Journal of Emerging Trends in Engineering and Applied Sciences: Research Areas

Abstract:
One of the basic observation in the social and behavioral sciences is that things are changed over the time. Longitudinal data analysis can yield valuable information about this change. Although many techniques have been developed to capitalize on these desirable features of longitudinal data, the structural equation modeling approach of building latent growth models (LGMs) has become one of the commonly used statistical models. A subset of data is taken from the National Longitudinal Survey 97, prepared by the Bureau of Labor Statistics, U.S. Four waves of mother monitoring reported by youth in the year 1997 to the year 2000 are used for the analysis. A total of 2675 adult respondents are used in our analysis. Mother monitoring scores reported by youth are used as a dependent variable. There are 52% male and 48% female in the data. Different linear, quadratic, autoregressive and moving average LGMs with gender as a covariate are used and compared to study the effects of mother monitoring over a 4 year period of time. It is found mom monitoring is increasing slowly over the period of time. An association was found between slop and intercept of fitted latent growth model and female has a significant effect on slop but not on the intercept of the fitted growth model. Five fit indices Chi-square, GFI, CFI, RMSEA, and AIC are used to select an appropriate model. In sum, the goal of this article is to introduce readers to latent growth modeling and to provide a concrete application of how a statistical analysis can be performed which may serve a helpful guide to researcher and graduate students wishing to use this technique to explore a change in longitudinal data.

Keywords: structural equation modeling, Latent growth modeling, autoregressive, moving average, covariance analysis

Download full paper