Author

John T. Kulas

Publication Date

2002

Document Type

Dissertation/Thesis

First Advisor

Finkelstein, Lisa M.

Degree Name

Ph.D. (Doctor of Philosophy)

Legacy Department

Department of Psychology

LCSH

Employees--Rating of--Psychological aspects; 360-degree feedback (Rating of employees); Self-perception

Abstract

The current research was conducted to clarify the meaning of computational self-awareness (mathematical discrepancy between self- and other-ratings) in multisource feedback. Through the application of Item Response Theory (IRT), the relationship between observed and underlying latent performance domains on a 360° assessment was compared for high versus low computationally self-aware individuals. These functional relationships were investigated to uncover potential sources of computational self-awareness variability. IRT analyses of differential item functioning (DIF) allowed for the comparison of latent performance domain/item endorsement relationships across high and low computationally self-aware groups. These analyses revealed two items that differentiated between high and low computationally self-aware individuals. Interpretation of the content of these items led to a “personal warmth” explanation of self-awareness differentiation. Classical Test Theory (CTT) ability distributions further suggest that high self-aware individuals may have more accurate self-ratings than do their low self-aware counterparts. These two perspectives can be utilized by future researchers to direct studies aimed at further understanding of the computational self-awareness construct.

Comments

Includes bibliographical references (pages [103]-108).

Extent

[x], 153 pages (some color pages)

Language

eng

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

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