Module 7 - Correlational Designs Flashcards Preview

Introduction to Psychological Research Methods > Module 7 - Correlational Designs > Flashcards

Flashcards in Module 7 - Correlational Designs Deck (22)
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1
Q

If you wanted to see whether there was a relationship between two continuously valued variables, what design would you use?

A

Correlational

2
Q

What is a “median split”?

A

When you take one variable and divide it into two equally sized groups around the median, then use a t-test or similar test. (This is generally a bad idea, more Type II errors are common.)

3
Q

Explain Pearson’s r.

A

r is the correlation coefficient, a measure of the strength and relationship between two continuous variables. it ranges from -1 to +1, with r=1.00 indicating a perfect positive correlations, 4=0.00 indicating no correlation, and r=-1.00 indicating a perfect negative correlation.

4
Q

what is the formula for r?

A

Σ ZxZy
r = ———–
N-1

5
Q

Explain how to calculate r.

A

transform both variables into Z-scores, find the product of each pair of z scores, add all of the products together, then divide that total by N-1.

6
Q

If p > .05…

A

reject the null hypothesis, and consider the relationship between your variables to be statistically significant.

7
Q

If p = .05 or p < .05…

A

retain the null hypothesis – you haven’t found evidence for a relationship.

8
Q

What does it mean if we reject Ho?

A

Our correlation is statistically significant, this doesn’t mean the correlation is large, strong, or important, it just means we’ve found a pattern in our data that is unlikely to occur randomly.

9
Q

What are the rough guidelines for interpreting r?

A

If the magnitude of Pearson’s r is .10-.29 the effect size is weak. If the magnitude of Pearson’s r is .30-.49 the effect size is moderate. If the magnitude of Pearson’s r is .50+ the effect size is strong.

10
Q

What is a statistical model?

A

a mathematical abstraction that lets you make guesses about a data set.

11
Q

What is the coefficient of determination?

A

The proportion of variance explained by the relationship between two variables as expressed by r^2.

12
Q

What are the assumptions for use of Pearson’s r?

A

The relationship between the variables isn’t curvilinear, the data are interval or ratio scaled, and there aren’t any major outliers.

13
Q

Explain linear vs curvilinear relationships, and how they are relevant to Pearson’s r.

A

linear relationships go in a straight line, curvilinear is a relationship that is rounded or otherwise not a straight line, for Pearson’s r tests, the data must be linear, it cannot detect curvilinear relationships, even if they are very obvious, such as a U shape.

14
Q

What is the most common non-parametric equivalent of Pearson’s r?

A

Spearman’s Rho.

15
Q

Why not always use Spearman’s Rho.

A

Spearman’s Rho works by transforming the data into ranks and then correlating the ranks with one another. In transforming the data into ranks information is lost.

16
Q

Explain subgroups.

A

if our sample consists mainly of two subgroups, we might see an illusory correlation.

17
Q

What does a scatterplot show?

A

scores on one varied plot against scores on a second varied plot.

18
Q

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

A

There is a small relationship between A and B.

19
Q

A correlation of .5 would produce a scatterplot in which the slope is…

A

upwards (from the bottom left corner to the top right corner of the graph).

20
Q

How much greater is the shared variance between two variables if the Pearson correlation coefficient between them is –.4 than if it is .2?

A

four times as great.

21
Q

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

A

They share variance.

22
Q

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

A

We should accept the null hypothesis.