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001 on1021186445;

003 OCoLC;

005 20180314112611.0;

006 m o d ;

007 cr unu||||||||;

008 180201s2017 ncua obm 000 0 eng d;

035 a| (Sirsi) o1021186445;

035 a| (OCoLC)1021186445;

049 a| EREE;

090 a| QA276;

100 1 a| Siver, Sydney R.,e| author.?| UNAUTHORIZED;

245 10 a| Methods for handling missing data for multiple-item questionnaires /c| by Sydney R. Siver.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2017.;

300 a| 54 pages :b| illustrations;

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 974.8 KB2| rda;

538 a| System requirements: Adobe Reader.;

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

502 b| M.A.c| East Carolina Universityd| 2017.;

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

500 a| Advisor: Alexander M. Schoemann;

500 a| Title from PDF t.p. (viewed February 28, 2018).;

520 3 a| Missing data is a common problem, especially in the social and behavioral sciences. Modern missing data methods are underutilized in the industrial/organizational psychology and human resource management literature. Recommendations for handling missing data and default options in software packages often use outdated, suboptimal methods for missing data. Resulting analyses tend to be biased, underpowered, or both. Best practice recommends for the handling of missing data includes the use of multiple imputation (MI) methods. However, this method is often ignored in favor of more convenient methods. For industrial/organizational psychologists, missing data is particularly problematic on multiple-item questionnaires, such as the Survey of Perceived Organizational Support (SPOS). Person mean imputation is one of the most common methods used to handle missing data on multiple-item questionnaires. However, it makes strong assumptions about the missing data mechanism and the underlying factor structure of a measure and should be avoided, particularly if there is a high rate of non-response. MI does not make the same assumptions as person mean imputation and may be a superior method when items are missing from a multiple-item questionnaire. Results indicate that PMI and MI provide similar results, however PMI may outperform MI when the number of variables is large.;

504 a| Includes bibliographical references.;

650 0 a| Missing observations (Statistics)=| ^A228878;

650 0 a| Multiple imputation (Statistics)=| ^A427597;

650 0 a| Questionnaires.=| ^A4312;

650 7 a| Missing observations (Statistics)2| fast0| (OCoLC)fst01023700;

650 7 a| Multiple imputation (Statistics)2| fast0| (OCoLC)fst01028979;

650 7 a| Questionnaires.2| fast0| (OCoLC)fst01085691;

653 a| person mean imputation;

653 a| Monte Carlo;

700 1 a| Schoemann, Alexander M.,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/10342/6517;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 4814939;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4814939-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 2/1/2018x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4814939-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 2/1/2018x| ETDz| HERESOURCE;

Methods for handling missing data for multiple-item questionnaires / by Sydney R. Siver.

Author/creator	Siver, Sydney R. author.
Other author	Schoemann, Alexander M., degree supervisor.
Other author	East Carolina University. Department of Psychology.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2017.
Description	54 pages : illustrations
Supplemental Content	Access via ScholarShip
Subjects	Missing observations (Statistics) Multiple imputation (Statistics) Questionnaires.

Summary	Missing data is a common problem, especially in the social and behavioral sciences. Modern missing data methods are underutilized in the industrial/organizational psychology and human resource management literature. Recommendations for handling missing data and default options in software packages often use outdated, suboptimal methods for missing data. Resulting analyses tend to be biased, underpowered, or both. Best practice recommends for the handling of missing data includes the use of multiple imputation (MI) methods. However, this method is often ignored in favor of more convenient methods. For industrial/organizational psychologists, missing data is particularly problematic on multiple-item questionnaires, such as the Survey of Perceived Organizational Support (SPOS). Person mean imputation is one of the most common methods used to handle missing data on multiple-item questionnaires. However, it makes strong assumptions about the missing data mechanism and the underlying factor structure of a measure and should be avoided, particularly if there is a high rate of non-response. MI does not make the same assumptions as person mean imputation and may be a superior method when items are missing from a multiple-item questionnaire. Results indicate that PMI and MI provide similar results, however PMI may outperform MI when the number of variables is large.
General note	Presented to the faculty of the Department of Psychology
General note	Advisor: Alexander M. Schoemann
General note	Title from PDF t.p. (viewed February 28, 2018).
Dissertation note	M.A. East Carolina University 2017.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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Methods for handling missing data for multiple-item questionnaires / by Sydney R. Siver.

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