Pearson r - Revision history

Casimir007 at 06:04, 14 November 2025

2025-11-14T06:04:36Z

Admin at 14:27, 20 April 2022

2022-04-20T14:27:02Z

Ldaigle at 20:26, 19 February 2022

2022-02-19T20:26:31Z

Ldaigle at 20:25, 19 February 2022

2022-02-19T20:25:05Z

Moyer008 at 15:04, 13 December 2019

2019-12-13T15:04:08Z

Admin: /* A "real life example" of using correlations to gauge winter weather */

2019-12-05T21:09:02Z

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

Kilbourn004: /* A "real life example" of using correlations to gauge winter weather */

2019-12-03T20:50:00Z

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

Kilbourn004 at 19:19, 3 December 2019

2019-12-03T19:19:25Z

Admin at 22:30, 19 November 2019

2019-11-19T22:30:12Z

Kuslis001 at 14:17, 17 November 2019

2019-11-17T14:17:22Z

← Older revision		Revision as of 06:04, 14 November 2025
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	''contributed by Lisa Daigle''		''contributed by Lisa Daigle''
		+
		+
		+	----
		+
		+	== The strength and direction of the linear relationship between two continuous variables are determined by the Pearson correlation coefficient (r). It is between -1.00 and +1.00, in which the sign is applied to indicate direction and the value is used to demonstrate strength. The positive correlation, like r = +0.75, means that the better one variable is, the better the other variable (hours spent studying and test scores). The negative correlation, like r = -0.60, implies that the higher one is, the lower the other one is (absences and grades). A correlation value near zero means that there is no significant correlation. In research studies in the education field, correlation helps in demonstrating trends without necessarily having to presuppose causality. A positive correlation between teacher collaboration and student achievement is strong enough to show that there is a relationship between the two, but it does not mean that one causes the other; rather, external factors like school culture or resources cause them both. This distinction is critical in the moral and proper interpretation of data. To do analysis in SPSS is Analyze - Correlate - Bivariate.
		+	The outcome shows the Pearson r and P value, which is the level of significance. A correlation below p =.05 is taken to be statistically significant. The researchers tend to understand the strength of r such that values from 0.10 - 0.29 (weak), 0.30-0.49 (moderate), and more than 0.50 (strong). Also, a scatterplot of data allows one to see the trend and the trend of the relationship. Data-driven leadership relies on correlational thinking. It will enable the educators to determine possible predictors of achievement, evaluate the relationship between attitudes and outcomes, and determine the consistency of school practices. Through learning to correlate, educational leaders will be able to go beyond intuition to evidence-based inquiry.
		+	==
		+	''contributed by Casimir007''

← Older revision		Revision as of 14:27, 20 April 2022
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	7. Click ok and a table will be generated with the Pearson correlation		7. Click ok and a table will be generated with the Pearson correlation

−	''~~Contributed~~ by Lisa Daigle''	+	''contributed by Lisa Daigle''

@@ Line 43: / Line 43: @@
 ''contributed by Lauren Moyer''
-'''How to Find the Pearson Correlation (r) in SPSS'''
+==How to Find the Pearson Correlation (r) in SPSS==
 In SPSS, follow these steps to find the r value:

← Older revision		Revision as of 20:25, 19 February 2022
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	''contributed by Lauren Moyer''		''contributed by Lauren Moyer''
		+
		+	'''How to Find the Pearson Correlation (r) in SPSS'''
		+
		+	In SPSS, follow these steps to find the r value:
		+
		+	1. Once you have the two variables you want to compare, click correlate.
		+	2. Choose bivariate
		+	3. Move the variables you want to compare over to the right box using the arrow.
		+	4. Make sure Pearson is checked off in the window
		+	5. Select two-tailed
		+	6. Click flag significant correlations- asterisks will flag a significant correlation.
		+	7. Click ok and a table will be generated with the Pearson correlation
		+
		+	''Contributed by Lisa Daigle''

← Older revision		Revision as of 15:04, 13 December 2019
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	''contributed by Emily Kilbourn''		''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''

← Older revision		Revision as of 21:09, 5 December 2019
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	Hinkle, D.E., Wiersma, W., & Jurs, S.G. (2003). Applied statistics for the behavioral sciences (5th edition). Boston, M.A.: Houghton Mifflin Company.		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. ~~Retrieved from:~~	+	Samenow, J. (2013). Judah Cohen’s winter outlook: A downer for East Coast winter weather lovers. The Washington Post.
		+
		+	''contributed by Emily Kilbourn''

@@ Line 28: / Line 28: @@
 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).
-[Note: I would like to include the graph/image]
+[[File:winter.jpg]]
 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.

← Older revision		Revision as of 19:19, 3 December 2019
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	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)
		+
		+	==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).
		+
		+	[Note: I would like to include the graph/image]
		+
		+	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. Retrieved from:

@@ Line 8: / Line 8: @@
 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"
+''contributed by Mary Fernand''
@@ Line 16: / Line 16: @@
 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.
-Contribution by: Mykal Kuslis, WCSU Cohort 8
+''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)

← Older revision		Revision as of 14:17, 17 November 2019
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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.
		+
		+	Contribution 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)