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Estimating performance mean and vari...

Colatat, Mahyulee Chang.

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Estimating performance mean and variability with distributional ratings scales: A field study towards enhanced performance measurement.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Estimating performance mean and variability with distributional ratings scales: A field study towards enhanced performance measurement./
Author:	Colatat, Mahyulee Chang.
Description:	59 p.
Notes:	Adviser: Milton Hakel.
Contained By:	Dissertation Abstracts International69-02B.
Subject:	Business Administration, Management. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3301777
ISBN:	9780549471875

Estimating performance mean and variability with distributional ratings scales: A field study towards enhanced performance measurement.
Colatat, Mahyulee Chang.

Estimating performance mean and variability with distributional ratings scales: A field study towards enhanced performance measurement. - 59 p.

Adviser: Milton Hakel.

Thesis (Ph.D.)--Bowling Green State University, 2008.

Research on distributional rating scales is mixed on whether they represent an improvement in performance measurement over traditional Likert-type scales. The present study attempted to reconcile the mixed results by suggesting that distributional ratings provide estimates of mean performance comparable to Likert-type ratings, yet also contribute conceptually critical estimates of performance variability unavailable from Likert-type ratings. Approximately 2,090 undergraduate students in 95 classes rated their instructors' performance. Data were collected in a between-classes design with random assignment to either distributional or Likert-type rating scale conditions. Results indicated no significant differences in estimates of mean performance or interrater agreement for mean performance between distributional and Likert-type rating scales. Further, raters used the distributional scale to report some degree of performance variability, and surprisingly, they agreed on variability estimates as much as or more than they agreed on mean estimates. Thus, distributional rating scales indeed have the potential to capture richer performance information than Likert-type scales.

ISBN: 9780549471875Subjects--Topical Terms:

626628
Business Administration, Management.

Estimating performance mean and variability with distributional ratings scales: A field study towards enhanced performance measurement.
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020 $a 9780549471875
035 $a (UMI)AAI3301777
035 $a AAI3301777
040 $a UMI $c UMI
100 1 $a Colatat, Mahyulee Chang. $3 1286411
245 1 0 $a Estimating performance mean and variability with distributional ratings scales: A field study towards enhanced performance measurement.
300 $a 59 p.
500 $a Adviser: Milton Hakel.
500 $a Source: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 1368.
502 $a Thesis (Ph.D.)--Bowling Green State University, 2008.
520 $a Research on distributional rating scales is mixed on whether they represent an improvement in performance measurement over traditional Likert-type scales. The present study attempted to reconcile the mixed results by suggesting that distributional ratings provide estimates of mean performance comparable to Likert-type ratings, yet also contribute conceptually critical estimates of performance variability unavailable from Likert-type ratings. Approximately 2,090 undergraduate students in 95 classes rated their instructors' performance. Data were collected in a between-classes design with random assignment to either distributional or Likert-type rating scale conditions. Results indicated no significant differences in estimates of mean performance or interrater agreement for mean performance between distributional and Likert-type rating scales. Further, raters used the distributional scale to report some degree of performance variability, and surprisingly, they agreed on variability estimates as much as or more than they agreed on mean estimates. Thus, distributional rating scales indeed have the potential to capture richer performance information than Likert-type scales.
590 $a School code: 0018.
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650 4 $a Psychology, Industrial. $3 520063
650 4 $a Psychology, Psychometrics. $3 1017742
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710 2 $a Bowling Green State University. $3 676285
773 0 $t Dissertation Abstracts International $g 69-02B.
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791 $a Ph.D.
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3301777