final quiz rm ch 9, 10, and 12

Question 1

Reviewing three Casual Claims

Answer 1

Covariance
Temporal Precedence
Internal Validity

Question 2

multivariate designs

Answer 2

longitudinal designs help address temporal precedence
multiple regression analyses help address internal validity

Question 3

Longitudinal designs

Answer 3

measure the same variable in the same group of people at different times

Question 4

cross sectional

Answer 4

two variables measured at the same point in time are correlated

Question 5

cross-lag correlations

Answer 5

the earlier measure of one variable is associated with a later measure of another variable (looking at how people change over time) – help us establish temporal precedence

Question 6

Regression results indicate whether a third variable explains the relationship

Answer 6

Criterion variables and predictor variables
- Using beta to test for third variables
- Predictor is independent variable
- In a multiple regression analysis, you will be studying three or more variables – you choose the variable you want to understand and that is called your criterion or dependent variable
- Where you see multiple regression output you will see a beta – in this example you will have one beta for exposure to sex on tv and another beta for age
- R just shares that there is a relationship between variables
- There is a negative relationship between the predictor variable and the criterion variable when other variables are constant
- No relationship when other predictors are controlled for

Question 7

Smaller the beta the weaker the relationship and the bigger the beta the stronger the relationship (B will be what you see in graphs) – B is unstandardized coefficient and beta is standardized coefficient

Answer 7

B is just like beta and we think about the positives and negatives in the same way
o We can not compare two B values in the same table
o But we can compare two beta values in the same table since one is standardized and one is unstandardized
o When we think about B – unstandardized, Beta = standardized
o Beta and B tell us information similar to what R tells us

Question 8

criterion

Answer 8

dependent

Question 9

Using Beta to test for third variables continued

Answer 9

95% Cis and statistical significance of Beta
- Your P value of .05 is complementing your .95 from CI
- When the P is greater than .05 the associated CI does include zero
- When the P value is less than .05 the associated CI does not include zero

Question 10

Higher beta, smaller beta

Answer 10

stronger relationship, weaker relationship

Question 11

Betas are

Answer 11

helping us answer our research questions and is a standardized coefficient that we can compare in one table – we can’t compare beta’s between two different tables

Question 12

regression in pop media articles

Answer 12

Controlled for
- Adjusting for
- Considering

Question 13

Regression does not establish causation

Answer 13

Multiple regression – is not foolproof way to rule out all kinds of third variables
- We can’t control variables we don’t measure
- There could be some variables we didn’t think about measuring

Question 14

Mediators versus third variables

Answer 14

Similarities
Both involve multivariate designs
Both can be detected using multiple regression

Differences
Third variables are external to the bivariate correlation (problematic)
Mediators are internal to the causal variable (not problematic).

Question 15

Mediators versus moderators

Answer 15

Mediator- Why?
Moderator- For whom and when does this relationship exist?

Question 16

Covariance

Answer 16

it’s about the results and differences between groups

Question 17

Control groups, treatment groups, and comparison groups

Answer 17

Control group- no treatment condition (not a control variable). This condition has no treatment and compare it to one with a treatment
Treatment group- One or more treatment conditions
Placebo group- control group exposed to a treatment that is inert

Question 18

Well-designed experiments establish internal validity

Answer 18

Design confounds- When a second variable varies systematically along with the independent variable and it gives us an alternative explanation for our results. Unable to support a causal claim. Only threatened if there is systematic variability which can’t be controlled
Ex: Students in the laptop group answer more difficult questions than the long-hand group. Can’t tell the cause because of a difference in the dependent variable

Selection effects- When the participants in one level of the IV are systematically different than the participants in another level of the IV
Random assignment avoids selection effects
Matched groups avoid selection effects. Ex: Matching IQ

Question 19

Independent-groups design (between-subjects designs)

Answer 19

Different groups of participants that are placed at different levels of your IV

Question 20

Posttest only

Answer 20

Equivalent groups. Simplest type of independent groups experiment where participants are randomly assigned to IV groups and are tested on your DV one time

Question 21

Pre-test Post-test design

Answer 21

Participants are randomly to at least two different groups and are tested on the key DV twice (before and after)

Question 22

Within-groups design

Answer 22

Each participant is presented with all levels of the IV

Repeated-measures design: Participants measured on the dependent variable more than one time after exposure to each level of the independent variable
Ex: Group tastes chocolate with confederate and say it tastes better than trying the same chocolate alone

Concurrent-measures Design- All participants are exposed to all levels of the independent variable are roughly the same time

Question 23

Advantages of within-groups design

Answer 23

Participants in your groups are equivalent because they are the same participants and serve as their own controls
Within-groups designs require fewer participants than other designs.

