Chapter 9: Multivariate Correlational Research

Question 1

Three Causal Criteria

Answer 1

1. Covariance
2. Temporal precedence
3. Internal validity

Question 2

Covariance

Answer 2

As A changes, B changes; e.g., high levels of A go with high levels of B, and low levels of A go with low levels of B (positive correlation).

Question 3

Temporal precedence

Answer 3

A comes first in time, before B.

Question 4

Internal validity

Answer 4

There are no possible alternative explanations for the change in B; A is the only thing that changed.

Question 5

Multivariate designs

Answer 5

Research study designs that involve more than 2 measured variables. Includes longitudinal design, multiple regression, and “pattern and parsimony”.

Question 6

Longitudinal design

Answer 6

A study in which the same variables are measured in the same people at different points in time. Establish temporal precedence.

Question 7

Cross-sectional correlations

Answer 7

A correlation between two variables that are measured at the same point in time. (think “s” for same)
Labeled with double-headed arrows because we are not assuming what causes what - we are just saying that they covary so the cause could go either way.

Question 8

Choosing multivariate design over experimental design

Answer 8

It may not be feasible to randomly assign people to particular experiences.
It may not be ethical to randomly assign people to particular experiences.

Question 9

Auto-correlation

Answer 9

The correlation of one variable with itself, measured at two different times. (think “autonomous”)
Single-headed arrows due to the passing of time - We KNOW that A comes before B in time.

Question 10

Cross-lag correlation

Answer 10

A correlation between earlier measure of one variable and a later measure of another variable. Addresses directionality problem and establishes temporal precedence.
Single-headed arrows that cross, make sure to label!

Question 11

Multiple regression

Answer 11

A statistical technique that computes the relationship between a predictor variable and a criterion variable, controlling for other predictor variables. AKA multivariate regression.

Question 12

Beta

Answer 12

Represents the relationship between IV and DV after controlling for other variables.

Question 13

Interpreting beta

Answer 13

Positive value indicates that as IV increases so does DV.
Negative value indicates inverse relationship.
Zero indicates there is no linear relationship.
Larger beta means stronger relationship.
Smaller beta means weaker relationship.
But there are no guidelines for interpreting effect sizes (weak, moderate, strong) in the same way we do with r.

Question 14

Regression in popular press articles

Answer 14

“Controlled for”
“Taking into account”
“Correcting for”

Question 15

Control for

Answer 15

Holding a potential third variable at a constant level (statistically or experimentally) while investigating the association between two other variables.

Question 16

Parsimony

Answer 16

The degree to which a theory provides the simplest explanation of some phenomenon. In the context of investigating a claim, the simplest explanation of a pattern of data; the best explanation that requires making the fewest exceptions or qualifications.

Question 17

Criterion variable

Answer 17

Dependent variable.

Question 18

German “B” and b

Answer 18

You can compare the strength of German “B” to other “B”s in the same regression table, but not from one table to another. This is a standardized coefficient.

You cannot compare two “b” values within the same table to each other. This is an unstandardized coefficient.

Question 19

Predictor variable

Answer 19

Independent variable.

Question 20

Mediator

Answer 20

A variable that helps explain the relationship between two other variables. Think “causal mechanism”

Question 21

Intro to mediation

Answer 21

Start with an association between IV and DV.
Mediation hypotheses propose a mechanism for a bivariate relationship. Why are these two variables correlated?
Mediation hypotheses are causal statements because they specify a time sequence for the three variables (temporal precedence).

Question 22

Mediators versus third variables

Answer 22

Both involve multivariate research designs.
Both can be detected using multiple regression.
Third variables are external to the bivariate correlation (problematic).
Mediators are internal to the causal variable (not problematic).

Question 23

Mediators versus moderators

Answer 23

Mediators ask “why,” and moderators ask “for whom” or “when.”
Detect moderation by looking at the correlations within groups!

Question 24

Multivariate designs and internal validity

Answer 24

Gets us closer to making causal claims because longitudinal studies help with temporal precedence, and multiple regression helps rule out third variables.

Question 25

Multivariate designs and construct validity

Answer 25

It is important to interrogate the construct validities in all of your variables in multivariate designs. Are your measures reliable and valid? How did you measure your variables?

Question 26

Multivariate designs and external validity

Answer 26

Consider whether the results are generalizable to other people, contexts, situations, and so forth. You would want to look at how the sample was obtained.

Question 27

Multivariate designs and statistical validity

Answer 27

Consider the effect size of the beta and the statistical significance (p value). You might have several betas that are statistically significant, but you can compare their effect sizes within the same beta table.