Partial Correlation
A partial correlation is the same as a Pearson's bivariate correlation , except that you add a control variable.
The control variable must be continuous, and the independent and dependent variables must both be continuous.
You perform and interpret the hypothesis test the same as for a Pearson's bivariate correlation . The hypotheses are:
Ho: There is no relationship between IV and DV, controlling for CV. r = 0
H1: There is a relationship between IV and DV, controlling for CV. r ne 0
*Fill in what IV, DV, and CV are in the above hypotheses.
Example 1:
Dependent Variable = income; measured in dollars
Independent Variable = tv hours; measured in daily hours watched
Control Variable (CV) = education; measured in years
Hypotheses:
Null: There is no relationship between the number of TV hours watched daily and income, adjusting for the number of years of education. r = 0
Research: There is a relationship between the number of TV hours watched daily and income, adjusting for the number of years of education. r ne 0
When we control for the effect of education on the relationship between number of TV hours watched daily and income, we find the following by doing a partial correlation with the GSS2000 data:
r = -.11, p = .000
p is less than alpha. Reject null.
There is a weak, negative relationship between the number of TV hours watched daily and income when we control for the effect of education. As the number of daily TV viewing hours goes up, income goes down for people of any level of education (r = -.11, p = .000). Or, as income increases the number of daily TV viewing hours goes down.
r2 = .11 * .11 = .0121
When controlling for the effect of education, the number of TV hours watched daily explains 1% of the variation in income. Or, when controlling for the effect of education, income explains 1% of the variation in the number of TV hours watched daily.
DO THIS AGAIN WITH GSS2006 DATA
Example 2: Take Home Exercise
I think the number of children that people have is determined, in part, by the amount of education they have, controlling for current income.
number of children (childs)
education (educ)
income (rincom98)
GET STATS from GSS2006
Example 3: Take Home Exercise
I think that age influences how well students do in college statistics courses, controlling for the number of previous math or statistics courses taken.
age = age in years
performance in statistics = average score in course on a scale of 0 to 100
previous math or statistics courses = total number of math or statistics courses previously taken for college credit
r = .18, p = .36
The control variable must be continuous, and the independent and dependent variables must both be continuous.
You perform and interpret the hypothesis test the same as for a Pearson's bivariate correlation . The hypotheses are:
Ho: There is no relationship between IV and DV, controlling for CV. r = 0
H1: There is a relationship between IV and DV, controlling for CV. r ne 0
*Fill in what IV, DV, and CV are in the above hypotheses.
Example 1:
Dependent Variable = income; measured in dollars
Independent Variable = tv hours; measured in daily hours watched
Control Variable (CV) = education; measured in years
Hypotheses:
Null: There is no relationship between the number of TV hours watched daily and income, adjusting for the number of years of education. r = 0
Research: There is a relationship between the number of TV hours watched daily and income, adjusting for the number of years of education. r ne 0
When we control for the effect of education on the relationship between number of TV hours watched daily and income, we find the following by doing a partial correlation with the GSS2000 data:
r = -.11, p = .000
p is less than alpha. Reject null.
There is a weak, negative relationship between the number of TV hours watched daily and income when we control for the effect of education. As the number of daily TV viewing hours goes up, income goes down for people of any level of education (r = -.11, p = .000). Or, as income increases the number of daily TV viewing hours goes down.
r2 = .11 * .11 = .0121
When controlling for the effect of education, the number of TV hours watched daily explains 1% of the variation in income. Or, when controlling for the effect of education, income explains 1% of the variation in the number of TV hours watched daily.
DO THIS AGAIN WITH GSS2006 DATA
Example 2: Take Home Exercise
I think the number of children that people have is determined, in part, by the amount of education they have, controlling for current income.
number of children (childs)
education (educ)
income (rincom98)
GET STATS from GSS2006
Example 3: Take Home Exercise
I think that age influences how well students do in college statistics courses, controlling for the number of previous math or statistics courses taken.
age = age in years
performance in statistics = average score in course on a scale of 0 to 100
previous math or statistics courses = total number of math or statistics courses previously taken for college credit
r = .18, p = .36
posted by jammie price at 2:38 PM
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