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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 – for students in EPSY

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

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. Weight Height Imagine that we created a scatterplot of first graders weight and height. Notice how the correlation is around r= Now lets 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).

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

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. Weight Height Imagine that we created a scatterplot of first graders weight and height. Notice how the correlation is around r= Now lets 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 = Extending the scatterplot to fourth graders increases the value of r even more.

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

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. Weight Height Imagine that we created a scatterplot of first graders weight and height. Notice how the correlation is around r= Now lets 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 = Extending the scatterplot to fourth graders increases the value of r even more The purpose of this demonstration is to illustrate that homogeneous groups

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

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Assume that the relationship between Variable 1 and Variable 2 is r = Variable 1 Variable 2

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

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

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

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Image that each year couples were married they became slightly less happy. Years Married Happiness

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

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

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

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Pearsons r would show no relationship (r=0.00) between years married and happiness even though the scatterplot clearly shows a relationship. Years Married Happiness

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This is an example of a curvilinear relationship. Pearsons r is not an appropriate statistic for curvilinear relationships. Years Married Happiness

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One of the assumptions for using Pearsons 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

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Variable 1 Variable Imagine that we are plotting the relationship between Variable 1 and Variable 2.

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Variable 1 Variable As values on Variable 1 increase, values on Variable 2 also increase.

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Variable 1 Variable As values on Variable 1 increase, values on Variable 2 also increase.

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Variable 1 Variable As values on Variable 1 increase, values on Variable 2 also increase.

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Variable 1 Variable As values on Variable 1 increase, values on Variable 2 also increase.

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Variable 1 Variable As values on Variable 1 increase, values on Variable 2 also increase.

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Variable 1 Variable

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Variable 1 Variable Suppose that the top score on the instrument used to measure Variable 2 is 9 (in other words there is a ceiling on Variable 2s measurement instrument).

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Variable 1 Variable Our subjects can continue to have higher scores on Variable 1, but they are restricted on Variable 2.

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Variable 1 Variable Our subjects can continue to have higher scores on Variable 1, but they are restricted on Variable 2.

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Variable 1 Variable Our subjects can continue to have higher scores on Variable 1, but they are restricted on Variable 2.

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Variable 1 Variable Our subjects can continue to have higher scores on Variable 1, but they are restricted on Variable 2.

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Variable 1 Variable Our subjects can continue to have higher scores on Variable 1, but they are restricted on Variable 2.

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Variable 1 Variable

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Variable 1 Variable 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.

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Variable 1 Variable Our subjects can continue to have higher scores on Variable 1, but they are restricted on Variable 2.

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Variable 1 Variable 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)

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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 – for students in EPSY

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