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Person level analysis in latent grow...

Baldasaro, Ruth E.

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Person level analysis in latent growth curve models.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Person level analysis in latent growth curve models./
Author:	Baldasaro, Ruth E.
Description:	194 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
Contained By:	Dissertation Abstracts International74-09B(E).
Subject:	Psychology, Psychometrics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3562687
ISBN:	9781303104626

Person level analysis in latent growth curve models.
Baldasaro, Ruth E.

Person level analysis in latent growth curve models. - 194 p.

Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2013.

Latent growth curve modeling is an increasingly popular approach for evaluating longitudinal data. Researchers tend to focus on overall model fit information or component model fit information when evaluating a latent growth curve model (LGCM). However, there is also an interest in understanding a given individual's level and pattern of change over time, specifically an interest in identifying observations with aberrant patterns of change. Thus it is also important to examine model fit at the level of the individual. Currently there are several proposed approaches for evaluating person level fit information from a LGCM including factor score based approaches (Bollen & Curran, 2006; Coffman & Millsap, 2006) and person log-likelihood based approaches (Coffman & Millsap, 2006; McArdle, 1997). Even with multiple methods for evaluating person-level information, it is unusual for researchers to report any examination of the person level fit information. Researchers may be hesitant to use person level fit indices because there are very few studies that evaluate how effective these person level fit indices are at identifying aberrant observations, or what criteria to use with the indices. In order to better understand which approaches for evaluating person level information will perform best for LGCMs, this research uses simulation studies to examine the application of several person level fit indices to the detection of three types of aberrant observations including: extreme trajectory aberrance, extreme variability aberrance, and functional form aberrance. Results indicate that examining factor score estimates directly can help to identify extreme trajectory aberrance, while approaches examining factor score residuals or examining a person log-likelihood are better at identifying extreme variability aberrance. The performance of these approaches improved with more observation times and higher communality. All of the factor score estimate approaches were able to identify functional form aberrance, as long as there were a sufficient number of observation times and either higher communality or a greater difference between the functional forms of interest.

ISBN: 9781303104626Subjects--Topical Terms:

1017742
Psychology, Psychometrics.

Person level analysis in latent growth curve models.
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245 1 0 $a Person level analysis in latent growth curve models.
300 $a 194 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
500 $a Adviser: Daniel J. Bauer.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2013.
520 $a Latent growth curve modeling is an increasingly popular approach for evaluating longitudinal data. Researchers tend to focus on overall model fit information or component model fit information when evaluating a latent growth curve model (LGCM). However, there is also an interest in understanding a given individual's level and pattern of change over time, specifically an interest in identifying observations with aberrant patterns of change. Thus it is also important to examine model fit at the level of the individual. Currently there are several proposed approaches for evaluating person level fit information from a LGCM including factor score based approaches (Bollen & Curran, 2006; Coffman & Millsap, 2006) and person log-likelihood based approaches (Coffman & Millsap, 2006; McArdle, 1997). Even with multiple methods for evaluating person-level information, it is unusual for researchers to report any examination of the person level fit information. Researchers may be hesitant to use person level fit indices because there are very few studies that evaluate how effective these person level fit indices are at identifying aberrant observations, or what criteria to use with the indices. In order to better understand which approaches for evaluating person level information will perform best for LGCMs, this research uses simulation studies to examine the application of several person level fit indices to the detection of three types of aberrant observations including: extreme trajectory aberrance, extreme variability aberrance, and functional form aberrance. Results indicate that examining factor score estimates directly can help to identify extreme trajectory aberrance, while approaches examining factor score residuals or examining a person log-likelihood are better at identifying extreme variability aberrance. The performance of these approaches improved with more observation times and higher communality. All of the factor score estimate approaches were able to identify functional form aberrance, as long as there were a sufficient number of observation times and either higher communality or a greater difference between the functional forms of interest.
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