Search Results

Author: Baldasaro, Ruth E.
Resulting in 1 citation.
1. Baldasaro, Ruth E.
Person Level Analysis in Latent Growth Curve Models
Ph.D. Dissertation, Department of Psychology, University of North Carolina at Chapel Hill, 2013
Cohort(s): Children of the NLSY79
Publisher: ProQuest Dissertations & Theses (PQDT)
Keyword(s): Growth Curves; Modeling, Growth Curve/Latent Trajectory Analysis; Peabody Individual Achievement Test (PIAT- Reading)

Permission to reprint the abstract has not been received from the publisher.

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.

The population values for the covariance matrix and mean vector for the latent trajectory parameters for the normal population come from McArdle and Bell (2000). The parameter estimates were obtained using a subset of data from the National Longitudinal Study of Youth (NLSY; Baker, Keck, Mott, & Quinlan, 1993; Chase-Lansdale, Mott, Brooks-Gunn, & Phillips, 1991). The data include 233 children who participated in the NLSY in 1986 when the children were ages 6 to 8. The data were selected because this subset of children had measures of their reading ability 4 times over 8 years. McArdle and Bell provide more information on how the reading scores were calculated. This study used the linear LGCM parameter estimates presented in Table 5.4 on page 83 of McArdle and Bell (2000)

Bibliography Citation
Baldasaro, Ruth E. Person Level Analysis in Latent Growth Curve Models. Ph.D. Dissertation, Department of Psychology, University of North Carolina at Chapel Hill, 2013.