Sains Malaysiana 42(8)(2013):
1159–1166
Perbandingan
Anggaran Parameter Terhadap Model Kecemerlangan Prestasi Institut
Pengajian TinggiBersandarkan Nilai Teras: Pendekatan Penganggaran
Kebolehjadian Maksimum (ML)
dan Kuasa Dua
Terkecil Separa (PLS)
(Comparison of Parameter Estimates on Value-based Performance
Excellence Model for Higher Education Institutes:
Approach of Maximum Likelihood (ML) and Partial Least
Squares (PLS) Estimations)
Mohd
Rashid Ab Hamid
Fakulti
Sains dan Teknologi Industri, Universiti Malaysia Pahang, Lebuhraya Tun Abdul
Razak
26300
UMP Kuantan, Pahang, Malaysia
Zainol
Mustafa*, Nur Riza Mohd Suradi
Pusat
Pengajian Sains Matematik, Fakulti Sains dan Teknologi, Universiti Kebangsaan
Malaysia, 43600 UKM Bangi, Selangor D.E.Malaysia
Fazli
Idris
Graduate
School of Business (GSB), Universiti Kebangsaan Malaysia
43600
UKM Bangi, Selangor D.E. Malaysia
Mokhtar
Abdullah
Universiti
Pertahanan Nasional Malaysia, Kem Sg. Besi, 53000 Kuala Lumpur, Malaysia
Diserahkan:
9 Mei 2012/Diterima: 17 Disember 2012
ABSTRAK
Pemodelan persamaan struktur (SEM) merupakan analisis
statistik multivariat yang mengkaji hubungan antara konstruk mengikut teori
atau kajian terdahulu melalui model hipotesis yang dibina. Kebiasaannya, kaedah
penganggaran yang digunakan dalam analisis pemodelan ini adalah penganggaran
kebolehjadian maksimum (ML). Kaedah
penganggaran tersebut memerlukan taburan data yang bersifat kenormalan
multivariat di samping memenuhi bilangan sampel yang tertentu. Oleh itu, penganggaran kuasa dua terkecil separa (PLS) amat berperanan
dalam mengatasi dua kekangan berkenaan dan isu multikolineariti. Oleh
itu makalah ini bertujuan untuk melakukan analisis perbandingan keputusan
pemodelan terhadap anggaran parameter dalam Model Kecemerlangan Prestasi
Institusi Pengajian Tinggi (IPT) bersandarkan nilai teras bagi mendapatkan
model akhir yang mematuhi kedua-dua teknik penganggaran ML dan PLS berkenaan. Model akhir merupakan model kecemerlangan yang disemak semula
berdasarkan tahap kesignifikanan secara statistik dan penting secara praktikal
bagi semua pekali lintasan dalam model. Kesimpulannya, kedua-dua teknik
penganggaran yang digunakan saling melengkapi antara satu sama lain dan memberikan nilai tambah kepada model hipotesis yang diuji.
Kata kunci: Analisis perbandingan; kebolehjadian maksimum; model
kecemerlangan prestasi IPT bersandarkan nilai teras; pemodelan
persamaan struktur; penganggaran kuasa dua terkecil separa
ABSTRACT
Structural equation modeling (SEM) is a multivariate
statistical analysis that examines the relationship between the constructs as
posited by theory or previous studies through the developed hypothesised model.
Usually, the estimation method used in the modeling analysis is the maximum
likelihood (ML)
estimation. This estimation method requires data that are multivariate normally distributed while meeting the required sample size. Following this, partial least squares (PLS) has its roles in overcoming those constraints and
multicollinearity issue. Therefore, this paper aimed to do comparative analysis
of the modeling results on parameter estimates of value-based performance
excellence model for Higher Education Institutions (HEIs) to obtain a final
model that meets the two estimation of ML and PLS. The final model is the revised model
based on the statistical significance and practically importance for all paths
in the model. In conclusion, both techniques used complement each other and
give an added value to the hypothesized model.
Keywords: Comparative analysis; maximum
likelihood; partial least squares estimation; structural equation modeling;
value-based performance excellence model for HEIs
RUJUKAN
Ab Hamid, M.R., Mustafa, Z., Mohd. Suradi, N.R., Idris, F., & Abdullah, M. 2012a. Model kecemerlangan
IPT berasaskan nilai teras: Pendekatan pemodelan kuasa dua terkecil separa. Jurnal Pengukuran Kualiti dan Analisis. Dalam
Penilaian.
