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Evaluating Psychometric and Imputati...

Luningham, Justin M.

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Evaluating Psychometric and Imputation-Based Methods for Data Integration.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Evaluating Psychometric and Imputation-Based Methods for Data Integration./
Author:	Luningham, Justin M.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	130 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-08, Section: B.
Contained By:	Dissertations Abstracts International80-08B.
Subject:	Quantitative psychology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13836272
ISBN:	9780438834057

Evaluating Psychometric and Imputation-Based Methods for Data Integration.
Luningham, Justin M.

Evaluating Psychometric and Imputation-Based Methods for Data Integration. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 130 p.

Source: Dissertations Abstracts International, Volume: 80-08, Section: B.

Thesis (Ph.D.)--University of Notre Dame, 2018.

IDA refers to combining data from independent studies into a concatenated dataset and analyzing the new data jointly (Curran & Hussong, 2009). IDA is an alternative to meta-analysis, which synthesizes summary statistics (such as effect sizes or standardized parameter estimates) from multiple studies. Pooling data for research is not a new idea; however, it has seen limited use in psychology. The IDA framework is innovative in its use of different models and methods to account for heterogeneity across different data sources. This dissertation proposal evaluates the performance of particular applications of IDA in the field of behavior genetics, where meta-analysis is common practice. Behavior genetics researchers often perform exhaustive searches across the entire genome seeking genetic markers that are associated with an observed outcome. Because there are millions of genetic markers, and because even true gene associations have extremely tiny effect sizes, genome-wide searches are often combined across multiple studies to increase statistical power. Regression coefficients from tests of association are meta-analyzed, even though psychological or behavioral outcomes may be measured or defined differently across studies. This dissertation tests the hypothesis that more precise measurement of an integrated outcome, achieved through IDA, can provide added power over typical meta-analysis in multi-study genome- wide searches. In chapter 1, a general overview of the IDA framework is discussed, genome-wide association studies are presented, and IDA for genome-wide searches is presented. Chapter 2 covers a simulation study evaluating measurement model IDA using a proposed bi-factor integration model. Chapter 3 covers simulation studies investigating multiple imputation IDA performance with a novel imputation model, namely, boosted decision trees as an extension of single-tree imputation. Chapter 4 presents an application of IDA to the Aggression in Children: Unraveling gene- environment interplay to inform Treatment and InterventiON strategies (ACTION) Consortium, a large-scale collaboration of six research projects studying the genetic underpinnings of aggression in children.

ISBN: 9780438834057Subjects--Topical Terms:

2144748
Quantitative psychology.
Subjects--Index Terms:

Genetic consortia

Evaluating Psychometric and Imputation-Based Methods for Data Integration.
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300 $a 130 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-08, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Lubke, Gitta H.
502 $a Thesis (Ph.D.)--University of Notre Dame, 2018.
520 $a IDA refers to combining data from independent studies into a concatenated dataset and analyzing the new data jointly (Curran & Hussong, 2009). IDA is an alternative to meta-analysis, which synthesizes summary statistics (such as effect sizes or standardized parameter estimates) from multiple studies. Pooling data for research is not a new idea; however, it has seen limited use in psychology. The IDA framework is innovative in its use of different models and methods to account for heterogeneity across different data sources. This dissertation proposal evaluates the performance of particular applications of IDA in the field of behavior genetics, where meta-analysis is common practice. Behavior genetics researchers often perform exhaustive searches across the entire genome seeking genetic markers that are associated with an observed outcome. Because there are millions of genetic markers, and because even true gene associations have extremely tiny effect sizes, genome-wide searches are often combined across multiple studies to increase statistical power. Regression coefficients from tests of association are meta-analyzed, even though psychological or behavioral outcomes may be measured or defined differently across studies. This dissertation tests the hypothesis that more precise measurement of an integrated outcome, achieved through IDA, can provide added power over typical meta-analysis in multi-study genome- wide searches. In chapter 1, a general overview of the IDA framework is discussed, genome-wide association studies are presented, and IDA for genome-wide searches is presented. Chapter 2 covers a simulation study evaluating measurement model IDA using a proposed bi-factor integration model. Chapter 3 covers simulation studies investigating multiple imputation IDA performance with a novel imputation model, namely, boosted decision trees as an extension of single-tree imputation. Chapter 4 presents an application of IDA to the Aggression in Children: Unraveling gene- environment interplay to inform Treatment and InterventiON strategies (ACTION) Consortium, a large-scale collaboration of six research projects studying the genetic underpinnings of aggression in children.
590 $a School code: 0165.
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653 $a Genetic consortia
653 $a Integrative data analysis
653 $a Multiple imputation
653 $a Twin modeling
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793 $a English
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