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Comparing artificial neural net with...

Baxter, James F.

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Comparing artificial neural net with multiple regression in a biodata criterion validation study.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Comparing artificial neural net with multiple regression in a biodata criterion validation study./
Author:	Baxter, James F.
Description:	72 p.
Notes:	Adviser: Antonio Santonastasi.
Contained By:	Dissertation Abstracts International67-12B.
Subject:	Artificial Intelligence. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3246087

Comparing artificial neural net with multiple regression in a biodata criterion validation study.
Baxter, James F.

Comparing artificial neural net with multiple regression in a biodata criterion validation study. - 72 p.

Adviser: Antonio Santonastasi.

Thesis (Ph.D.)--Capella University, 2007.

This research compared Artificial Neural Nets (ANNs) to multiple regression in a Biodata criterion validation study. Using four constructs derived from 15 Biodata questions, shared variance associated with oral interview scores were measured. We proposed: Biodata preselection inventory will predict Food Server oral interview success (H1); Using sequential regression, two Education constructs will predict Food Server Oral Interview success (H2); and (step 2) two Experience constructs will account for substantial incremental variance beyond that accounted for by two education constructs (H3); ANN will account for more shared variance compared to multiple regression (H4). Findings supported H1, H2, H3, and H4. Additional analysis of ANN should be conducted to clearly validate this technique.Subjects--Topical Terms:

769149
Artificial Intelligence.

Comparing artificial neural net with multiple regression in a biodata criterion validation study.
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008 110927s2007 eng d
035 $a (UMI)AAI3246087
035 $a AAI3246087
040 $a UMI $c UMI
100 1 $a Baxter, James F. $3 1296592
245 1 0 $a Comparing artificial neural net with multiple regression in a biodata criterion validation study.
300 $a 72 p.
500 $a Adviser: Antonio Santonastasi.
500 $a Source: Dissertation Abstracts International, Volume: 67-12, Section: B, page: 7423.
502 $a Thesis (Ph.D.)--Capella University, 2007.
520 $a This research compared Artificial Neural Nets (ANNs) to multiple regression in a Biodata criterion validation study. Using four constructs derived from 15 Biodata questions, shared variance associated with oral interview scores were measured. We proposed: Biodata preselection inventory will predict Food Server oral interview success (H1); Using sequential regression, two Education constructs will predict Food Server Oral Interview success (H2); and (step 2) two Experience constructs will account for substantial incremental variance beyond that accounted for by two education constructs (H3); ANN will account for more shared variance compared to multiple regression (H4). Findings supported H1, H2, H3, and H4. Additional analysis of ANN should be conducted to clearly validate this technique.
590 $a School code: 1351.
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Psychology, Behavioral. $3 1017677
650 4 $a Psychology, Psychometrics. $3 1017742
690 $a 0384
690 $a 0632
690 $a 0800
710 2 0 $a Capella University. $3 1017687
773 0 $t Dissertation Abstracts International $g 67-12B.
790 $a 1351
790 1 0 $a Santonastasi, Antonio, $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3246087