Ab Hamid, M.R., Mustafa, Z., Mohd. Suradi, N.R., Idris, F. & Abdullah, M. 2012b. Value-based performance excellence measurement for higher education
institution: Instrument validation. Quality & Quantity DOI:
10.1007/s11135-012- 9699-y.
Ab Hamid, M.R., Mustafa, Z., Mohd. Suradi, N.R., Idris, F., Abdullah, M., Yaziz, S.R., Ismail@Mustofa,
Z. & Ibrahim, A. 2011a. Value-based performance excellence model for
Malaysian Technical Universities: On Bayesian structural equation modeling
(SEM). Batu Pahat, Johor. Malaysian Technical Universities
International Conference on Engineering & Technology (MUiCET 2011).
Ab Hamid, M.R., Mustafa, Z., Mohd. Suradi, N.R., Idris, F., Abdullah, M. & Ibrahim, A. 2011b. Value-based performance excellence model: Case studies at Malaysian Technical
Universities. Australian Journal of Basic and Applied Sciences 5(12):
628-633.
Barclay, D., Higgins, C.
& Thompson, R. 1995. The
partial least squares (PLS) approach to causal modeling: Personal computer
adoption and use as an illustration. Technology Studies 2(2): 285-309.
Barroso, C., Carrion, G.C.
& Roldan, J.L. 2010. Applying
maximum likelihood and PLS on different sample sizes: Studies on SERVQUAL model
and employee behavior model. In Handbook of Partial Least Squares,
edited by Vinzi, V.E., Chin, W.W., Henseler, J. & Wang, H. Springer
Handbooks of Computational Statistics: DOI: 10.1007/978- 3-540-32827-8_20.
Bollen, K.A. 1989. Structural
Equations with Latent Variables. New York: John Wiley & Sons.
Buhi, E.R., Goodson, P. & Neilands, T.B.
2007. Structural equation modeling: A primer for health behavior researchers. American
Journal of Health Behavior 31(1): 74-85.
Byrne, B.M. 2010. Structural
Equation Modeling with AMOS. Edisi ke-2. New York: Taylor &
Francis Group.
Cassel, C.M., Hackl, P.
& Westlund, A.H. 1999. Robustness of partial least-squares method for
estimating latent variable quality structures. Journal of Applied
Statistics 26(4): 435- 446.
Cassel, C.M., Hackl, P.
& Westlund, A.H. 2000. On measurement of intangible assets: A study of robustness of partial least squares. Total Quality Management 11(7):
897-907.
Chua, Y.P. 2009. Statistik Penyelidikan
Lanjutan. Kuala Lumpur: McGraw Hill Malaysia.
Fornell, C., Johnson, M.D.,
Anderson, E.W., Jaesung, C. & Bryant, B.E. 1996. The American customer satisfaction index:
Nature, purpose and findings. Journal of Marketing 60(4): 7-18.
Fornell, C. & Bookstein, F.L. 1982. Two
structural equation models: LISREL and PLS applied to customer exit-voice
theory. Journal of Marketing Research 19: 440-452. variables and measurement error. Journal of
Marketing Research 48: 39-50.
Gefen, D., Straub, D.W. & Boudreau, M.C. 2000. Structural equation modeling and regression: Guidelines for research practice. Communications
of the Association for Information Systems 4(7): 1-79.
Ghosh, S., Handfield, R.B., Kannan, V.R. & Tan, K.C.
2003. A structural model analysis of the
Malcolm Baldrige National Quality Award Framework. Int. J. Management
and Decision Making 4(4): 289-311.
Grewal, R., Cote, J.A. & Baumgartner, H. 2004. Multicollinearity and measurement error in structural equation models: Implications
for theory testing. Marketing Science 23(4): 519-529.
Habshah,
M. & Azmi, J. 2006. The misuse of statistical techniques in research: An
observation and experiences. Prosiding Seminar Kebangsaan Sains Kuantitatif.
Hair, J.F., Black, W.C., Babin, B.J. & Anderson, R.E.
