0
$\begingroup$

My study is designed in such a way that I have individuals with varying numbers of observations of the predictors, but only one observation of the outcome. Like a multilevel model, but with the outcome at the 'group' level. Following the conclusions of Foster-Johnson and Kromrey (2018), I plan to conduct an OLS regression analysis of group means, with White's heteroscedasticity adjustment.

My concern lies in the fact that the number of observations of the predictors differs massively between individuals... the range is between 2 and about 9000. I have read that the OLS estimators would be unbiased, and the correction produces robust standard errors. But I can't shake the feeling that perhaps there are additional ways to address this huge difference in group size. Calculating a weighted mean for the OLS maybe? Or is the correction really enough? Would love to hear thoughts or recommendations on how to approach this. Thanks!

Foster-Johnson, L., & Kromrey, J. D. (2018). Predicting group-level outcome variables: An empirical comparison of analysis strategies. Behavior research methods, 50(6), 2461-2479.

$\endgroup$
4
  • $\begingroup$ Check out this thread and let us know if you have further questions: stats.stackexchange.com/questions/169512/… $\endgroup$
    – Erik Ruzek
    Commented Jun 19, 2020 at 20:22
  • $\begingroup$ Thanks for your comment. Actually, I had already come across that thread. I decided against that approach because the paper that I referenced concludes that little is to be gained from it compared to the one I have outlined (OLS + Whites correction). I'm just a little wary because my group sizes are so different, how much can I really trust the estimates... $\endgroup$
    – Hannah
    Commented Jun 19, 2020 at 20:58
  • $\begingroup$ That is exactly the reason you should be using the approached outlined in that thread. $\endgroup$
    – Erik Ruzek
    Commented Jun 20, 2020 at 0:05
  • $\begingroup$ Ha, OK, thanks for the advice! I'll take a closer look at the CV approach. $\endgroup$
    – Hannah
    Commented Jun 20, 2020 at 8:57

0

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.