1. Homogenous group (the subjects are very similar on the variables) Unreliable measurement instrument (our measurements can't be trusted and bounce all over the place) Nonlinear relationship (Pearson's r is based on linear relationships...other formulas can be used in this case) Ceiling or Floor with measurement (lots of scores clumped at the top or bottom...therefore no spread which creates a problem similar to the homogeneous group) Created by Del Siegle (del.siegle@uconn.edu – www.delsiegle.info) for students in EPSY 5601del.siegle@uconn.eduwww.delsiegle.info Factors That Limit a Pearson’s Product-Moment Correlation Coefficient

2. Homogenous group (the subjects are very similar on the variables) Unreliable measurement instrument (our measurements can't be trusted and bounce all over the place) Nonlinear relationship (Pearson's r is based on linear relationships...other formulas can be used in this case) Ceiling or Floor with measurement (lots of scores clumped at the top or bottom...therefore no spread which creates a problem similar to the homogeneous group) Created by Del Siegle (del.siegle@uconn.edu – www.delsiegle.info) for students in EPSY 5601del.siegle@uconn.eduwww.delsiegle.info Factors That Limit a Pearson’s Product-Moment Correlation Coefficient

3. . Weight Height................... Imagine that we created a scatterplot of first graders’ weight and height. Notice how the correlation is around r=.60.

4. . Weight Height................... Imagine that we created a scatterplot of first graders’ weight and height. Notice how the correlation is around r=.60.................... Now let’s add data from second graders (assuming second graders are generally heavier and taller than first graders but the relationship between their weight and height is similar to first graders).

5. . Weight Height................... Imagine that we created a scatterplot of first graders’ weight and height. Notice how the correlation is around r=.60.................... Now let’s add data from second graders (assuming second graders are generally heavier and taller than first graders but the relationship between their weight and height is similar to first graders).................... We now have added third graders. Notice how the total scatterplot for first through third graders resembles r=.80 while each grade resembled r =.60.

6. . Weight Height................... Imagine that we created a scatterplot of first graders’ weight and height. Notice how the correlation is around r=.60.................... Now let’s add data from second graders (assuming second graders are generally heavier and taller than first graders but the relationship between their weight and height is similar to first graders).................... We now have added third graders. Notice how the total scatterplot for first through third graders resembles r=.80 while each grade resembled r =.60.................... Extending the scatterplot to fourth graders increases the value of r even more.

7. . Weight Height................... Imagine that we created a scatterplot of first graders’ weight and height. Notice how the correlation is around r=.60.................... Now let’s add data from second graders (assuming second graders are generally heavier and taller than first graders but the relationship between their weight and height is similar to first graders).................... We now have added third graders. Notice how the total scatterplot for first through third graders resembles r=.80 while each grade resembled r =.60.................... Extending the scatterplot to fourth graders increases the value of r even more.................... As we add fifth graders, we can see that the correlation coefficient is approaching r=.95 for first through fifth graders.

8. . Weight Height................... Imagine that we created a scatterplot of first graders’ weight and height. Notice how the correlation is around r=.60.................... Now let’s add data from second graders (assuming second graders are generally heavier and taller than first graders but the relationship between their weight and height is similar to first graders).................... We now have added third graders. Notice how the total scatterplot for first through third graders resembles r=.80 while each grade resembled r =.60.................... Extending the scatterplot to fourth graders increases the value of r even more.................... The purpose of this demonstration is to illustrate that homogeneous groups

9. . Weight Height................... Imagine that we created a scatterplot of first graders’ weight and height. Notice how the correlation is around r=.60.................... Now let’s add data from second graders (assuming second graders are generally heavier and taller than first graders but the relationship between their weight and height is similar to first graders).................... We now have added third graders. Notice how the total scatterplot for first through third graders resembles r=.80 while each grade resembled r =.60.................... Extending the scatterplot to fourth graders increases the value of r even more.................... The purpose of this demonstration is to illustrate that homogeneous groups produce smaller correlations than heterogeneous groups.

10. Homogenous group (the subjects are very similar on the variables) Unreliable measurement instrument (our measurements can't be trusted and bounce all over the place) Nonlinear relationship (Pearson's r is based on linear relationships...other formulas can be used in this case) Ceiling or Floor with measurement (lots of scores clumped at the top or bottom...therefore no spread which creates a problem similar to the homogeneous group) Created by Del Siegle (del.siegle@uconn.edu – www.delsiegle.info) for students in EPSY 5601del.siegle@uconn.eduwww.delsiegle.info Factors That Limit a Pearson’s Product-Moment Correlation Coefficient

11. ........................................................ Assume that the relationship between Variable 1 and Variable 2 is r = - 0.90. Variable 1 Variable 2

12. ........................................................ If the instrument to measure Variable 1 were unreliable, the values for Variable 1 could randomly be smaller or larger. Variable 1 Variable 2

13. ........................................................ This would occur for all of the scores. Variable 1 Variable 2

14. ........................................................ Unreliable instruments limit our ability to see relationships. Variable 1 Variable 2

15. Homogenous group (the subjects are very similar on the variables) Unreliable measurement instrument (our measurements can't be trusted and bounce all over the place) Nonlinear relationship (Pearson's r is based on linear relationships...other formulas can be used in this case) Ceiling or Floor with measurement (lots of scores clumped at the top or bottom...therefore no spread which creates a problem similar to the homogeneous group) Created by Del Siegle (del.siegle@uconn.edu – www.delsiegle.info) for students in EPSY 5601del.siegle@uconn.eduwww.delsiegle.info Factors That Limit a Pearson’s Product-Moment Correlation Coefficient

