Exploring Influential Factors for Model Selection in Latent Growth Curve Models
| Autor: | SHIH-CHIEH WENG, 翁士傑 |
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| Rok vydání: | 2007 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 95 The use of Latent Growth Curve Modeling(LGCM), in longitudinal study data analysis has been widely used in the field of Psychology, Education and Medical research. We are interested in observing the effect of time on the action or attitude of the subject in a general longitudinal study, whether these behavior vary as time progress. Hence, LGCM is a technique to analyze the repeat measurements of a variable at different time frame. This essay will discuss the use of LGCM with small sample size, to identify the accuracy of model selection or power using test statistics. In the past, researchers rarely investigate the use of LGCM with small sample size, and the model selection is mainly continues variable type. This research will follow the selection of model and use continues variable type. In the simulation study, the following six factors are used: number of sample sizes, variance of intercept, variance of slope, means of slope, covariance of intercept and slope, number of observation variables. This research uses the test statistics to identify the power performance of LGCM in small sample size. The research uses Monte Carlo simulation, first simulating the data need for analysis, then feedback the data in to the five nested model for simulation research to estimate the power performance in different affecting factor variation. The results of simulation indicate that the main factors are number of sample sizes, covariance of intercept and slope and the number of observation variables. In terms of the power of the sample size and model selection for this research, BIC model selection indicator has the best performance, followed by, Adjusted-BIC has the worst performance. This research has resulted in producing a power table for the use of experimental researchers. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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