiple Regression, Swinburne University Press, Melbourne.
Galloway, L. (1998), “Quality perceptions of internal and external customers: a case study in
educational administration”, The TQM Magazine, Vol. 10 No. 1, pp. 20-6.
Gelman, A., Carlin, J.B., Stern, H.S. and Rubin, D.B. (2004), Bayesian Data Analysis, 2nd ed.,
Chapman and Hall/CRC, Boca Raton, FL.
Hair, J.F. Jr, Anderson, R.E., Tatham, R.L. and Black, W.C. (1998), Multivariate Data Analysis with
Readings, 5th ed., Prentice-Hall, Englewood Cliffs, NJ.
Hill, F. (1995), “Managing service quality in higher education: the role of the student as primary
consumer”, Quality Assurance in Education, Vol. 3 No. 3, pp. 10-21.
Hill, Y., MacGregor, J. and Dewar, K. (1996), “Nurses’ access to higher education: a quality
product”, Quality Assurance in Education, Vol. 4, pp. 21-7.
Ho, S.K. and Wearn, K. (1996), “A higher education TQM excellence model: HETQMEX”, Quality
Assurance in Education, Vol. 4, pp. 35-42.
Holmbeck, G.N. (1997), “Toward terminological, conceptual, and statistical clarity in the study of
mediators and moderators: examples from the child-clinical and pediatric psychology lite
ratures”, Journal of Consulting and Clinical Psychology, Vol. 4, pp. 599-610.
Hoyle, R.H. (Ed.) (1995), Structural Equation Modeling: Concepts, Issues and Applications, Sage,
Thousand Oaks, CA.
James, L.R. and Brett, J.M. (1984), “Mediators, moderators and tests of mediation”, Journal of
Applied Psychology, Vol. 69, pp. 307-21.
Krejcie, R. and Morgan, D. (1970), “Determining sample size for research activities”, Educational
and Psychological Measurement, Vol. 30, pp. 607-10.
Lassar, W.M., Manolis, C. and Winsor, R.D. (2000), “Service quality perspective and satisfaction
in private banking”, Journal of Services Marketing, Vol. 14 No. 3, pp. 244-71.
McElwee, G. and Redman, T. (1993), “Upward appraisal in practice – an illustrative example
using the QUALED model”, Education & Training, Vol. 35, pp. 27-31.
Mahapatra, S.S. and Khan, M.S. (2007), “A neural network approach for assessing quality in
technical education: an empirical study”, International Journal of Productivity and Quality
Management, Vol. 2 No. 3, pp. 287-306.
Mehta, S.C., Lalwani, A.K. and Han, S.L. (2000), “Service quality in retailing: relative efficiency of
alternative measurement scales for different product-service environments”, International
Journal of Retail & Distribution Management, Vol. 28 No. 2, pp. 62-72.
Oldfield, B.M. and Baron, S. (2000), “Student perceptions of service quality in a UK university
business and management faculty”, Quality Assurance in Education, Vol. 8, pp. 85-95.
Owlia, M.S. and Aspinwall, E.M. (1996), “Quality in higher education – a survey”, Total Quality
Management, Vol. 7 No. 2.
Rigotti, S. and Pitt, L. (1992), “SERVQUAL as a measuring instrument for service provider gaps
in business schools”, Management Research News, Vol. 15, pp. 9-17.
Robledo, M.A. (2001), “Measuring and managing service quality: integrating customer
expectations”, Managing Service Quality, Vol. 11 No. 1, pp. 22-31.
Schumacker, R.E. and Lomax, R.G. (1996), A Beginner’s Guide to Structural Equation Modeling,
Lawrence Erlbaum, Mahwah, NJ.
SQM of higher
education in India
77
JM2
6,1
Downloaded by Universidad Autonoma de Baja California At 11:14 11 December 2018 (PT)
78
Sirvanci, M. (1996), “Are students the true customers of higher education”, Quality Progress,
Vol. 29 No. 10, pp. 99-102.
