Test Four

Question 1

What is a factorial design?

Answer 1

A design with more than one level of independent variable.

Question 2

In factorial designs, what are factors and categories?

Answer 2

Factors are what each level of the IV is called, categories are a combination of levels.

Question 3

How do you write factorial designs?

Answer 3

The number of integers is the number of IVs, and their value is the number of factors. (ex. 2 x 3 design)

Question 4

What’s the purpose of factorial designs?

Answer 4

Testing more levels of IV make it more like real life–to test the generalizability of a finding.

Question 5

What can factorial designs reveal? (2)

Answer 5

Main effects and interactions

Question 6

What types of interactions are there? (2)

Answer 6

Crossover and spreading

Question 7

What are the two types of variables in factorial designs?

Answer 7

Manipulated and participant variables

Question 8

What do interactions show?

Answer 8

Interactions show moderating variables

Question 9

What are the two types of groups p’s can be assigned to in factorial designs?`

Answer 9

Independent (between subjects) and correlated (within subjects)

Question 10

How many times are p’s treated in independent groups? In corrlated groups?

Answer 10

1 in independent, >1 in correlated

Question 11

What is mixed assignment?

Answer 11

Some p’s are put in an independent group, some are put in a correlated group

Question 12

How does a quasi-experiment differ from a regular experiment? (3)

Answer 12

1) Nothing is manipulated
2) IV varied before researcher arrived
3) No random assignment

Question 13

Can IVs be repeated in quasi-experiments?

Answer 13

Yes, but not at the will of the researcher

Question 14

What threats are there to internal validity? (7)

Answer 14

Selection, maturation, history, regression to the mean, attrition, testing, and instrumentation

Question 15

How can selection threats be avoided in quasi-experimental designs? (2)

Answer 15

Repeated measures and better comparison groups

Question 16

How can maturation threats be avoided in QEDs?

Answer 16

Better comparison groups

Question 17

How can testing threats be avoided in QEDs?

Answer 17

Good comparison groups

Question 18

How can instrumentation threats be avoided in QEDs? (2)

Answer 18

Good comparison groups and interrupted time series

Question 19

What are threats related to human subjectivity that could be present int QEDs? (3)

Answer 19

Observer bias, placebo effect, and experimental demand

Question 20

As stated in the power points, what are two other validities that are critical for QEDs? (2)

Answer 20

Construct validity and statistical validity

Question 21

What are three priorities you should have in mind when conducting QEDs?

Answer 21

Opportunity, External validity, and Ethics

Question 22

How do small-N designs differ from large-N designs? (3)

Answer 22

1) Each p represents their own experiment; usually invokes repeated measures
2) individuals’ data presented
3) replicability determined by repeating test in another subject

Question 23

When would one use small-N designs? (3)

Answer 23

1) special or small populations
2) research is exhausting in terms of resources
3) when single negative response would refute a theory

Question 24

What are three designs that are employed for small-N designs?

Answer 24

Stable-baseline, repeated-baseline, and reversal

Question 25

How does one conduct a stable-baseline design?

Answer 25

Multiple baseline measurements -> intervention -> more measurements

Question 26

How does one conduct a repeated-baseline design?

Answer 26

Baseline measure -> Intervention -> more measurements -> new component added to baseline -> intervention -> more measurements, etc.

Question 27

How does one conduct a reversal design?

Answer 27

Baseline measurement -> intervention -> more measurements -> removal of treatment -> more measurements

Question 28

What are ethical considerations of reversal designs? (2)

Answer 28

1) Is it ethical to remove the treatment? | 2) Is it ethical to use a treatment that isn't empirically proven to work?

Question 29

How is external validity expressed in small-N designs? (4)

Answer 29

1) Replicate on other similar patients 2) study in animals 3) use other techniques in larger pops 4) No expectation results will generalize to everyone

Question 30

How can construct validity be measured in small-N designs?

Answer 30

Interrater reliability

Question 31

How important is statistical validity in small-N designs?

Answer 31

Not as much

Question 32

How can statistical validity be expressed in small-N designs? (2)

Answer 32

1) Effect size is measured per case | 2) Graphs provide quantitative data

Question 33

What must a study do to be important?

Answer 33

Be replicable

Question 34

What are the three types of replication studies?

Answer 34

Direct replication, conceptual replication, and replication with extension

Question 35

What is direct replication?

Answer 35

An exact replication of the study

Question 36

What is conceptual replication?

Answer 36

A study that uses the same variables and studies the same questions, but those variables are operationalized differently

Question 37

What is a replication with extension?

Answer 37

A mix of direct and conceptual replication

Question 38

What are two variables that can be changed for replicability's purposes?

Answer 38

Participant and situational variables

Question 39

What is a meta-analysis?

Answer 39

An article that summarizes and reviews previously conducted studies.

Question 40

What are important features of meta-analyses? (3)

Answer 40

1) Collect all possible examples of a particular kind of study 2) Average of all effect sizes 3) can sort groups into categories to see how effect sizes differ

Question 41

What's a problem with meta-analyses?

Answer 41

Findings that have been published may not reveal the entire picture.

Question 42

Can you generalize to other participants?

Answer 42

Yes, because they're A population, but it depends on HOW they were obtained.

Question 43

Can you generalize to other settings?

Answer 43

Yes, and conceptual replications can provide proof for this.

Question 44

What is theory-testing mode?

Answer 44

Testing association or causal claims to test a theory

Question 45

What is generalization mode?

Answer 45

Goal of generalizing results from a sample to a population.

Question 46

What are some results on the study of the Muller-Lyer Illusion?

Answer 46

Ps that grew up in a "carpentered society" were more likely to fall for the illusion.

Question 47

What types of subjects are usually tested in psychology?

Answer 47

WEIRD subjects? (Western, Educated, Industrialized, Rich, and Democratic)