-
Compare multilple groups by using a ___-___ design
multiple-group
-
More than two ___ ___ are needed to accurately compare multiple effects
data points
-
We need to see the full shape of the graph in order to determine the ___ ___
Functional Relationship
-
A manipulation's construct validity is weakened by ___ variables
Confounding
-
A group that recieves no treatment - not even a placebo
empty control group
-
a treatment that has no effect
placebo
-
When group means are further apart, there is greater ___
variability
-
ANOVA
Analysis of Variance
-
Allows us to compare between-groups variance to with-groups variance
ANOVA
-
the ratio of the between-groups variance to the within-groups variance
F ratio
-
Our environment varies in ___ and to ___ ___ a variable is present
whether and to what degree
-
level of a variable
data point
-
Finding the functional relationship requires ___ more than 2 data points
mapping
-
To make accurate statements regarding effects we need to know the ___ ___ of the IVs and DVs
functional relationship
-
Multilevel experiments means greater ___ and ___ due to ___ ___
external and construct
random selection
-
variables are ___ confounded
frequency
-
___ ___ experiments are stronger than the simple experiment
multiple group
-
in Multiple Group experiments, ___ validity is improved and we are able to map ___ relationships
construct
functional
-
Determine the ___ we will use BEFORE the type of Design
analysis
-
Type of analysis used will influence (3)
treatments
# of participants
hypothesis
-
To find a treatment effect, we look for ___
variability
-
difference among group means
variability
-
A ___ difference between means is most likely due to chance
small
-
A ___ difference between means is likely due to the effect of the treatment
large
-
Within-group variability is NOT due to ___
Treatment
-
Within-group variability is due to ___ ___
Random Error
-
Between-groups variability is due to ___ and ___ ___
Treatment and Random Error
-
the effects of random error
within-group variability
-
the effects of random error plus any treatment effects
between-group variability
-
Effects of random error (3)
individual differences
unreliability of the measure
poor standardization
-
tells us the extent to which chance caused individual scores to differ from each other
within-groups variability
-
tells us the degree to which our groups actually vary from one another
between-groups variability
-
Two factors affect the extent to which group means differ
random error
treatment effect
-
In an ___, we see any possible effects of individual IVs and ALSO any interaction effects between the variables
ANOVA
-
We can ask more refined questions in a ___ ___ experiment
multiple group
-
factors other than the treatment
extraneous factors
-
We can eliminate extraneous factors by using a ___-___ or the ___/___ design
two-group
pretest/posttest
-
If groups differ before the treatment, differences in scores could be due to ___ ___ which threatens ___ validity
selection bias
internal
-
By using ___, groups should have nearly identical characteristics
matching
-
groups are the same on the pretest, but develop at different rates
selection by maturation interaction
-
Average scores tend to become less extreme at the ___
posttest
-
tells us that extreme scores will revert back to more normal levels on retest
regression toward the mean
-
Groups may also end up being different due to ___ - when participants drop out of a study
mortality
-
use the same participants in the no-treatment group and the treatment group. This approach uses the ___-___ design
pretest/posttest
-
Participants may change because of ___. A change in a participant's environment has a ___ effect on their scores.
History
Systematic
-
when participants become better at taking our test due to practice resulting in ___ ___
testing effects
-
changes in the measuring scale resulting in changes in scores
instrumentation
-
Any change in the cause must be reflected by change in the ___
effect (outcome)
-
Differences in the ___ variable must be reflected by a change in the ___ variable
independent/ dependent
-
changes in the ___ occured before changes in the ___
treatment / activity
-
Experimental design where everything is identical for both treatment groups, except that one group recieves the treatment
Group scores are compared
two-group design
-
8 Threats to internal validity
- groups were different to begin with
Selection
-
8 Threats to internal validity
- groups were destined to grow in different ways
selection by maturation interaction
-
8 Threats to internal validity
- extreme pretest scores tend to normalize
regression effects
-
8 Threats to internal validity
- participants drop out
mortality
-
8 Threats to internal validity
- natural growth and development appear at treatment effects
maturation
-
8 Threats to internal validity
- changes in environment caused changes in participants
history
-
8 Threats to internal validity
- practice on the pretest caused changes in posttest scores
Testing
-
8 Threats to internal validity
- the questionnaire changed between pre- and posttest
Instrumentation
-
Arbitrary assignment to groups and when participants choose in which group they want to be.
These individuals are different!!!
selection bias
-
Measurements are only as accurate as the ___
tool we are using
-
Using unreliable tools produces ___ ___
random error
-
Group's averages tend to become less extreme during the ___
posttest
-
scores that are extreme will revert back to more normal levels at the retest
regression toward the mean
-
Design used to determine: Why people behave the way they do and How we can help them behave differently
Simple Experiment
-
Design used to isolate underlying causes:
Requires internal validity (cause and effect)
Simple Experiment
-
In a simple experiment, groups must NOT differ ___.
One group will receive treatment and the other will not.
Systematically
-
In a simple experiment, treatments can involve different ___ and ___ of activity
types / amounts
-
To ensure that the treatment is the only difference between groups of a simple experiment, we use ___ ___
random assignment
-
In a simple experiment, we test the experimental hypothesis against the ___ ___
null hypothesis
-
Hypothesis that states that the treatment WILL HAVE an effect
experimental hypothesis
-
Larger groups tend to be more ___, and effects will stand out better
similar
-
we need to know the scores of our Dependent variable to be able to calculate any ___ ___ of our Independent variable
significant effects
-
If a difference is ___, we can be certain, beyond a reasonable doubt, that the difference is NOT due to ___
Significant / Random Error
-
With a ___ ___ error, we state that a difference between our groups was Significant when it was NOT
Type 1
-
With a ___ ___ error, we state that a difference between our groups was NOT significant, but due to Chance, when it actually WAS significant
Type 2
-
To avoid Type 2 errors
Do Not set the significance level Too LOW
-
We need enough ___ to detect true Differences
power
-
Participants who are similar
homogenous
-
___ limits random error from disguising our treatment effect
power
-
Power -
Increase the ___ of our treatment effect
size
-
We use the ___ ___ to see whether the difference between Mean group Averages is big enough NOT to be due to random error.
t-test
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