chapter 10 Flashcards
(21 cards)
1
Q
key concepts of a complex design
A
- independent variable
- dependent variable
- experimental hypothesis
- manipulation check
- experimental control
2
Q
manipulation check
A
- what does the study’s manipulation
check tell us? - what if the manipulation check
doesn’t show a significant difference?
3
Q
factorial designs
A
- two or more independent variables-factors that are investigated simultaneously
- each level of one independent
variable is combined with each level of the others to produce all possible combinations with each combination becoming a condition in the experiment - one dependent variable
- allow us to investigate the main effects and interaction/combined effects
4
Q
increasing the levels of the independent variable
A
- provides more information about the relationship between the IV and the DV
- allows you to check for curvilinearity
5
Q
multiple variables
A
- measure more constructs
- learn more
- more realistic
- manipulation checks
- multiple measures of the same construct
- converging operations
6
Q
basic 2x2 factorial design
A
- a 2 × 2 factorial
design combines two variables, each of which has two levels - e.g. cell
phone use (yes vs. no) and time of day (day vs. night) on driving ability; four cells represent the four possible combinations or conditions: using a cell phone during the day, not using a cell phone during the day, using a cell phone at night, and
not using a cell phone at night
7
Q
3x2 factorial design
A
- if one of the independent variables had a third level it would be a 3 × 2 factorial design, and there would be six distinct conditions
- e.g. using a handheld cell phone, using a hands-free cell
phone, and not using a cell phone
8
Q
4x5 factorial design
A
20 conditions
9
Q
2x2x3
A
- run the 2x3 study twice
- doubles number of subjects needed
10
Q
2x2x2x3
A
- run a 2x2x3 study twice
- add variables = more complicated study
- e.g. depicts 4 variables, 24 groups
11
Q
incomplete factorial design
A
- can be a bad or good incomplete factorial design
- a bad complete factorial design is the result of not getting enough subjects which means you can’t achieve full array of data
- used if you have no treatment control group
12
Q
between subjects factorial design
A
- all of the independent variables are manipulated between subjects
- e.g. all participants could be tested either while using a cell phone or while not using a
cell phone and either during the day or during the night; this would mean that each participant would be
tested in one and only one condition - conceptually simpler
- avoids carry-over effects and minimizes the time and effort of participants
13
Q
within-subjects factorial design
A
- all of the independent variables are manipulated within subjects
- e.g. all participants could be tested both while using a cell phone and while not using a cell phone and both during the day and during the night; this would mean that each participant would need to be tested in all four conditions
- efficient for researchers and controls extraneous variables
14
Q
mixed factorial design
A
- manipulate one independent variable between subjects and another within subjects
- e.g. a researcher might choose to treat cell phone use as a within-subjects factor by testing the same participants both while using a cell phone and while not using a cell phone and they might choose to treat time of day as a between-subjects factor by testing each participant either during the day or during the night
- participants could be tested on 2/4 conditions
15
Q
non-manipulated independent variable
A
- the researcher measures it but does not manipulate it
- non- manipulated independent variables are usually participant variables (self-esteem, gender, etc)
- generally considered to be experiments as long as at least one independent variable is manipulated
- causal conclusions can only be
drawn about the manipulated independent variable
16
Q
non-experimental studies with factorial designs
A
- include only non-manipulated independent variables
- e.g. a researcher simply measures both the moods and the self-esteem of several participants—categorizing them as having either a positive
or negative mood and as being either high or low in self-esteem—along with their willingness to have
unprotected sexual intercourse. This can be conceptualized as a 2 × 2 factorial design with mood (positive vs. negative) and self-esteem (high vs. low) as non-manipulated between-subjects factors
17
Q
main effect
A
- the effect of one independent variable on the dependent variable
- averaging across the levels of the other independent variable
- normally 1 per independent variable
18
Q
interaction effect
A
when the effect of one independent variable depends on the level of another
19
Q
spreading interactions
A
- there is an effect of
one independent variable at one level of the other independent variable and there is either a weak effect or no effect of that independent variable at the other level of the other independent variable - e.g. independent variable “B” has an effect at level 1 of independent variable “A” but no effect at
level 2 of independent variable “A”
20
Q
cross-over interaction
A
- independent variable “B” again has an effect at both levels of independent variable “A,” but the effects are in opposite directions
- e.g. introverts perform better than extraverts when they have not ingested any caffeine. But extraverts perform better than introverts when they have ingested 4 mg of caffeine per kilogram of body weight
21
Q
simple effects
A
- a way of breaking down the interaction to figure out precisely what is going on
- simple effects analysis allows researchers to determine the effects of each independent variable at each level of the other independent variable
