Adjusting survey data that is taken at diffrent timepoints


New Member
Hi all,

I am trying to analyze the differences between two groups in the result of a psychometric survey after they have been affected by a traumatic event. The survey they have filled in consists of multiple standard psychometric scales designed to measure stress symptoms. Group A has taken the survey at 14 months after the event and then retaken the same survey after 36 months. The mean scores in the later survey is lower but that is expected since the effect of the event is expected to decrease with time. I think it is a reasonable assumption that the scores decrease in a linear way.

The trouble is that group B has taken the survey at 21 months from the event wich make the two groups not directly comparable. My goal is therefore to adjust the scores for group A so I can make a regression model with demographic and other factors as covariates and the scores of both groups at 21 months as the dependent variable.

My first idea was to simply take the proportional change in scores for each month between the 14 months and 36 months study and then adjust the scores observed at 14 month down to what they could be expected to in 21 months. I think this is fine if the scores are truly continious and a linear slope is assumed. However I think there are some problems with the first assumption.

First, the scores are not truly continious since they are the sums of yes/no questions for diffrent symptoms where "yes" is scored 1. Since the total score for an individual is supposed to measure stress and the questions are not weighted in any way the total sums can't be treated as really continious.

Furthermore the researcher I work with wants to dichotomize the total scores before analysis according to cutoffs that are standard in her field. (The whole questionaire and scoring system is a standard psychometric scale and is supposed to be treated like this). She then want to use logit analysis to estimate odds ratios. The trouble with this is that many of the individuals have exactly the score above the cutoff. If I then use the adjustment i wrote about above (meaning an adjustment of about 0.8 times the 14 months scores) theese individuals would be put below the cutoff and the incidence of stress symptoms would go from something like 40% to about 25%. Just by intuition I think this would be wrong since this drop seems way to large when the underlying scores only where supposed to drop 20 %.

I understand that there is impossible to give a clear answer for the but I would be happy to hear your experience if you encountered similar problems.

Anothed thing I was thinking about was if I could use a mixed model and set the estimates to show at 21 months. Then I dont have to adjust the scores at all but maybe this would be wrong since there is only one measurement. for the B group and no data for 21 months for the A group.

All comments are greatly appreciated.
I wouldn't do what you are suggesting since the adjustment you suggest assumes that the adjustment factor is representative of the population, which, since it is a sample and not the population, cannot be the case and there is no way, that I know of, to incorporate standard error information in the adjustment.

In a sense, the accuracy of your adjustment would be sensitive to the degree to which "time" predicts your DV. That is, does time explain 100% of the reason (eg. the variance) why there is a linear decrease in stress after a traumatic event? My guess is no and you wouldn't be taking into account the other factors with your adjustment.

In other words, the adjustment that you propose making would invariably be wrong, or at least suffer from inaccuracies.

The other option is to go ahead and make the adjustment but note in your paper, thesis, manuscript, etc. that the adjustment technique you use is imprecise. If I were reviewing the paper I would have serious reservations about your "adjustment" however.

What is the purpose of doing the adjustment, increasing sample size?
Hi thanks for your answer.

The reason for doing an adjustment was to make an unbiased comparison between group A and B. The event (a disaster) happened at the same point in time for both groups but the groups were surveyed at different timepoints, group A at 14 and 36 months and group B at 21 months after the event only.

I think you are right however and now belive the best approach is to compare the group B first with the 14 months group A data and then with the 36 months group A data and present both results (if they differ) in the article.

Anyone have opinions about this?