Correlation: Parametric and Nonparametric Measures
By Peter Y. Chen, Paula M. Popovich. 2002
546 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/e8ef331330b8b087c5d86c834d66d5123392b182
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Lori B. Shelby
- Tal Yarkoni
- Todd S. Braver
- Dirk Hermans
- Howard Giles
- Matthew Gibson
- N. Henderson
- Hannah Goodwin
In Statistics and Probability
- Lori B. Shelby
- Jerry J. Vaske
- Todd S. Braver
- Tal Yarkoni
- Dirk Hermans
- Howard Giles
- Matthew Gibson
- N. Henderson
Citations in the corpus
- Cognitive Neuroscience Approaches to Individual Differences in Working Memory and Executive Control: Conceptual and Methodological Issues (2010, methodology): “For example, Pearson’s correlation coefficient can be replaced with a number of alternatives such as Spearman’s rho, Kendall’s tau, or bootstrapped correlation coefficients (for reviews, see Chen & Popovich, 2002; Efron & Tibshirani, 1993; Nichols & Holmes, 2002; Siegel & Castellan, 1988).”
- Hypothesis spaces for minimum Bayes risk training in large vocabulary speech recognition (2006, methodology): “…coefficient of R ( θ i ) with E ( θ i ) for each hypothesis space/error metric combination. the Using significance test for the difference [8] it is observed that use of the FER metric yields a significantly greater (at the 95% confidence level) correlation coefficient than the Levenshtein…”
- Innocent intentions: a correlation between forgiveness for accidental harm and neural activity. (2009, unknown): “…between moral judgment of accidental harms and response in the RTPJ and the same correlation in the other ROIs, from the same samples (e.g., to test for the difference between the RTPJ and the DMPFC, we included only those subjects in whom we identified both RTPJ and DMPFC; Chen & Popovich, 2002).”
- Understanding Meta-Analysis: A Review of the Methodological Literature (2008, methodology): “Effect sizes measure the strength of relationship between variables and are typically used as the summary statistic in meta-analyses (e.g., Chen & Popovich, 2002; Cooper & Hedges, 1994; Grissom & Kim, 2005; Hedges & Olkin, 1985; Lipsey & Wilson, 2001; Rosenthal, Rosnow, & Rubin, 2000).”
- Brooding and Reflection as Subtypes of Rumination: Evidence from Confirmatory Factor Analysis in Nonclinical Samples using the Dutch Ruminative Response Scale (2010, methodology): “The t-statistic is computed using the method of Steiger (Cheng and Popovich 2002).”
- Testing Complex Correlational Hypotheses With Structural Equation Models (2006, methodology): “A procedure already exists for testing the equality of multiple independent correlations (Chen & Popovich, 2002, p. 22; Cohen & Cohen, 1983, p. 55; Hays, 1963, p. 532), but there is no documented test for multiple-group hypotheses involving partial or semipartial correlations.”
- Ethnic Identity Gratifications Selection and Avoidance by African Americans: A Group Vitality and Social Identity Gratifications Perspective (2007, methodology): “However, a follow-up t test, testing two dependent correlations (Chen & Popovich, 2002), indicated the correlations were not significantly different (t = .394, p = .69).”
- Gender Differences in Reading Motivation: Does Sex or Gender Identity Provide a Better Account?. (2012, methodology): “An examination of differences in the strength of association between feminine traits and masculine traits with each motivation dimension was carried out (Chen & Popovich, 2002).”