2010. Multivariate Data Analysis. 7 ed. Upper Saddle River, New Jersey: Prentice Hall.
Hansmann,
K.W. & Ringle, C.M. 2005. Enterprise-networks and
strategic success – An empirical analysis. In Strategies for
Cooperation, edited by Theurl, T. & Meyer, E.C. hlm. 133-152. Aachen.
Henseler,
J., Ringle, C.M. & Sinkovics, R.R. 2009. The use of partial least squares
path modeling in international marketing. Advances in International
Marketing 20: 277-319.
Hides,
M.T., Davies, J. & Jackson, S. 2004. Implementation of EFQM excellence
model self-assessment in the UK Higher Education Sector – Lessons learned
from other sectors. The TQM Magazine 16(3): 194-201.
Holbert,
R.L. & Stephenson, M.T. 2002. Structural equation
modeling in the communication sciences (1995-2000). Human
Communication Research 28(4): 531-551.
Inkpen,
A.C. & Birkenshaw, J. 1994. International joint ventures and performance:
An interorganizational perspective. International Business Review 3(3):
201-217.
Jagpal,
H.S. 1982. Multicollinearity in structural equation models with unobservable
variables. Journal of Marketing Research 19: 431-439.
Jaskyte,
K. 2004. Transformational leadership, organizational culture
and innovativeness in nonprofit organisations. Nonprofit Management
& Leadership 15(2): 153-168.
Kline,
R.B. 2011. Principles and Practice of Structural Equation
Modeling. 3rd ed. New York: The Guilford
Press.
MacCallum, R.C., Roznowski, M., Mar, C.M. & Reith, J.V.
1994. Alternative strategies for cross-validation of covariance
structure models. Multivariate Behavioral Research 29(1): 1-32.
Malaysia
Productivity Corporation (MPC). 2010. Guidebook on Malaysia Business
Excellence Model: Transforming Business through Productivity & Innovation.
Petaling Jaya: Malaysia Productivity Corporation.
Mohd
Zaidi, I. & Mohd Sani, B. 2011. Good governance - adab oriented tadbir in
Islam. Kuala Lumpur: Institute Islamic Understanding Malaysia (IKIM).
Nik
Mustapha, N.H. 2011. Pentingnya penghayatan akhlak dalam
meningkat kualiti pengurusan organisasi. Majlis Penyampaian Sijil dan
Seminar MS1900:2005 Sistem Pengurusan Kualiti Keperluan dari Perspektif Islam. Institut Kefahaman Islam Malaysia (IKIM).
Norliza, A., Maizah Hura, A. & Robiah, A. 2006. A comparative study on some methods for handling multicollinearity
problems. Matematika 22(2): 109-119.
Oakland,
J. 1999. Winning Performance through Business Excellence. Credit Control.
20(7): 23-31.
Oakland,
J.S. & Tanner, S.J. 2008. The relationship between business excellence and
performance – An empirical study using Kanji’s leadership excellence
model. Total Quality Management and Business Excellence 19(7–8):
733-749.
Omil,
J.C., Lorenzo, P.C. & Liste, A.V. 2011. The power of
intangibles in high-profitability firms. Total Quality Management
& Business Excellence 22(1): 29-42.
Sang,
S.L., Lee, J.D. & Lee, J. 2010. E-government adoption in Cambodia: A
partial least squares approach. Transforming Government: People, Process and
Policy 4(2): 138-157.
Shah, R. & Goldstein, S.M. 2006. Use
of structural equation modeling in operations management research: Looking back
and forward. Journal of Operations Management 24: 148-169.
Schumacker,
R.E. & Lomax, R.G. 2004. A Beginner’s Guide to
Structural Equation Modeling. Taylor & Francis
Group.
Suhr,
D.D. 2002. SEM for Health, Business, and Education. Proceedings from the 27th Annual SAS® Users Group International
Conference. April 14-17, Orlando, Florida.
Westlund,
A.H., Kallstrom, M. & Parmler, J. 2008. SEM-based customer satisfaction
measurement: On multicollineary and robust PLS estimation. Total Quality
Management and Business Excellence 19(7-8): 855-869.
*Pengarang
untuk surat-menyurat; email: zbhm@ukm.my
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