Perbandingan anggaran parameter terhadap model kecemerlangan prestasi institut pengajian tinggi (IPT) bersandarkan nilai teras

Pendekatan penganggaran kebolehjadian maksimum (ML) dan kuasa dua terkecil separa (PLS)

Translated title of the contribution: Comparison of parameter estimates on value-based performance excellence model for Higher Education Institutes (HEI): Approach of maximum likelihood (ML) and partial least squares (PLS) estimations

Mohd Rashid Ab Hamid, Zainol Mustafa, Nur Riza Mohd. Suradi, Fazli Idris, Mokhtar Abdullah

Research output: Contribution to journalArticle

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 practical importance for all paths in the model. In conclusion, both techniques used complement each other and give an added value to the hypothesized model.

Original languageUndefined/Unknown
Pages (from-to)1159-1166
Number of pages8
JournalSains Malaysiana
Volume42
Issue number8
Publication statusPublished - Aug 2013

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Maximum likelihood
Education
Maximum likelihood estimation
Statistical methods

Keywords

  • Comparative analysis
  • Maximum likelihood
  • Partial least squares estimation
  • Structural equation modeling
  • Value-based performance excellence model for HEIs

ASJC Scopus subject areas

  • General

Cite this

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title = "Perbandingan anggaran parameter terhadap model kecemerlangan prestasi institut pengajian tinggi (IPT) bersandarkan nilai teras: Pendekatan penganggaran kebolehjadian maksimum (ML) dan kuasa dua terkecil separa (PLS)",
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 practical 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",
author = "{Ab Hamid}, {Mohd Rashid} and Zainol Mustafa and {Mohd. Suradi}, {Nur Riza} and Fazli Idris and Mokhtar Abdullah",
year = "2013",
month = "8",
language = "Undefined/Unknown",
volume = "42",
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journal = "Sains Malaysiana",
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T2 - Pendekatan penganggaran kebolehjadian maksimum (ML) dan kuasa dua terkecil separa (PLS)

AU - Ab Hamid, Mohd Rashid

AU - Mustafa, Zainol

AU - Mohd. Suradi, Nur Riza

AU - Idris, Fazli

AU - Abdullah, Mokhtar

PY - 2013/8

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N2 - 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 practical importance for all paths in the model. In conclusion, both techniques used complement each other and give an added value to the hypothesized model.

AB - 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 practical importance for all paths in the model. In conclusion, both techniques used complement each other and give an added value to the hypothesized model.

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