Of course, there is software for the best, and most complicated way, involving use of an EM algorithm to do full-information imputation. But if you only want simple --

The simplest way, for a continuous variable, is to substitute the mean for missing values. Or, if the variable is categorical, and the modal category includes a great majority of the cases, you could  assign missing cases to the mode. This will reduce the variance of a continuous variable.  It is further problematic for a t-test if there is substantial bias in item non-response (which, in general, you can't easily detect).  But with a slight increase in complication, you can address such problems by doing your significance tests in a regression framework instead of simple t-tests.

For any of the independent variables (IVs) in a regression-type model, you could  include in the regression, for each IV, a dummy variable scored 1 if it is a case for which you have substituted the mean (or mode), and scored 0 if it is a case that did not have missing data on that variable. The dummy variables will help control for bias that might be introduced by the mean/mode substitution.  Your sample size will need to be fairly large, since the dummy variables could double the number of IVs in your regression model. Grilich's Law (n >=  5* # IVs) is always a wise guideline to follow.

Another handy trick, if you are adding several items together to form a scalee, is to use the mean of the items instead of the sum.  Recode the items with missing values to zero, and when you calculate the mean of the items, divide by the number of non-missing items instead of the total number of items.  Then you get a non-missing value for the scale if at least one of the items is non-missing.  Or, to be safe, you might decide to require that at least a majority of the items be non-missing.  If you are using SPSS for the data analysis, the Compute command, with subcommand Mean, lets you specify how many of the items must be non-missing to generate a non-missing mean for the scale. But pretty much any software can do the same thing, if you program it appropriately.

I hope this helps.

@Burke D. Grandjean

I support your answer to deal with missing data. Its really helpful.

Most of the time, you resolve missing data problems in applied statistics research by the method of missing-values imputation.

However, one does not ALWAYS deal with missing data dilemma by the method of missing-values imputation.

@Burke has provided a detailed response. 

Further to that I would like to add that if your dataset is pretty large then you can ignore NA.  but never add zero for NA. 

Good luck 

Bahadur,

With due respect to Burke's other suggestions, I argue against mean substitution for continuous data. It does terrible things to normality of residuals.

Pat

Patrick is right, of course. That's one reason EM imputation is preferred. On the other hand, mean substitution is quick and easy.  In a regression framework, inclusion of the dummy terms for missing data will help some with the residuals, and OLS regression is pretty robust to the normality assumption.

True, though mean substitutionn also reduces correlations between variables.

Hi guys... Good afternoon to one and all.

Let me repeat for those who are not quite paying close enough attention.

Almost all of the time, one does resolve missing data problems in applied statistics research by the method of missing-values imputation.

However, one does not ALWAYS IN EVERY INSTANCE, deal with missing data dilemma by the method of missing-values imputation.

How to deal with missing data?. Available from: https://www.researchgate.net/post/How_to_deal_with_missing_data#view=592fa36893553b256c546679 [accessed Jun 6, 2017].

You can create a new category for the missing values, if it's not a continuous variable. 

Mean, median can be used to replace missing values or try using regression imputation.

Here's another thought:  (1) Often, the cases that are missing on one of yours variables are pretty much the same cases as the missing data on the others. In that situation, listwise deletion of missing will not eliminate many cases, so you might decide not to worry about it.   (2) On the other hand, sometimes one variable has quite a bit of missing data, but other variables have much less.  In survey data, income might have 10% missing or more, while none of the other survey questions have more than 2% missing, and the missing cases on any one of those variables overlap a lot with the missing on the others   In those circumstances, listwise deletion will cost you a lot of cases, but there's a (simple and partial) fix available . You can do imputation (or mean substitution plus a dummy variable) only on the variable that's the biggest problem, That will reduce the problem to the equivalent of situation (1), so you might decide that the remaining missing data is not worth fixing.

If I say this a third time I might start sounding like a stuck cassette or a broken record.

At the risk of being censured or some other punishment I will say this once again....

In a project such as this, dimensions that cannot be measured in certain circumstances DO NOT have to be estimated or approximated by the technique of missing-values imputation.

Now that I've got that off my chest, I'm off to have my breakfast.

#Dalton, what is the way forward

Basically all the short answers are already here, but if you want some more expanded review, I would recommend the one that I usually give to our students

http://imaging.mrc-cbu.cam.ac.uk/statswiki/FAQ/emalgm?action=AttachFile&do=get&target=graham.pdf

Good morning Professor Mukangai.

For the answer to your question, please see the post by Oleksii Pasichnyi. If you tthen have further questions thereupon, you may direct them to me but my suggestion would be to ask Oleksii.

The reason I got slightly sarcastic and insistent is that everybody was blithely and totally ignoring what I was saying.

Have a good day, Professor Makangai.( Asante kwa swali lako. Uwe na siku njema.. Usiku mwema.... Na kuona hivi karibuni  )

"Plus ca change, plus c'est la meme chose."