II - Bivariate Correlational Research

Question 1

Strength of r

Answer 1

+/- .10 = small or weak
+/- .30 = Medium, or moderate
+/- .50 = Large, or strong

Question 2

Point-biserial correlation

Answer 2

A statistical test used
for evaluating the association between one
categorical variable and one quantitative
variable.

Question 3

Phi coefficient:

Answer 3

categorical variables

Question 4

Considerations for investigating statistical validity

Answer 4

Need to consider:
 The effect size
 The statistical significance of the relationship
 A ny sub tli bgroups or outliers
 Whether a zero relationship might actually be
curvilinear.

Question 5

Statistically Significant

Answer 5

A conclusion that a
result is extreme enough that it is unlikely to have happened by chance if the null hypothesis is true.
.Statistical significance calculations help
researchers evaluate the p y robability that the result
came from a population in which the association is
really zero.
 How often would we get an r of 0.24 just by chance,
even if there is no association in the population?

Question 6

P Value

Answer 6

The p p value helps evaluate the p y robability that
the sample’s association came from a
population in which the association is zero.
 If the probability is less than 5% (p < .05) the result
is very unlikely to have come from a “zeroassociation” population = statistically significant.
 If the probability is higher than 5% (p ≥ .05), we
cannot rule out the possibility that the result came
from a population in which the association is zero
= nonsignificant.

Question 7

Spurious

Answer 7

An association that is attributable
only to systematic mean differences on
subgroups within the sample.
 When you consider the subgroups separately, there
is no association (or the opposite association
appears)
two events or variables have no direct causal connection, yet it may be wrongly inferred that they do, due to either coincidence or the presence of a certain third, unseen factor (referred to as a “confounding factor” or “lurking variable”).
Suppose there is found to be a correlation between A and B. Aside from coincidence, there are three possible relationships:
A causes B,
B causes A,
OR
C causes both A and B.

Question 8

Outlier

Answer 8

One or a few cases that stand out as
either much higher or much lower than most of
the other scores in a sample.
 In a bivariate correlation, outliers are mainly
problematic when they involve extreme scores on
both of the variables.
 Outliers matter the most when a sample is small.

Question 9

The statistically valid way to analyze a

curvilinear relationship is to use:

Answer 9

a quadratic
model: test the correlation between one
variable and the square of the other

Question 10

Causation: Directionality problem

Answer 10

A situation in which it is
unclear which variable in an association came
first.

Question 11

Causation: Third-variable problem

Answer 11

A situation in which
plausible alternative explanations exist for the
association between two variables.

Question 12

Moderator

Answer 12

A third variable that, depending on
its level, changes the relationship between two
other variables.
Ex 1: Marital status moderates the relationship
between maternal employment and child
achievement
Ex 2: Weekend/weekday status does not moderate the relationship between small talk and well
being

Question 13

Moderators vs Subgroups

Answer 13

When we are asking , about moderators, our
goal is to ask whether the association
between the two variables is different within
the levels of some third variable (the
moderator).
When we are asking about subgroups, our
goal is to make sure that the overall
associ tiaon b t th t i bl i th between the two variables is the
same within the two subgroups.