Exam 3

Question 1

Multivariate designs

Answer 1

• involve more than 2 measured variables

Question 2

3 criteria for causation (apply these to correlation research)

Answer 2

1. covariance
2. temporal precedence
3. internal validity

Question 3

longitudinal design

Answer 3

• can provide evidence for temporal precedence by measuring the SAME variable in the SAME people at several points in time
• used in developmental psychology
• same variable, same group, over time

Question 4

Results of a longitudinal design

Answer 4

• because multiple variables involved -> gives individual correlations (3)
• 1 cross-sectional correlation = test to see whether 2 variables, measured at the same points in time are correlated
  (over evaluation time 1)-> (narcissism time 1)
• cannot alone establish temporal precedence
  2. Autocorrelations - the correlation of one variable with itself, measured at 2 different times (overvaluation time 1)-> (overvaluation time 2).
  3. cross lag correlation - show whether the earlier measure of one variable is associated with the later measure of the other variable (3 results)
  diagonal correlations

Question 5

longitudinal studies can provide some evidence for a causal relationship

Answer 5

1. covariance - when 2 variables correlated and CI does no include ) - covariance
2. temporal precedence -> each variable measured at clearly different points
3. internal validity -> when only measuring 2 key variables , longitudinal studies cannot rule out 3rd variables

Question 6

multiple regression -> deals with internal validity

Answer 6

• a statistical technique that computes the relationship bw a predictor variable and a criterion variable/controlling for other predictor variables

Question 7

control for

Answer 7

holding a potential 3rd variable at a constant
- accounting for subgroups

Question 8

2 variables

Answer 8

• criterion variable = dependent variable, variable researchers are most interested in understanding or predicting
• predictor variables - indep. variables, used to explain variance in the criterion variable

Question 9

Beta

Answer 9

• similar to r
  positive beta - positive relationship between predictor and criterion
  negative - negative relationship b/w predictor and criterion variable
  beta that is zero - no relationship
  higher beta = stronger
  lower beta = weaker

Question 10

common phrases with regression in media

Answer 10

” controlled for”
- “adjusting for”
- “considering”

Question 11

regression does not equal causation

Answer 11

• even though multivariate designs
• analyzed w regression stats can rule out 3rd variable
• can’t establish temporal precedence
• well run experiments more convincing then causation

Question 12

parsimony

Answer 12

• degree to which a scientific theory provides the simplist explanation of some phenomena
• ex - simplest context of investigating data

Question 13

mediator/mediating variable

Answer 13

• a variable that helps explain the relationship between two variables, can be experimental or correlation study
• mediation hypothesis -> causal claims
• must have temporal precedence

Question 14

mediators vs 3rd variables

Answer 14

mediators
- both have to be tested
- tells theoretical meaning
- direct interest to the researcher
- why are variables linked?
3rd variables
- variable is external to the original bivariate correlation
- nuance
BOTH CAN BE TESTED W/multiple regressions

Question 15

4 big validity questions for multivariate designs?

Answer 15

= same as bivariate correlation
1. internal
2. construct
3. statistical
4. external

Question 16

Interaction effect

Answer 16

• a result fro a factorial design in which the difference in the levels of the IV variable changes, depending on the level of the other IV, a difference of differences also called interaction

Question 17

Interaction

Answer 17

the difference in differences, the effect of one IV depende on the level of the other IV

Question 18

factorial design

Answer 18

• a study in which there are 2 or more IV or factor, way researcher tests for interactions
  most common -> researchers cross the 2 IVs, study each possible combo cell, one of the possible combos of 2 IVS

Question 19

simplest factorial design (2 IVS/FACTORS)

Answer 19

• cell phone use, age (each has 2 factors)
• 2x2 factorial design
• the levels of the IV are crossed w/2 levels of the other IV
  2 x 2 = 4 cell design

Question 20

participant variable

Answer 20

• a variable whose levels are selected (measured)
• not manipulated
  ex - gender, ethnicitu,

Question 21

external validity

Answer 21

• testing limits
• when researchers test an IV in more than one group at once, testing whether the effect generalizes

Question 22

moderators in factorial design

Answer 22

• moderator is an IV that changes the relationship between another IV and a DV

Question 23

interpreting factorial results

Answer 23

1 main effect
- overall effect of one IV on the DV averaging over the levels of the other iV
- main effect -> simple difference
- factorial design w/2 IVs = main effects

Question 24

marginal means

Answer 24

• arithmetic means for each level of the IV averaging over other levels of the IV
• sample size is equal: marginal means are a simple average
• sample size is unequal - marginal mean computed using the weighted average counting the larger sample more

Question 25

estimating how large each main effect is

Answer 25

- calculate difference between marginal means - compute 95% CI - contains 0 - not significant - doesn't contain 0 = significant

Question 26

interaction effect

Answer 26

the difference in differences detecting on a graph - line graph - lines are not parallel may be interaction - lines are parrallelish- no interaction - interactions are more important than main effects

Question 27

factorial design variations

Answer 27

- independent groups factorial design - both IVs are studied as indep. groups - if a design is 2x2 (4 groups) within groups factorial design - both IV variables manipulated as within groups - if design is 2x2, one group participates in all 4 combos, cells of design counterbalanced - fewer participants mixed factorial design - one IV is manipulated as Indep. groups, other is manipulated as within groups - levels of IV can increase - can have 3 levels 2x3 = 6 design

Question 28

increasing the number of the iV's

Answer 28

- 2x2x2 factorial design - three way design - 3 main effects - 3 possible two way interactions

Question 29

identifying factorial design in popular media

Answer 29

- "it depends" - "only when" - look for participant variable (age, gender)