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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.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Comparing artificial neural net with multiple regression in a biodata criterion validation study./
作者:	Baxter, James F.
面頁冊數:	72 p.
附註:	Adviser: Antonio Santonastasi.
Contained By:	Dissertation Abstracts International67-12B.
標題:	Artificial Intelligence. -
電子資源:	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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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.
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650 4 $a Psychology, Behavioral. $3 1017677
650 4 $a Psychology, Psychometrics. $3 1017742
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710 2 0 $a Capella University. $3 1017687
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