I have panel data where different students are tested for overlapping 2-year periods.
- Subject A is observed for years 1 & 2. - Subject B is observed for years 2 & 3. - Subject C is observed for years 3 & 4. - etc up to year 12 (of school) For each observed year there are three separate test occasions (fall, winter, spring) and two subjects (reading, math). It seems to me I can impute the missing test scores provided I am willing to assume something about lags that are 2 years are longer. For example, I could assume that the partial correlation at lags of 2 years or longer is zero. This is not an unreasonable assumption since the correlations at shorter lags are very strong (.8-.9). Is there software that will allow me to do this conveniently? My usual strategy is to reshape the data from long to wide and then impute using a multivariate normal model. There are several packages that will permit this; however, I am not aware of software that will let me constrain the covariance matrix in the way I have described. I have not used imputation software that are tailored for panel data -- such as Schafer et al's PAN package, recently ported from S-Plus to R. I could try that, provided there is a convenient way to restrict the long lags. Thanks! -- Best wishes, Paul von Hippel Assistant Professor LBJ School of Public Affairs Sid Richardson Hall 3.251 University of Texas, Austin 2315 Red River, Box Y Austin, TX 78712 mobile, preferred (614) 282-8963 office (512) 232-3650
