In my study, I have 5 independent variables which contains 21 observation each. However one of my independent variables have 3 gaps. What should I do?


I would need to know the effect of education to inequality and I would like to conduct standard OLS method. However, I have one unbalanced data which have gaps unlike the rest of the data... what should I do? per say in 1997 (no data) 1998 (0.2345) 1999 (no data) 2000 (0.3453) and then from 2001 - 2017 it has continuous data....should I just drop it and start from 2001?

  • $\begingroup$ It is impossible to answer this question without knowing your exact goal and the nature of the data. $\endgroup$ – Giskard Apr 2 '19 at 19:20
  • $\begingroup$ Information about your ethical flexibility may also be of interest. $\endgroup$ – Giskard Apr 2 '19 at 19:20
  • $\begingroup$ I edited my question. can you take a look in it. Thank you. $\endgroup$ – valve01 Apr 2 '19 at 19:43


This situation that requires an imputation method that you must pick from. Dealing with missing data is a sensitive matter. Depending on which way you choice to handle missing observation will impact your project and in this case (with observations less than 100) could make your analysis poor/basis.

Here are some popular options:

  1. Impute the value of the missing data
  2. Remove a variable which has a lot of missing data and use other variables which measure similar aspects of the characteristics being studied.
  3. You could create a regression model to impute missing values.
  4. You can use the average (caution when using this method as there may be other variables in your data set that may be dependent).
  5. Duplicate the previous or nearest value.

Here is a website on some tips: http://www.real-statistics.com/descriptive-statistics/missing-data/

Here is a working paper on the subject: liberalarts.utexas.edu/prc/_files/cs/Missing-Data.pdf

Missing values is delicate subject. Use caution and always, always, always DOCUMENT WHAT YOU ARE DOING, because others want to know and it makes your work so much better.


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