Here's how you can handle missing data in regression analysis.

2 Pairwise Deletion

Pairwise deletion is an alternative to listwise deletion that uses all available data by computing correlations between pairs of variables that have no missing values. This method can retain more data compared to listwise deletion, potentially leading to more accurate results. However, it comes with its own set of complications, such as the possibility of obtaining a non-positive definite correlation matrix, which can be problematic for some types of regression analysis. You should weigh the benefits of retaining more data against the potential for introducing inconsistencies when opting for pairwise deletion.

3 Mean Imputation

Mean imputation involves replacing missing values with the mean of the available cases for that variable. This method is simple and retains the sample size, but it can underestimate the variability and standard errors, leading to biased estimates. Mean imputation assumes that the missing values are similar to the observed average, which might not be the case. While it's a quick fix, you should be cautious as it can distort the true relationships between variables in your regression model.

4 Regression Imputation

Regression imputation uses the relationships between variables to estimate missing values. By creating a regression model with the incomplete variable as the dependent variable and other variables as predictors, you can predict the missing values. This method can provide more accurate imputations than mean imputation if the relationships are well-modeled. However, like mean imputation, regression imputation can underestimate variability and should be used with an understanding of its limitations.

5 Multiple Imputation

Multiple imputation is a more sophisticated approach that involves creating several imputed datasets and then combining the results. Each missing value is replaced with a set of plausible values that represent the uncertainty around the right value to impute. This method accounts for the variability between the imputed datasets in the final analysis, providing more reliable standard errors and confidence intervals. Multiple imputation requires more complex statistical software but is considered one of the best practices for handling missing data in regression analysis.

6 Maximum Likelihood

Maximum likelihood estimation (MLE) is a statistical method that estimates the parameters of a model in a way that maximizes the likelihood of observing the given sample data. When dealing with missing data, MLE can provide unbiased parameter estimates under the assumption that the data are missing at random (MAR). This approach uses all available information and can be more efficient than other methods like listwise or pairwise deletion. However, MLE requires a proper understanding of the underlying assumptions and can be computationally intensive.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?