Hi, Currently this selection is done manually so I have no code to share. I believe I can automate the process in SAS and I thought it would be good to ask here in case anyone can give me some pointers. I have looked for like questions but didn't find anything. The end goal is to identify a sample that represents group characteristics with as few of the observations as possible. Based on group totals formulas and rounding are used to generate a list of “need” characteristic totals. After an observation is selected to be a part of the sample they need to be deducted from the needed count (Need_initial), until a sample is selected that represents the total desired count for each characteristic. I have added a pretend dataset that represents a group. In the table below I put the initial total counts (Need initial), an example of an observation selected for the sample, and then how that first selection impacted the needed totals (Need_2). Sometimes subcategory counts may not be in line with the category total. For example, in this case the sample only needs 1 observation with patterns but the sample should include 1 pattern_dots and 1 pattern_plaid regardless of whether that combination can be identified within one observation. Need_Initial Selection_1 Need_2 Dinner 14 1 13 Likes_talking 8 1 7 Visiting 8 1 7 Hates_talking 7 0 7 Green 3 0 3 Dark_Yellow 3 1 2 Dark_Green 2 1 1 Dark_White 2 0 2 Blue 1 0 1 Yellow 1 1 0 White 1 0 1 Red 1 0 1 Black 1 1 0 Grey 1 0 1 Light_Blue 1 0 1 Light_orange 1 0 1 Light_red 1 0 1 Dark_Black 1 1 0 Patterns 1 1 0 Pattern_Dots 1 0 1 Pattern_Plaid 1 1 0
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