I currently have data from a longitudinal study that I am analyzing. I current have two times points, but participants will continue to be followed every three years, so there will be more time points to look at in the future.
My dependent variable is measured at two time points, and is continuous.
My independent variables and covariates include continuous, ordinal, and nominal variables (all also measured at both time points).
For data at just one time point, I ran three multiple regression models: one with my independent variables, one with independent variables and demographic covariates, and one with independent variables and demographic and health-related covariates.
Since I have two time points, it seems that a mixed effects model would be the best approach? Are there any resources (either for R or SPSS) that people could recommend? Thanks.
Mixed effects models work better with multilevel or nested data. Is your data mutilevel?
I'd say it depends on what you are trying to find out with your longitudinal analysis. A mixed effects model is one option. With more than two time points, a mixed effects model would allow you to analyze growth curves in your dependent variable and relate the curves to the predictors and covariates. If you search for "fitting multilevel models for longitudinal data in SPSS" or similar terms within Google Scholar, you should find some tutorial papers and other resources.
Another option may be an autoregressive/crosslagged panel model that can be fit within the framework of path analysis and structural equation modeling (e.g. in lavaan within R). The autoregressive/crosslagged model allows you to examine relationships over time which may be more in line with what you're trying to do (?)
Yes, you could (not should). However, you might make use of the nine take-away points dissected by Bliese et al. (2018), fully cited below.
Bliese, P. D., Maltarich, M. A., & Hendricks, J. L. (2018). Back to basics with mixed-effects models: Nine take-away points. Journal of Business and Psychology, 33(1), 1–23. https://doi.org/10.1007/s10869-017-9491-z
Good luck,
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