Contents
How do you treat missing values in survey data?
Techniques for Handling the Missing Data
- Listwise or case deletion.
- Pairwise deletion.
- Mean substitution.
- Regression imputation.
- Last observation carried forward.
- Maximum likelihood.
- Expectation-Maximization.
- Multiple imputation.
How do you determine if data is missing completely at random?
The only true way to distinguish between MNAR and Missing at Random is to measure the missing data. In other words, you need to know the values of the missing data to determine if it is MNAR. It is common practice for a surveyor to follow up with phone calls to the non-respondents and get the key information.
How should I deal with missing data from my online survey?
If it makes sense, impute the missing values. This should only be done for continuous or interval data (like age or Likert-scale responses), not for categorical data (like gender). If your dataset is large enough, just don’t use the responses that had missing values for that variable.
How much missing data is in my sample?
I have approximately 20% data missing in my sample (n=3215). I aim to assess the pre-post differences on a psychometric scale. Especially post measures are missing because of follow-up issues. What to do?
When do you know you are missing data?
The threshold for missing data is flexible, but generally, if you are missing more than 10% of the responses on a particular variable, or from a particular respondent, that variable or respondent may be problematic. There are several ways to deal with problematic variables. Just don’t use that variable.
What happens if you miss a lot of data?
If you are missing much of your data, this can cause several problems. The most apparent problem is that there simply won’t be enough data points to run your analyses. The EFA, CFA, and path models require a certain number of data points in order to compute estimates.