Question 24

Order effects:

Answer 24

When exposure to one level of the independent variable influences reactions to other levels of the independent variable. Applies to any design

Practice effect: When our participants are either getting better or worse at a task from practice or fatigue. (also known as fatigue effect)

Carryover effect: When one condition carries over to the next

Question 25

Avoiding order effects Two types of counterbalancing

Answer 25

Presenting the levels of independent variables in different orders Full counterbalancing: all possible condition orders are presented Partial counterbalancing; Only some of the possible condition orders are used

Question 26

What a mediating variable it is, what question your mediator asks?

Question 27

Think about regression analysis, or what it means when you add extra variables to your regression analysis.

Answer 27

all coefficients are changed

Question 28

Be able to understand what a matched group design is

Question 29

What are things you lok for in multiple regression analysis? What values?

Answer 29

Coefficient estimate for the regression. Tell us what the correlation coeffeicei This particular variables significantly predicted above and beyond all of the other variables there Or does not TYPES OF BETA VALUES: Standardized betas: compare the values in the tables compare values with all the IV. Cannot compare standardized betas with other standardized betas in other tables Two different tables Unstandardized betas: we know the relationship between the DV and IV but you can not compare these in a table. You can not compare those.

Question 30

Standardized betas

Answer 30

compare the values in the tables compare values with all the IV. Cannot compare standardized betas with other standardized betas in other tables Two different tables

Question 31

Unstandardized betas

Answer 31

we know the relationship between the DV and IV but you can not compare these in a table. You can not compare those. tell us if the IV is a significant predictor of the dependent variable

Question 32

What is a selection effect?

Answer 32

When the participant in an IV are systematically different than the participants in another level of the independent variable

Question 33

Avoiding selection effects with random assignment

Answer 33

there should be no systematic differences between the groups

Question 34

Avoiding selection effects with matched groups

Answer 34

take two people and match them on a particular characteristic and put them in two equal groups

Question 35

Interaction

Answer 35

the effect of one independent variable depends on the level of the other independent variable

Question 36

Crossover interaction

Answer 36

it depends, It depends on.

Question 37

Spreading interaction

Answer 37

“especially when” Deliciousness of sammie vs presence of bacon

Question 38

Factorial designs Study

Answer 38

Two or more IVs, two or more factors Cell: number of IVs will impact the number of cells we have, one the phone and not on phone, young drivers vs old drivers, 4 cells, 2 factors Participant variable: can’t change the variable. IV without being a true IV Ex: age Study either manipulated or participant variables

Question 39

Repeated measures design

Answer 39

One group à taste chocolate with confederateà rate chocolate -> taste chocolate alone à rate chocolate (confederate – someone apart of research study but participant doesn’t know that) Participants measured on the dependent variable more than one time after exposure to each level of the independent variable

Question 40

Concurrent measures design

Answer 40

o Where all of your participants are exposed to all levels of independent variable at the same time o One group – exposed to male and female faces and then dependent variable is who they looked at longest

Question 41

Advantages of within groups designs

Answer 41

Participants in your groups are equivalent because they are the same participants and serve as their own controls. 2. Within groups designs require fewer participants than other designs

Question 42

Internal validity: controlling for order effects

Answer 42

Exposure to one level of independent variable influences reactions to other levels of independent variables - When being exposed to one condition affects how participants respond to other conditions

Question 43

Practice effects

Answer 43

one type of order effect when our participants are either getting better at a task because they have practiced it or they are getting worse at a task because of fatigue (also known as fatigue effect)

Question 44

Carryover effect

Answer 44

Some sort of contamination carrying over from one condition to the next.

Question 45

Factorial designs study two independent variables

Answer 45

Cell Participant variable o Ex. On cell phone, not on cell phone + younger drives, older drivers à younger drives on cell phones, younger drives not on phones OR older drivers on cell phones, older drivers not on phones o We use our factorial designs to study independent (manipulated) variables or participant variables o Some variables you cannot manipulate – such as age – which then are considered participant variables

Question 46

Main effect

Answer 46

is there an overall difference? o When you are thinking about the main effect you think about the overall effect of one independent variable on your dependent variable, averaging other levels of the other independent variable overall effect of one of those IVs on the DV We compare our marginal means and if there is a significant difference between the marginal means there is a main effect

Question 47

Confidence interval that includes zero

Answer 47

not significant because it indicates no relationship

Question 48

2 by 2

Answer 48

means there are 2 independent variables but 2 levels of independent variables

Question 49

2 by 1

Answer 49

means we have 2 independent variables (there are two things on either side of the “by”) and 2 levels of the first independent and 1 level for the second independent variable

Question 50

3 by 3 by 3

Answer 50

3 independent variables and each has 3 levels

Question 51

2 by 3 by 4

Answer 51

3 independent variables one has level 2, one has level 3 and one has level 4 \

Question 52

ndependent – groups factorial designs

Answer 52

Both IVs are studied as independent groups - A 2 x2 independent groups factorial design has four groups or cells - This is known as a between subject’s design

