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The empirical nature and statistical...

Tannenbaum, Christyn E.

The empirical nature and statistical treatment of missing data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The empirical nature and statistical treatment of missing data.
作者:	Tannenbaum, Christyn E.
面頁冊數:	108 p.
附註:	Source: Dissertation Abstracts International, Volume: 70-10, Section: A, page: .
附註:	Adviser: Robert Boruch.
Contained By:	Dissertation Abstracts International70-10A.
標題:	Education, Tests and Measurements.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3381876
ISBN:	9781109431223

The empirical nature and statistical treatment of missing data.
Tannenbaum, Christyn E.

The empirical nature and statistical treatment of missing data. - 108 p.

Source: Dissertation Abstracts International, Volume: 70-10, Section: A, page: .

Thesis (Ph.D.)--University of Pennsylvania, 2009.

Introduction. Missing data is a common problem in research and can produce severely misleading analyses, including biased estimates of statistical parameters, and erroneous conclusions. In its 1999 report, the APA Task Force on Statistical Inference encouraged authors to report complications such as missing data and discouraged the use of traditional missing data methods, such as listwise and pairwise deletion. While many advances in the statistical treatment of missing data have been made, it remains to be seen whether these procedures are applied in practice. In their study examining missing data reporting practices of studies published in 1999 and 2003, Peugh and Enders found that missing data was rarely acknowledged and, when it was addressed, out-of-date statistical methods were used in response.

ISBN: 9781109431223Subjects--Topical Terms:

212776
Education, Tests and Measurements.

The empirical nature and statistical treatment of missing data.
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500 $a Source: Dissertation Abstracts International, Volume: 70-10, Section: A, page: .
500 $a Adviser: Robert Boruch.
502 $a Thesis (Ph.D.)--University of Pennsylvania, 2009.
520 $a Introduction. Missing data is a common problem in research and can produce severely misleading analyses, including biased estimates of statistical parameters, and erroneous conclusions. In its 1999 report, the APA Task Force on Statistical Inference encouraged authors to report complications such as missing data and discouraged the use of traditional missing data methods, such as listwise and pairwise deletion. While many advances in the statistical treatment of missing data have been made, it remains to be seen whether these procedures are applied in practice. In their study examining missing data reporting practices of studies published in 1999 and 2003, Peugh and Enders found that missing data was rarely acknowledged and, when it was addressed, out-of-date statistical methods were used in response.
520 $a Purpose. The purpose of this dissertation is to (a) provide an overview of the causes, assumptions, misconceptions, and statistical remedies regarding missing data in applied research; (b) replicate partially, extend, and expand the Peugh and Enders findings; (c) identify the ways in which missing data are addressed; and (d) assess current reporting practices.
520 $a Sample. Data from 1,106 studies were collected from the 24 educational and applied psychological journals published in 2007.
520 $a Methods. Data were collected on a number of points including the amount of missing data, the missing data method used, the study design, the cause of missing data, the type of missing data, the underlying nature of the missing data, the missing data mechanism, and whether or not a power analysis was performed.
520 $a Findings. Compared to the Peugh and Enders findings, the current sample of studies published in 2007 showed improvements in the assessment and treatment of missing data. More studies explicitly acknowledged missing data, modern missing data methods were employed more often, more studies discussed missing data in detail, and the underlying nature of the missing data was examined more often. However, the way in which many studies reported the amount, type, and cause of missing data was often inaccurate and/or unclear. This dissertation gives a statistical and narrative description of results and provides recommendations for improvement.
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