Difference between revisions of "Pearson r"
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Also referred to as the Pearson Correlation Coefficient Squared, it is the proportion of variance in the criterion variable that can be accounted for by the predictor variable. (from Dr. Nancy Heilbronner) | Also referred to as the Pearson Correlation Coefficient Squared, it is the proportion of variance in the criterion variable that can be accounted for by the predictor variable. (from Dr. Nancy Heilbronner) | ||
| − | + | ''contributed by Mary Fernand'' | |
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The Pearson r score (say for example .80) is the number where the distribution will peak, and the remaining distribution will spread out around the number. | The Pearson r score (say for example .80) is the number where the distribution will peak, and the remaining distribution will spread out around the number. | ||
| − | + | ''contributed by Mykal Kuslis, WCSU Cohort 8'' | |
Reference: | Reference: | ||
Meyers, S., Gamst, G., & Guarino, A.J. (2017). Applied multivariate research: Design and interpretation. Thousand Oaks, CA: Sage Publications. (P. 21) | Meyers, S., Gamst, G., & Guarino, A.J. (2017). Applied multivariate research: Design and interpretation. Thousand Oaks, CA: Sage Publications. (P. 21) | ||
Revision as of 16:30, 19 November 2019
Also known as Pearson's product-moment correlation. This technique is used to correlate the raw scores of two variables.
Also visit http://psych.csufresno.edu/psy144/Content/Statistics/relationship_strength.html for more information on Pearson r.
contributed by Kara Kunst
Also referred to as the Pearson Correlation Coefficient Squared, it is the proportion of variance in the criterion variable that can be accounted for by the predictor variable. (from Dr. Nancy Heilbronner)
contributed by Mary Fernand
Note: Pearson r scores cannot exceed 1.00 or -1.00 (range is between -1.00 and 1.00).
The Pearson r score (say for example .80) is the number where the distribution will peak, and the remaining distribution will spread out around the number.
contributed by Mykal Kuslis, WCSU Cohort 8
Reference:
Meyers, S., Gamst, G., & Guarino, A.J. (2017). Applied multivariate research: Design and interpretation. Thousand Oaks, CA: Sage Publications. (P. 21)
