Ideal for researchers and graduate students in the social sciences who require knowledge of structural equation modeling techniques to answer substantive research questions, Using Mplus for Structural Equation Modeling provides a reader-friendly introduction to the major types of structural equation models implemented in the Mplus framework. This practical book, which updates author E. Kevin Kelloway’s 1998 book Using LISREL for Structural Equation Modeling, retains the successful five-step process employed in the earlier book, with a thorough update for use in the Mplus environment. Kelloway provides an overview of structural equation modeling techniques in Mplus, including the estimation of confirmatory factor analysis and observed variable path analysis. He also covers multilevel modeling for hypothesis testing in real life settings and offers an introduction to the extended capabilities of Mplus, such as exploratory structural equation modeling and estimation and testing of mediated relationships. A sample application with the source code, printout, and results is presented for each type of analysis.
Researchers are constantly exhorted to move beyond cross-sectional analysis to the use of longitudinal designs. It has long been recognized that cross-sectional data may result in biased parameter estimates (Maxwell & Cole, 2007) and does not allow causal inference (Taris, 2003). Moreover, our conceptualization of what constitutes longitudinal data has changed (Kelloway & Francis, 2012); increasingly, researchers recognize the need for at least three waves of data. Although two-wave studies are still common, we now recognize that such designs are inadequate for describing the process of change (Singer & Willett, 2003) and may confound measurement change with substantive change (Ployhart & Vandenberg, 2010; Singer & Willett, 2003).
In this chapter, we consider three common models for longitudinal analysis, recognizing that there are many ...