I have 299 surveys collected from 299 individuals working at 26 different locations. I want to understand how the location specific features relate to the individual survey scores. The only inference I have as to location features is gathered from the individual survey scores. Is it a valid strategy to calculate means for each location based on the individual scores, and include this as a level 2 variable? Further, does it also make sense to include the same variable but as the level 1 variable, with slope varying freely between locations, if I want to compare the relative usefulness of the mean (best estimate of 'reality') to a persons individual score? (their perception of reality and response biases).
I feel like I may have some circularity in the logic. My implementation in R for one of the variables of interest follows, any feedback is welcome!
lmer(X21~X25+meanX25+(X25|X1),data=datai) Linear mixed model fit by REML Formula: X21 ~ X25 + meanX25 + (X25 | X1) Data: datai AIC BIC logLik deviance REMLdev 1079 1105 -532.7 1056 1065 Random effects: Groups Name Variance Std.Dev. Corr X1 (Intercept) 0.384983 0.62047 X25 0.012382 0.11127 -1.000 Residual 1.936068 1.39143 Number of obs: 299, groups: X1, 26 Fixed effects: Estimate Std. Error t value (Intercept) 1.13616 0.38013 2.989 X25 0.56683 0.05265 10.766 meanX25 0.33897 0.12213 2.775 Correlation of Fixed Effects: (Intr) X25 X25 -0.119 meanX25 -0.838 -0.389