Merge stata pdf#

Perhaps you remember that 16,775,215 is the largest integer that can be stored precisely as a float and 9,007,199,254,740,991 is the largest that can be stored precisely as a double. When I obtain data from other sources that contain numeric ID variables, I assume that the other source improperly stored the values until proven otherwise. When I read raw data and store the ID variables as numeric, I worry whether I have specified a storage type sufficient to avoid rounding. If Bob has Social Security number 888888869 and Mary has 888888921, and Bob appears in dataset one and Mary in dataset two, then Bob and Mary will be combined because they share the same rounded Social Security number.Īlways be suspicious of numeric ID variables stored numerically, not just those stored as floats. Then 888888888 becomes 888888896, and so does every Social Security number between 888888888927, some 63 in total. Say you have a raw data file containing perfectly valid Social Security numbers recorded in just this manner. Sometimes they are even stored numerically. Social Security numbers are invariably stored in computers as 888888888, which is to say that they are run together and look a lot like the number 888,888,888. Say the identification variable is Social Security number, an example of which is 888-88-8888. This can happen to both string and numeric variables, but right now, we are going to emphasize the numeric case. One way the id variable can become corrupted is if it is not stored properly or if it is read improperly. 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.ġ. 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 typingĬould 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.Īs I said, the topic for today is merges gone bad, by which I mean producing a merged result with the wrong records combined.

Merge 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 Stata’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 “key” 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. I’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.Īnd 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=”Bob” will not match id=”Bob ” (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=”Mary” is matched with the observation with id=”Mary”, id=”Bob” with id=”Bob”, id=”United States” with id=”United States”, 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.