Spearman Rank Correlation

By J. H. Zar. 2005

643 citations recorded by the source corpus.

Subfields: Food Science, Statistics and Probability, Water Science and Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Food Science

1. Phillip E. Pope
2. Zhixu Li
3. Siyuan Ma
4. Pengfei Liu
5. J. Gabriel
6. Yan Song
7. Samyadeep Basu
8. See-Kiong Ng

In Statistics and Probability

1. Phillip E. Pope
2. Zhixu Li
3. Siyuan Ma
4. Pengfei Liu
5. Ehud Reiter
6. J. Gabriel
7. Yan Song
8. Julius Adebayo

In Water Science and Technology

1. Phillip E. Pope
2. Zhixu Li
3. Siyuan Ma
4. Pengfei Liu
5. J. Gabriel
6. Yan Song
7. Samyadeep Basu
8. See-Kiong Ng

Citations in the corpus

• GPTScore: Evaluate as You Desire (2023, methodology): “In this work, we adopt two widely-used correlation measures: (1) Spearman correlation ( ρ ) (Zar, 2005) and (2) Pearson correlation ( r ) (Mukaka, 2012).”
• BARTScore: Evaluating Generated Text as Text Generation (2021, background): “Spearman Correlation [73] assesses the monotonic relationships between two variables.”
• Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings (2018, methodology): “The Spearman’s rank correlation (ρ) (Zar, 1998) is adopted to measure how close the similarity scores to human judgments on the three datasets.”
• MobileOne: An Improved One millisecond Mobile Backbone (2022, methodology): “We also estimate the Spearman rank correlation [63] in Table 1a.”
• Exploring the Use of Large Language Models for Reference-Free Text Quality Evaluation: A Preliminary Empirical Study (2023, methodology): “Individual Score In order to assess the reliability of Individual Scores, we utilize the Spearman (Zar, 2005) and Pearson (Mukaka, 2012) correlation coefficients.”
• Is ChatGPT a Good NLG Evaluator? A Preliminary Study (2023, methodology): “Two widely-used correlation measures are adopted: (1) Spearman correlation (Zar, 2005) assesses the monotonic relationships between two variables; (2) Pearman correlation (Mukaka, 2012) measures the linear relationships between two sets of data; (3) Kendall’s Tau (Kendall, 1938) evaluates the ordina”
• Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation (2022, background): “, 2007) and Spearman’s rank correlation (Zar, 2005) coefficients between each pair.”
• Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models (2023, methodology): “Then,they compute Spearman coefficients [74],Pearson correlation coefficients [75].”
• EvalCrafter: Benchmarking and Evaluating Large Video Generation Models (2023, methodology): “To demonstrate the effectiveness of our model in aligning with human scores, we calculate Spearman’s rank correlation coefficient [64] and Kendall’s rank correlation coefficient [27], both of which are non-parametric measures of rank correlation.”
• Influence Functions in Deep Learning Are Fragile (2020, methodology): “Evaluation Metrics : We evaluate the accuracy of influence estimates at a given test point z t using both Pearson [24] and Spearman rank-order correlation [25] with the ground-truth (obtained by re-training the model) across a set of training points.”
• Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models (2023, methodology): “Then,they compute Spearman coefficients [105],Pearson correlation coefficients [66].”
• Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging (2020, methodology): “We use the Structural SIMilarity (SSIM) index to assess the change of the original saliency map with the saliency maps generated from the model after each randomization step[21].”