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008 160129s2015 ncua obm 000 0 eng d;

035 a| (Sirsi) o936213497;

035 a| (OCoLC)936213497;

049 a| EREE;

090 a| QA166.2;

100 1 a| Raines, Justin M.,e| author.?| UNAUTHORIZED;

245 14 a| The tree to success :b| using decision tree analysis to predict employee performance and health /c| by Justin M. Raines.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2015.;

300 a| 153 pages :b| illustrations;

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 1.23 MB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| Ph.D./Au.D.c| East Carolina Universityd| 2015.;

500 a| Presented to the faculty of the Department of Psychology.;

500 a| Advisor: Mark C. Bowler.;

500 a| Title from PDF t.p. (viewed March 3, 2016).;

504 a| Includes bibliographical references.;

520 3 a| The purpose of the current study was to evaluate the relative advantages and disadvantages of a novel statistical technique within the context of personnel decision-making. Specifically, the use of decision tree analysis was examined regarding its potential benefits over binary logistic regression. Using Monte Carlo simulation, a series of data sets were generated based on meta-analytic correlation matrices representing the topics of (1) employee performance and (2) employee health. Each data set was analyzed via both decision tree analysis and binary logistic regression with subsequent comparisons being made concerning model validity, adverse impact, and interpretability. Overall, decision tree analysis demonstrated a variety of benefits over the more traditional method. In general, decision tree analysis produced predictive models that possessed nearly equivalent levels of validity as models produced by logistic regression. Of greater importance, the majority of decision tree analysis models produced no adverse impact, whereas logistic regression models were largely associated with discriminatory results. Lastly, decision tree analysis models were generally more parsimonious and interpretable than the competing logistic regression models. The practical implications of these results are discussed and suggest that the use decision tree analysis holds the potential to greatly improve the way in which organizations make decisions regarding the productivity and health of employees.;

650 0 a| Decision trees.=| ^A295237;

650 0 a| Employeesx| Attitudes.=| ^A382108;

650 0 a| Work environment.=| ^A96199;

650 0 a| Monte Carlo method.=| ^A37649;

650 0 a| Regression analysis.=| ^A1986;

653 a| Employee Health;

653 a| Employee Performance;

653 a| Selection;

700 1 a| Bowler, Mark C.,e| degree supervisor.?| UNAUTHORIZED;

710 2 a| East Carolina University.b| Department of Psychology.?| UNAUTHORIZED;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/5128;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 4356315;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4356315-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 1/29/2016x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4356315-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 1/29/2016x| ETDz| HERESOURCE;

The tree to success : using decision tree analysis to predict employee performance and health / by Justin M. Raines.

Author/creator	Raines, Justin M. author.
Other author	Bowler, Mark C., degree supervisor.
Other author	East Carolina University. Department of Psychology.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2015.
Description	153 pages : illustrations
Supplemental Content	Access via ScholarShip
Subjects	Decision trees. Employees--Attitudes. Work environment. Monte Carlo method. Regression analysis.

Summary	The purpose of the current study was to evaluate the relative advantages and disadvantages of a novel statistical technique within the context of personnel decision-making. Specifically, the use of decision tree analysis was examined regarding its potential benefits over binary logistic regression. Using Monte Carlo simulation, a series of data sets were generated based on meta-analytic correlation matrices representing the topics of (1) employee performance and (2) employee health. Each data set was analyzed via both decision tree analysis and binary logistic regression with subsequent comparisons being made concerning model validity, adverse impact, and interpretability. Overall, decision tree analysis demonstrated a variety of benefits over the more traditional method. In general, decision tree analysis produced predictive models that possessed nearly equivalent levels of validity as models produced by logistic regression. Of greater importance, the majority of decision tree analysis models produced no adverse impact, whereas logistic regression models were largely associated with discriminatory results. Lastly, decision tree analysis models were generally more parsimonious and interpretable than the competing logistic regression models. The practical implications of these results are discussed and suggest that the use decision tree analysis holds the potential to greatly improve the way in which organizations make decisions regarding the productivity and health of employees.
General note	Presented to the faculty of the Department of Psychology.
General note	Advisor: Mark C. Bowler.
General note	Title from PDF t.p. (viewed March 3, 2016).
Dissertation note	Ph.D./Au.D. East Carolina University 2015.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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The tree to success : using decision tree analysis to predict employee performance and health / by Justin M. Raines.

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