Drop observations in stata pdf#

Use theory to check IDs if they are numeric But what if id=4713 means Bob in one dataset and Mary in the other? That can happen if the id variable is simply wrong from the outset or if the id variable became corrupted in prior processing.ДЎ. There is no problem assuming the values in the id variable are correct and consistent between datasets. Bob matched with Bob, Mary matched with Mary, and so on. It is difficult to imagine that typingД¬ould produce such a result because, to be matched, the observations had to have equal values of the ID. We are going to pick up where the discussion in merge leaves off.ДЄs I said, the topic for today is merges gone bad, by which I mean producing a merged result with the wrong records combined.

Drop observations in stata pdf#

Type help merge in Stata and click on merge at the top to take you to the full PDF manuals. I am going to assume you are familiar with StataвЂ™s merge command. I am going to assume that you are familiar with merging datasets both conceptually and practically that you already know what 1:1, m:1, 1:m, and m:n mean and that you know the role played by вЂњkeyвЂќ variables such as ID. Only three things are certain in this world: death, taxes, and merges gone bad. You seriously need to consider the possibility that this could happen to you. IвЂ™ve seen it happen, be discovered later, and necessitate withdrawn results. Observations are mistakenly combined more often than many researchers realize. It is the mistakenly combined observations that can go unnoticed.ДЄnd that is the topic for today, mistakenly matched observations, or merges gone bad. Mistakenly unmatched observations tend to get noticed because of all the missing values they cause in subsequent calculations. For instance, id=вЂќBobвЂќ will not match id=вЂќBob вЂќ (with the trailing blank), but if you expected all the observations to match, you will ultimately notice the mistake. This way, the observation with id=вЂќMaryвЂќ is matched with the observation with id=вЂќMaryвЂќ, id=вЂќBobвЂќ with id=вЂќBobвЂќ, id=вЂќUnited StatesвЂќ with id=вЂќUnited StatesвЂќ, and id=4934934193 with id=4934934193. The better solution is to match the observations on equal values of an identification variable.

What if you are wrong? If observation 2 in one.dta is Bob and observation 2 in two.dta is Mary, you will mistakenly combine the observations for Bob and Mary and, perhaps, never notice the mistake. You are merely assuming that observation 1 matches with observation 1, observation 2 matches with observation 2, and so on. Never do this because it is too dangerous. In Stata, you could obtain that result by typing The matching of the two datasets - deciding which observations in one.dta are combined with which observations in two.dta - could be done simply on the observation numbers: Match one.dta observation 1 with two.dta observation 1, match one.dta observation 2 with two.dta observation 2, and so on. But just to fix ideas, appending looks like this: Appending datasets is not the subject for today. When it comes to combining datasets, the alternative to merging is appending, which is combining datasets on the same variables to produce a result with more observations. We will call the datasets one.dta and two.dta. Merging concerns combining datasets on the same observations to produce a result with more variables.