In research, which method is mainly used to estimate missing relationships?

The method primarily used to estimate missing relationships in research is predictive replacement. This technique involves using available data to make educated estimates or predictions about the missing values based on the relationships found within the data set. Essentially, it employs statistical models to infer what the missing values might be, allowing researchers to maintain the integrity of the dataset and facilitate more comprehensive analysis.

Predictive replacement is particularly beneficial because it helps to minimize the biases that could arise from simply removing data points with missing values. By leveraging existing patterns and relationships among the data, researchers can make more informed decisions and draw more accurate conclusions.

On the other hand, listwise deletion removes entire cases from the analysis if any single value is missing, which can lead to a loss of valuable information. Pairwise deletion, similar to listwise deletion, analyzes only the subset of data that has complete values for the specific variables being examined but does not attempt to estimate missing values. Data validation refers to the process of ensuring data accuracy and integrity rather than addressing missing values directly.