A comparison of five methods for analyzing change with longitudinal panel data

Persistent Link:
http://hdl.handle.net/10150/289218
Title:
A comparison of five methods for analyzing change with longitudinal panel data
Author:
McKnight, Katherine May
Issue Date:
2000
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Within the past few decades, methodologists have made major advances in statistical methods for the analysis of change using longitudinal panel data, particularly in the area of modeling individual differences (Bryk & Raudenbush, 1987; Collins & Horn, 1991; Rogosa, 1991; Willett & Sayer, 1994; Willett, Singer, & Martin, 1998). These advances have made it possible for researchers to measure change and the correlates of change in ways that were not thought possible a few decades ago. These improvements should allow researchers to make stronger and more informed inferences regarding change over time. Despite the improvements individual growth modeling methods represent for the analysis of change, it remains unclear as to their adequacy for informing about individual differences with respect to change. The purpose of the present study was to directly compare three general classes of individual growth modeling strategies with each other and with two commonly used traditional fixed effects models of change in order to assess (a) the conclusions that can be drawn about change in general and about individual differences in change in particular; and (b) the robustness or stability of these various data analytic strategies.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Statistics.; Psychology, Clinical.; Psychology, Psychometrics.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Psychology
Degree Grantor:
University of Arizona
Advisor:
Sechrest, Lee

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleA comparison of five methods for analyzing change with longitudinal panel dataen_US
dc.creatorMcKnight, Katherine Mayen_US
dc.contributor.authorMcKnight, Katherine Mayen_US
dc.date.issued2000en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractWithin the past few decades, methodologists have made major advances in statistical methods for the analysis of change using longitudinal panel data, particularly in the area of modeling individual differences (Bryk & Raudenbush, 1987; Collins & Horn, 1991; Rogosa, 1991; Willett & Sayer, 1994; Willett, Singer, & Martin, 1998). These advances have made it possible for researchers to measure change and the correlates of change in ways that were not thought possible a few decades ago. These improvements should allow researchers to make stronger and more informed inferences regarding change over time. Despite the improvements individual growth modeling methods represent for the analysis of change, it remains unclear as to their adequacy for informing about individual differences with respect to change. The purpose of the present study was to directly compare three general classes of individual growth modeling strategies with each other and with two commonly used traditional fixed effects models of change in order to assess (a) the conclusions that can be drawn about change in general and about individual differences in change in particular; and (b) the robustness or stability of these various data analytic strategies.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectStatistics.en_US
dc.subjectPsychology, Clinical.en_US
dc.subjectPsychology, Psychometrics.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplinePsychologyen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorSechrest, Leeen_US
dc.identifier.proquest9992101en_US
dc.identifier.bibrecord.b41170118en_US
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