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Performance of Number of Factors Pro...

Porritt, Marc Thomas.

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Performance of Number of Factors Procedures in Small Sample Sizes.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Performance of Number of Factors Procedures in Small Sample Sizes./
作者:	Porritt, Marc Thomas.
面頁冊數:	82 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.
Contained By:	Dissertation Abstracts International76-02B(E).
標題:	Quantitative psychology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3641417
ISBN:	9781321275872

Performance of Number of Factors Procedures in Small Sample Sizes.
Porritt, Marc Thomas.

Performance of Number of Factors Procedures in Small Sample Sizes. - 82 p.

Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.

Thesis (Ph.D.)--Loma Linda University, 2015.

This item must not be sold to any third party vendors.

Recent studies have indicated that under the proper circumstances factor anaylisis may be accurately performed in samples as small as N = 9. However, all of these studies have extracted a pre-known number of factors, leaving an examination of determining the proper number of factors to future studies. The current study uses examines the following methods for determining the proper number of factors: Monte Carlo data to examine the performance of common versions of the Kaiser Rule, minimum average partial, parallel analysis and salient loading criteria under the conditions created by all possible combinations of method, model strength, overdetermination and sample size. Method performance was compared for overall accuracy (percent correct), and average discrepancy (mean difference from correct). ANOVA revealed that item level methods, including salient loading criteria and MAP procedures, maintain accuracy when model strength is at least moderate and overdetermiantion is high. Use of selected empirical methods for determining the number of factors is possible in small sample sizes only when overdetermination and model strength are adequately high, larger sample sizes should be preferred when possible.

ISBN: 9781321275872Subjects--Topical Terms:

2144748
Quantitative psychology.

Performance of Number of Factors Procedures in Small Sample Sizes.
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245 1 0 $a Performance of Number of Factors Procedures in Small Sample Sizes.
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500 $a Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.
500 $a Adviser: Kendal C. Boyd.
502 $a Thesis (Ph.D.)--Loma Linda University, 2015.
506 $a This item must not be sold to any third party vendors.
520 $a Recent studies have indicated that under the proper circumstances factor anaylisis may be accurately performed in samples as small as N = 9. However, all of these studies have extracted a pre-known number of factors, leaving an examination of determining the proper number of factors to future studies. The current study uses examines the following methods for determining the proper number of factors: Monte Carlo data to examine the performance of common versions of the Kaiser Rule, minimum average partial, parallel analysis and salient loading criteria under the conditions created by all possible combinations of method, model strength, overdetermination and sample size. Method performance was compared for overall accuracy (percent correct), and average discrepancy (mean difference from correct). ANOVA revealed that item level methods, including salient loading criteria and MAP procedures, maintain accuracy when model strength is at least moderate and overdetermiantion is high. Use of selected empirical methods for determining the number of factors is possible in small sample sizes only when overdetermination and model strength are adequately high, larger sample sizes should be preferred when possible.
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