Pearson r

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

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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)

A "real life example" of using correlations to gauge winter weather

Every teacher in New England has a vested interest in understanding how winter weather may impact the school calendar. I recently heard an interview with Judah Cohen on NPR, and sourced an older article from the Washington Post which includes a graph, which illuminates how this meteorologist who works for the firm, Atmospheric and Environmental Research, uses correlations to forecast East Coast weather. Specifically, Cohen evaluates the Siberian snow cover in October to predict winter weather in New England (Samenow, 2013).

Because we’ve learned about correlational statistics, specifically what’s implied by the correlation coefficient or r-value, we can look beyond the narrative offered in the Washington Post article, which describes the statistical correlation as “striking.” In fact, we can look at the r =.810 in the graph below, and determine that because this number is close to 1, the Snow Advance Index (which relates to the Siberian snow cover) and the Arctic Oscillation (which produces the winter weather patterns in the Northeast) are strongly positively correlated (Hinkle, Wiersma, & Jurs, 2003, pp.98-99).

Given the strong positive correlation, teachers in New England might pay a little more attention to what’s happening in Siberia in October to determine how much hot chocolate to buy in advance of snow days and how far those snow days will cause us to overshoot our districts’ June calendars.

References: Hinkle, D.E., Wiersma, W., & Jurs, S.G. (2003). Applied statistics for the behavioral sciences (5th edition). Boston, M.A.: Houghton Mifflin Company.

Samenow, J. (2013). Judah Cohen’s winter outlook: A downer for East Coast winter weather lovers. The Washington Post.

contributed by Emily Kilbourn

It is important to keep in mind that Pearson r is not reporting a cause and effect relationship, since consideration for classification of independent and dependent variables is not taken into account. However, it is a good measure for seeing the strength of relationship between two variables.

contributed by Lauren Moyer