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General Psych > final quiz rm ch 9, 10, and 12 > Flashcards

final quiz rm ch 9, 10, and 12 Flashcards

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1
Q

Reviewing three Casual Claims

A

Covariance
Temporal Precedence
Internal Validity

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2
Q

multivariate designs

A

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

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3
Q

Longitudinal designs

A

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

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4
Q

cross sectional

A

two variables measured at the same point in time are correlated

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5
Q

cross-lag correlations

A

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

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6
Q

Regression results indicate whether a third variable explains the relationship

A

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

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7
Q

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

A

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

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8
Q

criterion

A

dependent

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9
Q

Using Beta to test for third variables continued

A

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

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10
Q

Higher beta, smaller beta

A

stronger relationship, weaker relationship

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11
Q

Betas are

A

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

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12
Q

regression in pop media articles

A

Controlled for
- Adjusting for
- Considering

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13
Q

Regression does not establish causation

A

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

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14
Q

Mediators versus third variables

A

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).

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15
Q

Mediators versus moderators

A

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

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16
Q

Covariance

A

it’s about the results and differences between groups

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17
Q

Control groups, treatment groups, and comparison groups

A

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

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18
Q

Well-designed experiments establish internal validity

A

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

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19
Q

Independent-groups design (between-subjects designs)

A

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

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20
Q

Posttest only

A

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

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21
Q

Pre-test Post-test design

A

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

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22
Q

Within-groups design

A

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

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23
Q

Advantages of within-groups design

A

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.

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24
Q

Order effects:

A

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

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25
Q

Avoiding order effects
Two types of counterbalancing

A

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

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26
Q

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

A
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27
Q

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

A

all coefficients are changed

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28
Q

Be able to understand what a matched group design is

A
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29
Q

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

A

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.

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30
Q

Standardized betas

A

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

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31
Q

Unstandardized betas

A

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

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32
Q

What is a selection effect?

A

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

33
Q

Avoiding selection effects with random assignment

A

there should be no systematic differences between the groups

34
Q

Avoiding selection effects with matched groups

A

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

35
Q

Interaction

A

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

36
Q

Crossover interaction

A

it depends, It depends on.

37
Q

Spreading interaction

A

“especially when”
Deliciousness of sammie vs presence of bacon

38
Q

Factorial designs Study

A

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

39
Q

Repeated measures design

A

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

40
Q

Concurrent measures design

A

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

41
Q

Advantages of within groups designs

A

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

42
Q

Internal validity: controlling for order effects

A

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

43
Q

Practice effects

A

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)

44
Q

Carryover effect

A

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

45
Q

Factorial designs study two independent variables

A

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

46
Q

Main effect

A

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

47
Q

Confidence interval that includes zero

A

not significant because it indicates no relationship

48
Q

2 by 2

A

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

49
Q

2 by 1

A

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

50
Q

3 by 3 by 3

A

3 independent variables and each has 3 levels

51
Q

2 by 3 by 4

A

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

52
Q

ndependent – groups factorial designs

A

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

53
Q

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

A

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

54
Q

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

A

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

55
Q

Interactions are more important than main effects

A

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

56
Q

Independent – groups factorial designs

A

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

57
Q

2x2x2 : THREE WAY DESIGN

A

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

58
Q

INcreasing the number of Levels of an Independent Variable

A

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

59
Q

Within-groups factorial design

A

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

60
Q

Mixed factorial designs

A

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

61
Q

MOST OUTCOMES IN PSYCHOLOGY STUDIES

A

ARE NOT MAIN EFFECTS BUT RATHER INTERACTIONS

62
Q

Adding more variables to congressional analysis?

A

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

63
Q

Indicate an experiment was done?

A

One of the variable is manipulated control group comparison group

64
Q

Going to the mall ask about positive attitudes with shopping?

A

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

65
Q

Independent groups’ design is known as what?

A

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

66
Q

COMPARED TO WITHIN GROUPS

A

Same groups experiences all the same levels of the IV

67
Q

CONCURRENT MEASURES DESIGN

A

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

68
Q

MEDIATING VARIABLE

A

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

69
Q

I don’t know about this

A

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.

70
Q

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

A

changes the size or sign of the coefficients

71
Q

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

A

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

72
Q

Repeated measures design

A

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

73
Q

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

A

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

74
Q

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

A

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

75
Q

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

A

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

76
Q

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

A

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

77
Q

Popular media would talk about a multiple regression finding

A

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

78
Q

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?

A

demand characteristic

General Psych (8 decks)

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