Which of the following influences the results of clustering in hierarchical analysis?

In hierarchical clustering, standardization of variables plays a crucial role in influencing the results. When the data features have different scales or units, it can lead to biased clustering results. For instance, if one variable ranges from 1 to 10 and another from 1 to 10,000, the larger range will dominate the distance calculations used in clustering algorithms. This can distort the representation of the dataset and result in misidentified clusters.

Standardization, or normalization, involves adjusting the data so that each feature contributes equally to the analysis, typically by transforming the variables to have a mean of zero and a standard deviation of one. This ensures that no single variable disproportionately influences the clustering outcome, leading to more meaningful and interpretable clusters.

While threshold settings, data splitting, and cluster size determination are factors that can influence clustering outcomes in general, they do not have the same foundational impact as the standardization of variables, as standardization directly affects how distance measures are computed, which is fundamental to the clustering process itself.