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Question 1

Multivariate designs

Answer 1

involve more than two measured variables

Question 2

two types of multivariate design

Answer 2

• Longitudinal designs
• Multiple-regression

Question 3

Longitudinal designs

Answer 3

provides evidence for temporal precedence by measuring the same variable in the same people at several different times

Question 4

types of correlations from longitudinal designs

Answer 4

• Cross-sectional correlations
• Autocorrelations
• Cross-lag correlations

Question 5

Cross-sectional correlations

Answer 5

tell us whether two variables measured at the same time are correlated

Question 6

Autocorrelations

Answer 6

when a variable correlates with itself across time

Question 7

Cross-lag correlations

Answer 7

• show whether an earlier measure of one variable is associated with a later measure of the other variable
• helps establish temporal precedence/ directionality problem

Question 8

how do you know which variable comes first from a cross-lag correlation?

Answer 8

If earlier variable A’s correlation to B’s later on is statistically significant and earlier B’s correlation to A’s later on isn’t, we can know that A comes before B

whichever relationship is statistically significant is the direction of the relationship

Question 9

Three possible patterns for cross-lag correlations

Answer 9

• A comes before B
• B comes before A
• Mutually reinforcing: both correlations are statistically significant, indicating a cycle in which each variable reinforces the other continuously

Question 10

do longitudinal studies address the third variable problem?

Answer 10

• hen conducted simply, longitudinal studies only measure the two key variables and can’t rule out a third variable
• Might be able to design their studies in particular ways or do statistical analyses to address some third variables

Question 11

Multiple regression (multivariate regression)

Answer 11

a statistical technique to help rule out some third variables, addressing internal validity concerns

Question 12

Criterion variable

Answer 12

-dependent variable

• the variable in multiple regression they are most interested in understanding or predicting/ outcome of interest
• Specified in either top row or title of regression table

Question 13

Predictor variables

Answer 13

-independent variables

• the rest of the variables measured in a regression analysis that may cause the criterion variable

-Although it is considered an independent variable, it isn’t manipulated, and causation cannot be inferred

Question 14

Beta

Answer 14

• similar to r, in that it denotes direction and strength of relationships. Represents the relationship between the criterion variable and the predictor variable

Question 15

direction of beta

Answer 15

• Positive beta means a positive relationship when other predictor variables are statistically controlled for, and vice versa
• A beta that is zero or not statistically different from zero represents no relationship when other predictors are statistically controlled for

Question 16

can you compare betas on different regression tables?

Answer 16

Within a single regression table, we can usually compare predictor variables but not across different regression tables

• No absolutes for effect sizes (like cohen’s for r)

Question 17

how do you measure effect sizes of beta?

Answer 17

R squared is a measure of effect size- regression analyses (software) often includes measure of r squared- gives you information about effect size for each of the predictor variables

Question 18

The coefficient b

Answer 18

The coefficient b
same as beta except it hasn’t been standardized to a scale (between 1 and -1)

• so it can’t be compared even within same table

Question 19

Statistical significance of beta

Answer 19

Show us whether or not the results are due to chance/sampling error- whether or not they actually come from a population where the relationship is zero

Question 20

how is statistical significance marked on a regression table? what values

Answer 20

Regression tables have column labeled sig or p, or asterisked footnote with p value for each beta
Less than .05 = statistically significant
If more than .05 data isn’t significant

Question 21

what does adding more predictors to a regression table do? (2)

Answer 21

• Can help control for several third variables at once (closer to causal claim)
• Examines the betas for all the other predictor variables to get a sense of which factors most strongly predict chance of our criterion variable

Question 22

when can beta exceed an absolute value of 1?

Answer 22

• Multicollinearity- where predictors are so strongly correlated that you can’t separate the contribution of each
• Or when variables aren’t continuous variables/ or dichotomous yes or no variables

Question 23

Multicollinearity

Answer 23

where predictors are so strongly correlated that you can’t separate the contribution of each

Question 24

R squared

Answer 24

measure of effect size- regression analyses (software) often includes measure of r squared- gives you information about effect size for each of the predictor variables

Question 25

Phrases to detect if regression analysis was used in media

Answer 25

- “Controlled for” - “Taking into account” - “Correcting for” or “adjusting for’

Question 26

Regression doesn’t establish causation- Even if a large # of variables are controlled for, two problems

Answer 26

- Temporal precedence isn’t always established - Even when a study takes place over time (longitudinally,) can’t control for unknown third variables- variables they didn’t think to measure

Question 27

why are experiments more convincing than multiple-regression in terms of internal validity?

Answer 27

random assignment makes two groups likely to be equal on any third variable the researchers don't think to measure

Question 28

Parsimony (in causal claims)

Answer 28

the simplest explanation of a pattern of data- the theory that requires making the fewest exceptions

Question 29

“Pattern and parsimony” approach

Answer 29

- investigate causality by using a variety of correlational studies that all point in a single, causal direction - includes making and testing a series of questions based off/ all can be explained by a simple theory

Question 30

Mediators (mediating variables)

Answer 30

a variable that serves as an explanation for a connection between variables

Question 31

5 steps of mediation

Answer 31

1. Test relationship c (the original correlation or causation) 2. Test relationship a ( the relationship between the first variable and the mediator 3. Test relationship b ( relationship between third variable and mediator 4. Run a regression test using the first variable and the mediator as predictors of the third variable - the relationship between the first and third variables should drop when the mediator is controlled for 5. Temporal precedence: mediation is definitively established only when the proposed causal variable is measured or manipulated first in a study, followed later by the mediating variable, followed by the proposed outcome variable

Question 32

similarities and differences of mediators and third variables

Answer 32

Similarities: - Multivariate research designs - Detected using multiple regression Differences: - third variables are external to the bivariate correlation (problematic “lurking variable”) - mediators are internal to the causal variable, meaning they work directly as a step within the relationship and aren’t problematic

Question 33

Mediators vs moderators

Answer 33

- Mediators ask “why”, come between two variables - Moderators ask “for whom” or “when”- does it work in every situation- is it different based on the level of the variable?

Question 34

statistical validity in multivariate designs

Answer 34

- Effect size of beta (compare within table) - and the statistical significance Also look for - Subgroups - Outliers - Curvilinear associations