I’ll add to Gene’s comment by saying that if you use propensity score methods, you can retain the entire comparison population, and weight the people/units to create a balanced design, allowing you to analyze
the data using methods effected by unbalanced designs. However you must meet and test the assumptions for propensity score matching, if you don’t, or it’s in question, don’t use it, it has been shown to create increased bias in this situation.
However, if this is a design which can make use of MLM/HLM analysis, they will not be biased by the unbalanced design, and I actually have a paper coming out discussing the advantages to power of having a greater
number of control units, without weighted matching. However, as it’s a working paper, I can’t/won’t say much more about it.
If this is a repeated measures design, meaning you have data collected on the same people repeatedly, and can match the data at each time point to an individual such that time points are nested within people,
I would suggest this route. It will give the greatest strength in your analysis. Assuming the repeated measures exist as your outcome, as well as predictors, you can analyze the time variant and time invariant predictors.
If, when it comes right down to this, you have a design in which you want to see if treatment scored better than control over time, and the covariates in the model are simply things like demographics, then they
can be treated as fixed effects at the person level. The outcome/s would simply be the DV’s in the model, and you would use an indicator variable for the treatment/control portion. Covariates then serve to equalize the groups at baseline.
Matthew J Poes
Research Data Specialist
Center for Prevention Research and Development
University of Illinois
510 Devonshire Dr.
Champaign, IL 61820
Phone: 217-265-4576
email: [hidden email]
From: SPSSX(r) Discussion [mailto:[hidden email]]
On Behalf Of Allen Frommelt
Sent: Thursday, May 10, 2012 10:28 AM
To: [hidden email]
Subject: Advice on analyzing data
I am looking for some advice on the best approach for analyzing our data in SPSS. We are currently running version 19. We have a group of 2,405 people who received an intervention and 5,702 people who did not receive the intervention
(the groups were not randomly selected so we know we have bias in that area). For each individual, we have up to 27 months of data from the period prior to the start of the intervention and 12 months of data post-intervention. For most of the outcomes of
interest, the data is highly skewed and not normally distributed.
Thanks,
Allen
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