Kimberly L Fine
Biographic Data
| ID | 8867645 |
|---|---|
| NAME | Kimberly L Fine |
| GIVEN NAMES | Kimberly L |
| FAMILY NAME | Fine |
| SIGNATURE | FINE K L |
| AFFILIATIONS | Arizona State University |
| ORCID | 0000-0001-9900-5079 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
The Importance of Time Metric Precision When Implementing Bivariate Latent Change Score Models
The literature on latent change score models does not discuss the importance of using a precise time metric when structuring the data. This study examined the influence of time metric precision on model estimation, model interpretation, and parameter estimate accuracy in bivariate LCS (BLCS) models through simulation. Longitudinal data were generated with a panel study where assessments took place during a given time window with variation in star…
Accounting for standard errors of measurement when modeling change
Modeling within-person change over time and between-person differences in change over time is a primary goal in prevention science. When modeling change in an observed score over time with multilevel or structural equation modeling approaches, each observed score counts toward the estimation of model parameters equally. However, observed scores can differ in terms of their precision—both within and across participants. We propose an approach to w…
Examination of Nonlinear and Functional Mixed-Effects Models with Nonparametrically Generated Data
Previous research has shown functional mixed-effects models and traditional mixed-effects models perform similarly when recovering individual trajectories when data were generated following a parametric structure. We extend this previous work and compare nonlinear mixed-effects (NMEM) and functional mixed-effects models' (FMEM) ability to recover underlying trajectories when generated from an inherently nonparametric process. Nonlinear trajectori…
An Examination of a Functional Mixed-Effects Modeling Approach to the Analysis of Longitudinal Data
Growth curve modeling is one of the main analytical approaches to study change over time. Growth curve models are commonly estimated in the linear and nonlinear mixed-effects modeling framework in which both the mean and person-specific curves are modeled parametrically with functions of time such as the linear, quadratic, and exponential. However, when more complex nonlinear trajectories need to be estimated and researchers do not have a priori …
No prominent works on this page.
An Examination of a Functional Mixed-Effects Modeling Approach to the Analysis of Longitudinal Data
Growth curve modeling is one of the main analytical approaches to study change over time. Growth curve models are commonly estimated in the linear and nonlinear mixed-effects modeling framework in which both the mean and person-specific curves are modeled parametrically with functions of time such as the linear, quadratic, and exponential. However, when more complex nonlinear trajectories need to be estimated and researchers do not have a priori …
Accounting for standard errors of measurement when modeling change
Modeling within-person change over time and between-person differences in change over time is a primary goal in prevention science. When modeling change in an observed score over time with multilevel or structural equation modeling approaches, each observed score counts toward the estimation of model parameters equally. However, observed scores can differ in terms of their precision—both within and across participants. We propose an approach to w…
Examination of Nonlinear and Functional Mixed-Effects Models with Nonparametrically Generated Data
Previous research has shown functional mixed-effects models and traditional mixed-effects models perform similarly when recovering individual trajectories when data were generated following a parametric structure. We extend this previous work and compare nonlinear mixed-effects (NMEM) and functional mixed-effects models' (FMEM) ability to recover underlying trajectories when generated from an inherently nonparametric process. Nonlinear trajectori…
The Importance of Time Metric Precision When Implementing Bivariate Latent Change Score Models
The literature on latent change score models does not discuss the importance of using a precise time metric when structuring the data. This study examined the influence of time metric precision on model estimation, model interpretation, and parameter estimate accuracy in bivariate LCS (BLCS) models through simulation. Longitudinal data were generated with a panel study where assessments took place during a given time window with variation in star…
Computer Science (4 works) · Mathematics (4 works) · Statistics (4 works) · Econometrics (3 works) · Functional data analysis (2 works) · Mental Health Research Topics (2 works) · Mixed model (2 works) · Nonlinear system (2 works) · Nonparametric statistics (2 works) · Parametric model (2 works)