16. ........................................................ Imagine that each year couples were married they became slightly less happy. Years’ Married Happiness

17. ............................................................................................................... Imagine that after they are married for 7 years, they slowly become more happy each year. Years’ Married Happiness

18. ............................................................................................................... The negative correlation for the first 7 years… Years’ Married Happiness

19. ............................................................................................................... …cancels the positive relationship for the next 7 years. Years’ Married Happiness

20. ............................................................................................................... Pearson’s r would show no relationship (r=0.00) between year’s married and happiness even though the scatterplot clearly shows a relationship. Years’ Married Happiness

21. ............................................................................................................... This is an example of a curvilinear relationship. Pearson’s r is not an appropriate statistic for curvilinear relationships. Years’ Married Happiness

22. ........................................................ One of the assumptions for using Pearson’s r is that the relationship is linear. That is why the first step in correlation data analysis is to create a scatterplot. Years’ Married Happiness

23. Homogenous group (the subjects are very similar on the variables) Unreliable measurement instrument (our measurements can't be trusted and bounce all over the place) Nonlinear relationship (Pearson's r is based on linear relationships...other formulas can be used in this case) Ceiling or Floor with measurement (lots of scores clumped at the top or bottom...therefore no spread which creates a problem similar to the homogeneous group) Created by Del Siegle (del.siegle@uconn.edu – www.delsiegle.info) for students in EPSY 5601del.siegle@uconn.eduwww.delsiegle.info Factors That Limit a Pearson’s Product-Moment Correlation Coefficient

24. . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Variable 1 Variable 2 1 2 3 4 5 6 7 8 9............................................................................................... Imagine that we are plotting the relationship between Variable 1 and Variable 2.

25. . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Variable 1 Variable 2 1 2 3 4 5 6 7 8 9............................................................................................... As values on Variable 1 increase, values on Variable 2 also increase.

26. . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Variable 1 Variable 2 1 2 3 4 5 6 7 8 9............................................................................................... As values on Variable 1 increase, values on Variable 2 also increase.

27. . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Variable 1 Variable 2 1 2 3 4 5 6 7 8 9............................................................................................... As values on Variable 1 increase, values on Variable 2 also increase.

28. . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Variable 1 Variable 2 1 2 3 4 5 6 7 8 9............................................................................................... As values on Variable 1 increase, values on Variable 2 also increase.

29. . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Variable 1 Variable 2 1 2 3 4 5 6 7 8 9............................................................................................... As values on Variable 1 increase, values on Variable 2 also increase.

30. . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Variable 1 Variable 2 1 2 3 4 5 6 7 8 9...............................................................................................

31. . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Variable 1 Variable 2 1 2 3 4 5 6 7 8 9............................................................................................... Suppose that the top score on the instrument used to measure Variable 2 is 9 (in other words there is a “ceiling” on Variable 2’s measurement instrument).

32. . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Variable 1 Variable 2 1 2 3 4 5 6 7 8 9............................................................................................... Our subjects can continue to have higher scores on Variable 1, but they are restricted on Variable 2.

33. . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Variable 1 Variable 2 1 2 3 4 5 6 7 8 9............................................................................................... Our subjects can continue to have higher scores on Variable 1, but they are restricted on Variable 2.

34. . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Variable 1 Variable 2 1 2 3 4 5 6 7 8 9............................................................................................... Our subjects can continue to have higher scores on Variable 1, but they are restricted on Variable 2.

35. . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Variable 1 Variable 2 1 2 3 4 5 6 7 8 9............................................................................................... Our subjects can continue to have higher scores on Variable 1, but they are restricted on Variable 2.

36. . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Variable 1 Variable 2 1 2 3 4 5 6 7 8 9............................................................................................... Our subjects can continue to have higher scores on Variable 1, but they are restricted on Variable 2.

37. . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Variable 1 Variable 2 1 2 3 4 5 6 7 8 9...............................................................................................

38. . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Variable 1 Variable 2 1 2 3 4 5 6 7 8 9............................................................................................... We can see that the ceiling on Variable 2 is causing us to have a lower correlation than if our subjects were able to continue to score higher on Variable 2.

39. . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Variable 1 Variable 2 1 2 3 4 5 6 7 8 9............................................................................................... Our subjects can continue to have higher scores on Variable 1, but they are restricted on Variable 2.

40. . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Variable 1 Variable 2 1 2 3 4 5 6 7 8 9 10................... When a variable is measured with an instrument that has a ceilings (or floor), we obtain a lower correlation coefficients than if the variable were measured with an instrument that did not have a ceiling (or floor).............................................................................

41. Homogenous group (the subjects are very similar on the variables) Unreliable measurement instrument (our measurements can't be trusted and bounce all over the place) Nonlinear relationship (Pearson's r is based on linear relationships...other formulas can be used in this case) Ceiling or Floor with measurement (lots of scores clumped at the top or bottom...therefore no spread which creates a problem similar to the homogeneous group) Created by Del Siegle (del.siegle@uconn.edu – www.delsiegle.info) for students in EPSY 5601del.siegle@uconn.eduwww.delsiegle.info Factors That Limit a Pearson’s Product-Moment Correlation Coefficient