Question 1

A Pearson's correlation coefficient of -.5 would be represented by a scatterplot in which:

	a.	The regression line slopes upwards.
	b.	Half of the data points sit perfectly on the line.
	c.	There is a moderately good fit between the regression line and the individual data points on the scatterplot.
	d.	The data cloud looks like a circle and the regression line is flat.

Question 2

If you have a curvilinear relationship, then:

	a.	It is not appropriate to use Pearson's correlation because it assumes a linear relationship between variables.
	b.	You can use Pearson's correlation; you just need to remember that a curve indicates that the variables are not linearly related.
	c.	Pearson's correlation can be used in the same way as it is for linear relationships.
	d.	Transforming the data will not help.

Question 3

If two variables are significantly correlated, r = .67, then:

	a.	They share variance.
	b.	The variables are independent.
	c.	There is no unique variance.
	d.	The relationship is weak.

Question 4

Looking at the table below, which variables were the most strongly correlated?

u05q1 Question 16 table
		Work ethic	Annual income	IQ
Work ethic	Pearson's correlation	1.000	.72	.66
	Sig. (2-tail)	.	.001	.000
	N	550	550	550
Annual income	Pearson's correlation	.72	1.000	.47
	Sig. (2-tail)	.000	.	.03
	N	550	550	550
IQ	Pearson's correlation	.66	.47	1.000
	Sig. (2-tail)	.000	.03	.
	N	550	550	550

	a.	Work ethic and IQ.
	b.	None of the correlations are significant.
	c.	Annual income and IQ.
	d.	Work ethic and annual income.

Question 5

The relationship between two variables partialling out the effect that a third variable has on one of those variables can be expressed using a:

	a.	Point-biserial correlation.
	b.	Bivariate correlation.
	c.	Semi-partial correlation.
	d.	Partial correlation.

Question 6

A Pearson's correlation of -.71 was found between number of hours spent at work and energy levels in a sample of 300 participants. Which of the following conclusions can be drawn from this finding?

	a.	Amount of time spent at work accounted for 71% of the variance in energy levels.
	b.	There was a strong negative relationship between the number of hours spent at work and energy levels.
	c.	The estimate of the correlation will be imprecise.
	d.	Spending more time at work caused participants to have less energy.

Question 7

The coefficient of determination:

	a.	Is a measure of the amount of variability in one variable that is shared by the other.
	b.	Is the square root of the correlation coefficient.
	c.	Is the square root of the variance.
	d.	Indicates whether the correlation coefficient is significant.

Question 8

The correlation between two variables A and B is .12 with a significance of p < .01. What can we conclude?

	a.	That variable A causes variable B.
	b.	That there is a substantial relationship between A and B.
	c.	All of these.
	d.	That there is a small relationship between A and B.

Question 9

Which of the following statements about Pearson's correlation coefficient is not true?

	a.	It can be used on ranked data.
	b.	It cannot be used with binary variables (those taking on a value of 0 or 1).
	c.	It can be used as an effect size measure.
	d.	It varies between -1 and +1.

Question 10

If a correlation coefficient has an associated probability value of .02 then:

	a.	The hypothesis has been proven.
	b.	There is only a 2% chance that we would get a correlation coefficient this big (or bigger) if the null hypothesis were true.
	c.	We should accept the null hypothesis.
	d.	The results are important.