You just joined the quantitative research team as an independent consultant and wasn’t involved in data collection. But when you began data analysis, you realized that there are a number of wrong or missing information in the data. How would you deal with this issue
You just joined the quantitative research team as an independent consultant and wasn’t involved in data collection. But when you began data analysis, you realized that there are a number of wrong or missing information in the data. How would you deal with this issue
Would leave the data or do data imputation to replace the them. Suppose the number of cases of missing values is extremely small; then, an expert researcher may drop or omit those values from the analysis. In statistical language, if the number of the cases is less than 5% of the sample, then the researcher can drop them.
In the case of multivariate analysis, if there is a larger number of missing values, then it can be better to drop those cases (rather than do imputation) and replace them. On the other hand, in univariate analysis, imputation can decrease the amount of bias in the data, if the values are missing at random.
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