So, one can have
1. Cross-sectional Data and Cross-sectional Models
2. Longitudinal Data and Longitudinal Models
3. Cross-sectional Data and Longitudinal Models[1: p. 82 on]
In other words, time can be built in models with cross-sectional data, see my last figure in http://evaluatehelp.blogspot.com/2013/08/placeholders.html
What I want to show here however is how time can be extracted from repeated cross-sectional data, particularly when one is interested in development and changes in the context of natural/historical development. One of the original visual introductions to this approach is in Muthen (2000) (there are certainly earlier ones...):
In the context of say students (grades 6th to 12th) surveyed every 2 years or so, one can build a dataset structured as 'repeated", i.e. with 'year' and 'cohort' fields into it, to compare changes over time as done here [4]: http://www.getcited.org/pub/103503915
1. Gollob, H. F., & Reichardt, C. S. (1987). Taking account of time lags in causal models. Child Development, 58(1), 80-92.
2. Gollob, H. F., & Reichardt, C. S. (1991). Interpreting and estimating indirect effects assuming time lags really matter. In L. M. Collins & J. L. Horn (Eds.), Best methods for the analysis of change: Recent advances, unanswered questions, future directions (pp. 243-259). Washington, DC, US: American Psychological Association.
3. Muthén, B. O. (2000). Methodological issues in random coefficient growth modeling using a latent variable framework: Applications to the development of heavy drinking. In J. Rose, L. Chassin, C. Presson & J. Sherman (Eds.), Multivariate applications in substance use research (pp. 113–140). Hillsdale: Erlbaum.
4. Combining missing-by-design and mixture modeling to assess impact of a community-wide interventions. A social marketing and text messaging campaign to reduce alcohol use among high school students. E Coman, G Rots, S Suggs, S Fuxman - 2012 Modern Modeling Methods, 2012
