Comparing Five Empirical Biodata Scoring Methods for Personnel Selection

PDF Version Also Available for Download.

Description

A biodata based personnel selection measure was created to improve the retention rate of Catalog Telemarketing Representatives at a major U.S. retail company. Five separate empirical biodata scoring methods were compared to examine their usefulness in predicting retention and reducing adverse impact. The Mean Standardized Criterion Method, the Option Criterion Correlation Method, Horizontal Percentage Method, Vertical Percentage Method, and Weighted Application Blank Method using England's (1971) Assigned Weights were employed. The study showed that when using generalizable biodata items, all methods, except the Weighted Application Blank Method, were similar in their ability to discriminate between low and high retention employees … continued below

Creation Information

Ramsay, Mark J. August 2002.

Context

This thesis is part of the collection entitled: UNT Theses and Dissertations and was provided by the UNT Libraries to the UNT Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 11206 times, with 35 in the last month. More information about this thesis can be viewed below.

Who

People and organizations associated with either the creation of this thesis or its content.

Chair

Committee Members

Publisher

Rights Holder

For guidance see Citations, Rights, Re-Use.

  • Ramsay, Mark J.

Provided By

UNT Libraries

The UNT Libraries serve the university and community by providing access to physical and online collections, fostering information literacy, supporting academic research, and much, much more.

Contact Us

What

Descriptive information to help identify this thesis. Follow the links below to find similar items on the Digital Library.

Description

A biodata based personnel selection measure was created to improve the retention rate of Catalog Telemarketing Representatives at a major U.S. retail company. Five separate empirical biodata scoring methods were compared to examine their usefulness in predicting retention and reducing adverse impact. The Mean Standardized Criterion Method, the Option Criterion Correlation Method, Horizontal Percentage Method, Vertical Percentage Method, and Weighted Application Blank Method using England's (1971) Assigned Weights were employed. The study showed that when using generalizable biodata items, all methods, except the Weighted Application Blank Method, were similar in their ability to discriminate between low and high retention employees and produced similar low adverse impact effects. The Weighted Application Blank Method did not discriminate between the low and high retention employees.

Subjects

Language

Identifier

Unique identifying numbers for this thesis in the Digital Library or other systems.

Collections

This thesis is part of the following collection of related materials.

UNT Theses and Dissertations

Theses and dissertations represent a wealth of scholarly and artistic content created by masters and doctoral students in the degree-seeking process. Some ETDs in this collection are restricted to use by the UNT community.

What responsibilities do I have when using this thesis?

When

Dates and time periods associated with this thesis.

Creation Date

  • August 2002

Added to The UNT Digital Library

  • Sept. 26, 2007, 2:39 a.m.

Description Last Updated

  • June 23, 2008, 4:04 p.m.

Usage Statistics

When was this thesis last used?

Yesterday: 0
Past 30 days: 35
Total Uses: 11,206

Interact With This Thesis

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

International Image Interoperability Framework

IIF Logo

We support the IIIF Presentation API

Ramsay, Mark J. Comparing Five Empirical Biodata Scoring Methods for Personnel Selection, thesis, August 2002; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc3220/: accessed May 27, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .

Back to Top of Screen