ch8

Question 1

Bivariate Correlations (bivariate associations)

Answer 1

• associations that examine exactly two variables at a time
• When there are multiple variables, the authors of the study can present the bivariate correlations between different pairs of variables separately

Question 2

types of bivariate correlations

Answer 2

• Describing associations between 2 quantitative variables
• Describing associations with categorical data

Question 3

how do you describe associations between 2 quantitative variables

Answer 3

Describe the relationship between variables using scatterplots and correlation coefficient r

Question 4

qualities of r:

Answer 4

• Direction
• Strength- how close r is to (-)1

Question 5

Cohens guide to evaluating strength of association

Answer 5

r… would be considered to have an effect size of
(-).10 = small/weak
(-) .30 = medium/moderate
(-) .50 = large/strong

Question 6

how to describe associations with categorical data

Answer 6

• More common to plot the results of an association with a categorical variable as a bar graph: examines difference between the group means
• t-test statistic

Question 7

mean

Answer 7

arithmetic avg

Question 8

t-test

Answer 8

a statistic to test the difference between two group averages (means)

Question 9

What makes a study correlational? (What is it supported by?)

Answer 9

• Having two measured variables
• An association claim is supported by a study design- correlational research- in which all variables are measured, not supported by statistics or graphs

Question 10

Which validities are most important for association claims?

Answer 10

construct and statistical

Question 11

how to interrogate construct validity in association claims

Answer 11

• How well are each of the variables measured?
• Does the measure have good reliability?
• Is it measuring what it’s supposed to?

Question 12

what kinds of validity are important in the construct validity of association claims

Answer 12

• Face validity
• Concurrent validity (are these results consistent with previous established measures of this construct?)
• Discriminant and convergent validity

Question 13

how to interrogate statistical validity in association claims

Answer 13

look at:
- effect size

• statistically significance

-outliers

• restriction of range
• curvilinear association

Question 14

effect size (+ how to compute)

Answer 14

Effect size: describes the strength of an association

compute by taking value for r and square it

Question 15

how does effect size help us get accurate predictions?

Answer 15

• Stronger effect sizes mean more accurate predictions
• a larger effect size is usually more important, but in life/death situations even a small effect size can have dramatic effects

Question 16

statistical significance

Answer 16

Statistical significance: how likely it is to get a correlation of that size by chance if there isn’t one in the population

**depends on effect size and sample size

uses p value

Question 17

When researchers obtain a correlation coefficient (r ) they establish

Answer 17

• Direction of slope
• Strength of relationship
• Statistical significance

Question 18

Logic of statistical inference

Answer 18

if there is a correlation in a sample there probably is one in the population

Question 19

Probability estimate (p value or sig)

Answer 19

provides information about stat. Significance by evaluating the probability that the sample’s association came from a zero-association population.

• Less than .05 is statistically significant, considered rare
• more than .05 = not rare not statisitically significant/nonsignificant/ not rare

Question 20

how does effect size effect statistical significance?

Answer 20

Usually the stronger a correlation is ( the larger its effect size) the more likely it will be statistically significant

• Because, the stronger an association is, the more rare it would be in a zero-association population
• Need to examine the p value to make sure, because it is dependent on both effect size and sample size

Question 21

when can a small effect size be statistically significant

Answer 21

A small effect size might be significant if the sample is large, but not in a small sample

Question 22

how is statistically significant info indicated in journal articles

Answer 22

indicated with an asterisk (*), “sig,” or p<.05 or p<.01

Question 23

Outlier (+ how to find)

Answer 23

an extreme score

can have effect on correlation coefficient

• look at scatterplot

Question 24

when are outliers most problematic

Answer 24

• Most problematic when extreme scores on both variables
• Most influential and problematic when sample size is small

Question 25

Restriction of range (+ when does it happen)

Answer 25

when there’s not a full range of scores on one of the variables in an association, it can make the correlation look smaller than it is

• Restriction of range can apply when for some reason one of the variables has little variance (i.e. all upper middle class parents)
• ask about it when correlation is weak

Question 26

Correction for restriction of range

Answer 26

statistical technique that estimates the full set of scores based on what we know about a restricted set, and recomputes the calculation

Question 27

how to correct for restriction of range (2 ways)

Answer 27

• Use statistical techniques (correction for restriction of range
• Recruit more people at ends of the spectrum

Question 28

Curvilinear association

Answer 28

a relationship between two variables that isn’t a straight line- positive up to a point, then negative, or vice versa

• Correlation coefficient may report that there is zero/no relationship , because its meant for straight lines
• look at scatterplot to find

Question 29

To statistically analyze curvilinear associations

Answer 29

compute the correlation between one variable and the square root of the other

Question 30

three causal criteria (in relation to association claims)

Answer 30

• covariance of cause and effect
• temporal precedence (directionality problem)
• internal validity (third variable problem)

Question 31

Directionality problem:

Answer 31

in association claims, we don’t know which came first (no temporal precedence)

Question 32

third variable problem

Answer 32

Third variable problem: is there a c variable that is associated with both a and b, independently? (internal validity)

Question 33

how to check if theres an internal validity third variable problem

Answer 33

If you separate results into subgroups of a third variable, is there still a correlation in each subgroup?

• if so, no internal validity problem
• if not, is a spurious association

Question 34

spurious association

Answer 34

Spurious association: an association only present due to a third variable

Question 35

External validity in association claims

Answer 35

does the sample generalize to other people, places, and times

• Size of sample isn’t as important as how the sample was selected from the population
• If a bivariate correlational study doesn’t use a random sample, it is still useful- generalizability can be addressed in another study
• sample may still generalize to different populations

Question 36

moderator (+ what kind of validity does it inform)

Answer 36

Moderator: a variable that changes the relationship of itself and another variable, depending on its level

• Informs external validity- if an association is moderated by a variable, then we know it doesn’t generalize to other situations