Question 53

Interactions: Is there a difference in differences? Options 1 and 2

Answer 53

We need a p value; confidence interval corresponding to p value and get other information to be able to differentiate differences in tables - If it is statistically significant, we can identify that by the table

Question 54

Interactions: is there a different in differences? Line and bar graphs

Answer 54

When lines are parallel there is no interaction but when they cross it is statistically significant because they overlap - Interactions will either be a crossover interaction or spreading interaction - Interactions from bar graphs – connect the bars with a line to determine the interactions

Question 55

Interactions are more important than main effects

Answer 55

When a study shows both a main effect and an interaction, the interaction is almost always more important - There may be real differences in marginal means, but the more exciting part is the interaction

Question 56

Independent – groups factorial designs

Answer 56

Both IVs are studied as independent groups - A 2x2 independent groups factorial design has four groups or cells BLANK BY BLANK, how many variables, how many levels are in each Ex: cell phone 2 x 3 Do we increase main effects if we increase levels? NO # of main effects is not affected by levels but by variables How to figure out how many cells? Multiple the levels PUT SMALLER NUMBER IN THE FRONT

Question 57

2x2x2 : THREE WAY DESIGN

Answer 57

3 variables: with two levels each Two age conditions Two traffic conditions Two cell phone conditions the difference of differences of differences

Question 58

INcreasing the number of Levels of an Independent Variable

Answer 58

Blank X black Main effects and interactions from a three-way design Why worry about all these interactions? Adding the third one to

Question 59

Within-groups factorial design

Answer 59

All the people are participating in every level of the independent variable - Needs less participants than independent groups

Question 60

Mixed factorial designs

Answer 60

One IV is manipulated as independent-groups and the other is manipulated within-groups - This design is intermediate between the within groups design and the independent groups design in terms of number of participants

Question 61

MOST OUTCOMES IN PSYCHOLOGY STUDIES

Answer 61

ARE NOT MAIN EFFECTS BUT RATHER INTERACTIONS

Question 62

Adding more variables to congressional analysis?

Answer 62

TO account for as many third variables as possible all at once

Question 63

Indicate an experiment was done?

Answer 63

One of the variable is manipulated control group comparison group

Question 64

Going to the mall ask about positive attitudes with shopping?

Answer 64

Study, questionnaire no comparison group Convenience sampling Dependent came before iv

Question 65

Independent groups' design is known as what?

Answer 65

Between-subjects design: REQUIRES MORE PARTICIPANTS NEED DIFFERENT PEOPLE FOR EACH LEVEL OF IV Everyone experiences different conditions

Question 66

COMPARED TO WITHIN GROUPS

Answer 66

Same groups experiences all the same levels of the IV

Question 67

CONCURRENT MEASURES DESIGN

Answer 67

Participant is experiencing all levels at the same time Monkey’s studies HArry Harlow’s Baby monkey choose wire monkey or cloth monkey Monkey’s preference COKE VS PEPSI

Question 68

MEDIATING VARIABLE

Answer 68

Telling you why this variable impacts those other variable why IV impacts DV Why the relationship exists

Question 69

I don't know about this

Answer 69

YOu have variable A and the relationship between C above A and C you have variable B. B needs to have an arrow to A and C. B is the mediator.

Question 70

Think about regression analysis, or what it means when you add extra variables to your regression analysis.

Answer 70

changes the size or sign of the coefficients

Question 71

Within groups study between group studies which one of those would be affected by practice effects

Answer 71

WITHIN GROUPS WOULD BE EFFECTED, WHEN you have the same participants who are experiencing all levels of of what they or you are asking they may get tired. Which cold influence your results

Question 72

Repeated measures design

Answer 72

a type of within groups design where your participants are measured on DV more than one time and how to identify it

Question 73

FIgure out how much the number of cells for your factorial design

Answer 73

BETWEEN GROUP 16 people per cell and there are 8 cells 2x4 = 8 cells How many IVs and how many DVs

Question 74

What is the difference between the interaction and the main effect?

Answer 74

Main effects are looking at the differences between 1 of IV Interactions looks at the difference of differences in the IVs and how they interact Tells up about moderating variable

Question 75

How can we determine the number of main effects you are looking for in a factorial analysis?

Answer 75

Looking at the number of IVs, if we have 4 IVs how many effects do we have 4.

Question 76

Can we have a situation where in a In our factorial analysis where our Main effect is not significant but our interaction is significant

Answer 76

MAIN EFFECT IS NOT SIGNIFICANT AND INTERACTION CAN BE Crossover interactions yes There is a relation between the IV and 1 of the DV

Question 77

Popular media would talk about a multiple regression finding

Answer 77

be able to identify multiple regression in a popular media sources, depends…. accounting for….. Even when we control for or even when we think about, our variable of interest still impacts In light of, controlling for Controlling for, accounting for, in light of

Question 78

When you are conducting a study and your participants kind of suspect the purpose of the study, they act upon that and they give you what you are looking for. What is this called?

Answer 78

demand characteristic