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The sequential Kaiser-Meyer-Olkin procedure as an alternative for determining the number of factors in common-factor analysis : = A Monte Carlo simulation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The sequential Kaiser-Meyer-Olkin procedure as an alternative for determining the number of factors in common-factor analysis :/
其他題名:	A Monte Carlo simulation.
作者:	Hill, Brent Dale.
面頁冊數:	1 online resource (154 pages)
附註:	Source: Dissertations Abstracts International, Volume: 73-04, Section: B.
Contained By:	Dissertations Abstracts International73-04B.
標題:	Educational psychology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3474663click for full text (PQDT)
ISBN:	9781124914718

The sequential Kaiser-Meyer-Olkin procedure as an alternative for determining the number of factors in common-factor analysis : = A Monte Carlo simulation.
Hill, Brent Dale.

The sequential Kaiser-Meyer-Olkin procedure as an alternative for determining the number of factors in common-factor analysis :A Monte Carlo simulation. - 1 online resource (154 pages)

Source: Dissertations Abstracts International, Volume: 73-04, Section: B.

Thesis (Ph.D.)--Oklahoma State University, 2011.

Includes bibliographical references

Scope and Method of Study. Widely utilized in the behavioral and social sciences, common-factor analysis (CFA) is a statistical technique which is used to investigate the latent traits (factors) that underlie a set of observed variables. The proper number of factors to extract is a fundamental question in exploratory CFA, and many methods to answer that question have been devised. This study examines the performance characteristics (accuracy, precision, and bias) of four variants of the sequential Kaiser-Meyer-Olkin (SKMO), a new method for determining dimensionality in CFA. This study also compares the SKMO to various other well-known dimensionality tests, such as the Kaiser-Guttman criterion, Horn's parallel analysis, and Velicer's MAP test. This study was conducted using an extensive Monte Carlo simulation which manipulated the actual number of factors, the variable-to-factor ratio, the pattern-magnitude interval, sample size, and inter-factor correlations. Findings and Conclusions. The simulation revealed that the best-performing SKMO variant was that which incorporated noniterated communality estimation and a .50 cutoff. The simulation also showed that the SKMO performed better than most other number-of-factors tests, including the Kaiser-Guttman criterion and Velicer's MAP test. The SKMO was better than one version of parallel analysis and a close second to the remaining forms. These results suggest that the SKMO is a viable candidate for general use with CFA. However, this suggestion is tentative as further research is needed to determine the performance characteristics of the SKMO under increasingly complex conditions.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9781124914718Subjects--Topical Terms:

517650
Educational psychology.
Subjects--Index Terms:

Common factor analysisIndex Terms--Genre/Form:

542853
Electronic books.

The sequential Kaiser-Meyer-Olkin procedure as an alternative for determining the number of factors in common-factor analysis : = A Monte Carlo simulation.
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520 $a Scope and Method of Study. Widely utilized in the behavioral and social sciences, common-factor analysis (CFA) is a statistical technique which is used to investigate the latent traits (factors) that underlie a set of observed variables. The proper number of factors to extract is a fundamental question in exploratory CFA, and many methods to answer that question have been devised. This study examines the performance characteristics (accuracy, precision, and bias) of four variants of the sequential Kaiser-Meyer-Olkin (SKMO), a new method for determining dimensionality in CFA. This study also compares the SKMO to various other well-known dimensionality tests, such as the Kaiser-Guttman criterion, Horn's parallel analysis, and Velicer's MAP test. This study was conducted using an extensive Monte Carlo simulation which manipulated the actual number of factors, the variable-to-factor ratio, the pattern-magnitude interval, sample size, and inter-factor correlations. Findings and Conclusions. The simulation revealed that the best-performing SKMO variant was that which incorporated noniterated communality estimation and a .50 cutoff. The simulation also showed that the SKMO performed better than most other number-of-factors tests, including the Kaiser-Guttman criterion and Velicer's MAP test. The SKMO was better than one version of parallel analysis and a close second to the remaining forms. These results suggest that the SKMO is a viable candidate for general use with CFA. However, this suggestion is tentative as further research is needed to determine the performance characteristics of the SKMO under increasingly complex conditions.
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