東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Estimates of Type I error and power ...

Russell, Steven S.

Linked to FindBook

Google Book

Amazon

博客來

Estimates of Type I error and power for indices of differential bundle and test functioning.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Estimates of Type I error and power for indices of differential bundle and test functioning./
Author:	Russell, Steven S.
Description:	97 p.
Notes:	Adviser: Michael J. Zickar.
Contained By:	Dissertation Abstracts International66-05B.
Subject:	Psychology, Industrial. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3175804
ISBN:	9780542155574

Estimates of Type I error and power for indices of differential bundle and test functioning.
Russell, Steven S.

Estimates of Type I error and power for indices of differential bundle and test functioning. - 97 p.

Adviser: Michael J. Zickar.

Thesis (Ph.D.)--Bowling Green State University, 2005.

Analyzing single items for differential item functioning (DIF) provides valuable psychometric information, especially during the early stages of test validation. There are also numerous advantages to be gained by conducting DIF analyses at aggregate levels, such as comparing multiple items simultaneously, allowing DIF amplification or cancellation effects, and exploring sources of test bias. This study evaluated the aggregate-level DIF detection capabilities of two IRT-based techniques---DFIT and SIBTEST---using Monte Carlo performance criteria. Estimates of Type I error and power rates were computed across several variables (e.g., sample size, test dimensionality) for differential test functioning (using DFIT) and differential bundle functioning (using SIBTEST). The overall Type I error rates for both techniques were inflated beyond the nominal rejection rate, and varied quite a bit according to the manipulated variables in the study. The overall power rate for DFIT was fairly low, whereas SIBTEST demonstrated satisfactory power. In follow-up analyses, the techniques were used jointly to examine archival datasets. Implications for organizational research are discussed, including future research directions.

ISBN: 9780542155574Subjects--Topical Terms:

520063
Psychology, Industrial.

Estimates of Type I error and power for indices of differential bundle and test functioning.
LDR:02134nam 2200277 a 45 001 969798
005 20110920
008 110921s2005 eng d
020 $a 9780542155574
035 $a (UnM)AAI3175804
035 $a AAI3175804
040 $a UnM $c UnM
100 1 $a Russell, Steven S. $3 1293855
245 1 0 $a Estimates of Type I error and power for indices of differential bundle and test functioning.
300 $a 97 p.
500 $a Adviser: Michael J. Zickar.
500 $a Source: Dissertation Abstracts International, Volume: 66-05, Section: B, page: 2867.
502 $a Thesis (Ph.D.)--Bowling Green State University, 2005.
520 $a Analyzing single items for differential item functioning (DIF) provides valuable psychometric information, especially during the early stages of test validation. There are also numerous advantages to be gained by conducting DIF analyses at aggregate levels, such as comparing multiple items simultaneously, allowing DIF amplification or cancellation effects, and exploring sources of test bias. This study evaluated the aggregate-level DIF detection capabilities of two IRT-based techniques---DFIT and SIBTEST---using Monte Carlo performance criteria. Estimates of Type I error and power rates were computed across several variables (e.g., sample size, test dimensionality) for differential test functioning (using DFIT) and differential bundle functioning (using SIBTEST). The overall Type I error rates for both techniques were inflated beyond the nominal rejection rate, and varied quite a bit according to the manipulated variables in the study. The overall power rate for DFIT was fairly low, whereas SIBTEST demonstrated satisfactory power. In follow-up analyses, the techniques were used jointly to examine archival datasets. Implications for organizational research are discussed, including future research directions.
590 $a School code: 0018.
650 4 $a Psychology, Industrial. $3 520063
650 4 $a Psychology, Psychometrics. $3 1017742
690 $a 0624
690 $a 0632
710 2 0 $a Bowling Green State University. $3 676285
773 0 $t Dissertation Abstracts International $g 66-05B.
790 $a 0018
790 1 0 $a Zickar, Michael J., $e advisor
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3175804