Sureshchandar, G.S., Rajendran, C. and Anantharaman, R.N. (2001), “A holistic model for total
quality service”, International Journal of Service Industry Management, Vol. 12 No. 4,
pp. 378-412.
Tate, R. (1998), An Introduction to Modeling Outcomes in the Behavioral and Social Sciences,
Pearson Custom, Boston, MA.
Teas, R.K. (1993), “Expectations, performance evaluation, and consumers’ perceptions of
quality”, Journal of Marketing, Vol. 57, pp. 18-34.
Zeithaml, V. (1987), “Defining and relating price, perceived quality and perceived value”, Request
No 87-101, Marketing Science Institute, Cambridge, MA.
Further reading
Cronin, J.J. and Taylor, S.A. (1994), “SERVPERF versus SERVQUAL: reconciling performance
based and perception based ^ minus expectation ^ measurements of service quality”,
Journal of Marketing, Vol. 58, January, pp. 125-31.
Hill, Y., Lomas, L. and MacGregor, J. (2003), “Students’ perceptions of quality in higher
education”, Quality Assurance in Education, Vol. 11 No. 1, pp. 15-20.
Corresponding author
N. Senthilkumar can be contacted at: [email protected]
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motivation in Indian universities. Journal of Modelling in Management 11:2, 488-517. [Abstract] [Full
Text] [PDF]
22. Ehsan Sadeh, Mansour Garkaz. 2015. Explaining the mediating role of service quality between quality
management enablers and students' satisfaction in higher education institutes: the perception of managers.
Total Quality Management & Business Excellence 26:11-12, 1335-1356. [Crossref]
23. Syed Zamberi Ahmad. 2015. Evaluating student satisfaction of quality at international branch campuses.
Assessment & Evaluation in Higher Education 40:4, 488-507. [Crossref]
24. Ali Bassam Mahmoud, Bayan Khalifa. 2015. A confirmatory factor analysis for SERVPERF instrument
based on a sample of students from Syrian universities. Education + Training 57:3, 343-359. [Abstract]
[Full Text] [PDF]
25. C Neelaveni, S Manimaran. 2015. A study on students satısfactıon based on qualıty standards of
accedıtatıon ın hıgher educatıon. Educational Research and Reviews 10:3, 282-289. [Crossref]
26. Ismail Bakan, Tuba Buyukbese, Burcu Ersahan. 2014. The impact of total quality service (TQS) on
healthcare and patient satisfaction: An empirical study of Turkish private and public hospitals. The
International Journal of Health Planning and Management 29:3, 292-315. [Crossref]
27. V. Mohan Sivakumar, S.R. Devadasan, R. Murugesh. 2014. Theory and practice of knowledge managed
ISO 9001:2000 supported quality system. The TQM Journal 26:1, 30-49. [Abstract] [Full Text] [PDF]
28. Johan de Jager, Gbolahan Gbadamosi. 2013. Predicting students' satisfaction through service quality
in higher education. The International Journal of Management Education 11:3, 107-118. [Crossref]
29. David Schüller, Martina Rašticová, Štěpán Konečný. 2013. Measuring student satisfaction with the quality
of services offered by universities – Central European View. Acta Universitatis Agriculturae et Silviculturae
Mendelianae Brunensis 61:4, 1105-1112. [Crossref]
30. Mohd Zuhdi Ibrahim, Mohd Nizam Ab Rahman, Ruhizan M. Yasin. 2012. Assessing Students
Perceptions of Service Quality in Technical Educational and Vocational Training (TEVT) Institution in
Malaysia. Procedia - Social and Behavioral Sciences 56, 272-283. [Crossref]
31. Mohd Zuhdi Ibrahim, Mohd Nizam Ab Rahman, Ruhizan M. Yasin. Developing a measurement model
of service quality in skills training institutes using confirmatory factor analysis 1-7. [Crossref]
Downloaded by Universidad Autonoma de Baja California At 11:14 11 December 2018 (PT)
32. Hatice Camgoz-Akdag, Selim Zaim. 2012. Education: A Comparative Structural Equation Modeling
Study. Procedia - Social and Behavioral Sciences 47, 874-880. [Crossref]
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