weights for these households as the attrition calculations are now incorrect. Therefore, in order to have accurate base weights for the calculation of round 3, round 2 weights must be re-calculated using the third round re-interview information. Returning members should be marked as present in round 2, and the attrition correction re-calculated based on this information. These re-calculated ‘shadow’ weights from round 2 become the base weights for the round three calculations.

Note that when the round 3 attrition calculations are done, the necessary data from round 2 will be missing for returning household members. These missing values will either have to be recaptured using the data from the 1st and 3rd rounds, or imputed. For example, if an adult household member reports having the same number of years of completed education in rounds 1 and 3, it is probably safe to assume they had the same number of years of education in round 2. If the member lives in a household with a traditional roof in rounds 1 and 3, he can be assumed to also have had a traditional roof in round 2. This is slightly different from the case of years of education because while the years of education could not have increased and then decreased for the missing year, it is possible that the returning member lived in a house with an improved roof while he was absent from the original household. This distinction is unimportant, however, because we are not trying to impute the values for the returning member’s life while they were missing, only what it would have been had they remained in the household. This means that the dataset compiled to construct the shadow round 2 weights has no real analytical meaning beyond these calculations.

# **Other Weights**

For each round of the panel, there are _2n-1_ sets of weights that can be calculated from the data. In round 1, there is only one set of weights - the cross sectional weights for that round. In round 2, there are three sets of weights: one set of cross sectional weights for each round and then the panel weights to be used for combined analysis. By the third round, there are seven sets of weights. The first three are cross-sectional weights, one for each round. There would also be the combined panel weights (to be used when all three rounds of data are analyzed), a first-and-second only set, a second-and-third only set, and a first-and-third only set. In most cases, it is not necessary to calculate all possible combinations of weights, only those which are necessary for the given analysis. Note also that the calculation of different combinations of weights becomes more complicated in later rounds of the panel, as the shadow weights must take into account the proper combination of absent and returning members in order to form an unbiased base for the calculations.

# **Using Panel Weights**

Regardless of which set of weights are appropriate, all analysis using data from a stratified cluster sample should take into account the complex design features of the data to correctly estimate standard errors. The can be done using the _svy_ package in Stata software or the _PROC SURVEY_ commands in SAS. The cluster and stratification identifier variables should be based on the initial selection in round 1, even if households are no longer physically located in the same cluster. For example, all households found in cluster 1 in stratum 1 in round 1 of the survey should use the cluster identifier 1 and stratum identifier 1 in analysis in subsequent rounds, even if they have now physically relocated to cluster 8 in stratum 3. This is also true of split-off households, which should be classified as part of the cluster where the source member was surveyed in round 1, even if they have never lived in that cluster as a complete household.<sup>10</sup>

> 10 In terms of using the later rounds of a panel survey in sample design for other data collection efforts, some caution should be used with the intracluster correlation coefficient (icc).  The icc for later rounds of the panel survey represents the correlation amongst persons or households based on their cluster location at the time the original sample was selected.  As time passes, members may have shifted out of the original cluster location physically but retain the original identifiers for the purpose of correctly estimating standard errors in panel analysis.  There would

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