Which method involves replacing missing data with an estimate such as the mean?

The method that involves replacing missing data with an estimate such as the mean is known as Predictive Replacement. This technique, often referred to as mean imputation, substitutes missing values based on the average of the available data, allowing for more complete datasets without losing cases entirely. This method is commonly used when the amount of missing data is small, and adding estimated values helps maintain the integrity of the dataset for analysis.

Listwise deletion and pairwise deletion refer to different approaches regarding handling missing data. Listwise deletion removes any case (participant or observation) with missing data, effectively reducing the sample size. Pairwise deletion uses all available data for each analysis by selecting cases that have data for the specific variables being analyzed, which might lead to inconsistencies across different analyses.

Exploratory data analysis, on the other hand, is a broader approach used to summarize the main characteristics of data, often with visual methods, rather than focusing on imputing missing values. Thus, the correct choice aligns with the method specifically designed for estimating missing data using existing values within the dataset.