A comparison of five methods for analyzing change with longitudinal panel data
| dc.contributor.advisor | Sechrest, Lee | en_US |
| dc.contributor.author | McKnight, Katherine May, 1963- | |
| dc.creator | McKnight, Katherine May | en_US |
| dc.date.accessioned | 2013-05-09T09:41:27Z | |
| dc.date.available | 2013-05-09T09:41:27Z | |
| dc.date.issued | 2000 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10150/289218 | |
| dc.description.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. | |
| dc.language.iso | en_US | en_US |
| dc.publisher | The University of Arizona. | en_US |
| dc.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. | en_US |
| dc.subject | Statistics. | en_US |
| dc.subject | Psychology, Clinical. | en_US |
| dc.subject | Psychology, Psychometrics. | en_US |
| dc.title | A comparison of five methods for analyzing change with longitudinal panel data | en_US |
| dc.type | text | en_US |
| dc.type | Dissertation-Reproduction (electronic) | en_US |
| thesis.degree.grantor | University of Arizona | en_US |
| thesis.degree.level | doctoral | en_US |
| dc.identifier.proquest | 9992101 | en_US |
| thesis.degree.discipline | Graduate College | en_US |
| thesis.degree.discipline | Psychology | en_US |
| thesis.degree.name | Ph.D. | en_US |
| dc.description.note | This item was digitized from a paper original and/or a microfilm copy. If you need higher-resolution images for any content in this item, please contact us at repository@u.library.arizona.edu. | |
| dc.identifier.bibrecord | .b41170118 | en_US |
| dc.description.admin-note | Original file replaced with corrected file July 2023. | |
| refterms.dateFOA | 2018-06-17T04:15:07Z | |
| html.description.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. |
