Lecture 5 Flashcards
In a non-experimental study do we have an IV or DV?
in a non-experimental study we don’t have IV or DV because we are observing both
What are the 3 steps for establishing causality?
- Correlation, or relationship, between IV and DV
(this means if we have a 2 conditions study, that means that those conditions have different levels of the DV because the IV is changing it.) - Temporal precedence
- Causes must precede effects § IV precedes DV
(we manipulate the variable first and then observe the effects of it after) - Eliminate alternative explanations, or confounds § (similar to third variables in non-experimental designs)
- IV is the only variable impacting DV
(third variables is for the non-experimental method and we use the word confound for experimental designs)
What is a confound?
Confound: a variable that researcher manipulates that is not directly related to the hypothesis
- Think ‘accidental IV’
(its not something they wanted to manipulate but it is changing and systematically)
What are the 2 rules of confounds?
- Confounds must covary/correlate with IV (the confound will usually be present at one level of the IV and not at the other level of the IV)
- Confounds must cause change in DV (Ths is because if it doesnt impact the DV it cant be responsible for the change)
Provide 2 confound examples with a hypothsesis, method, and IV and DV
Hypothesis: “Listening to relaxing music improves test performance”
Method: Randomly assign participants to either wear headphones playing relaxing music or a control condition
IV: Relaxing music vs. control
DV: Test performance
n=100
- Relaxing music (headphones)
- high test performance
- no music
- Low test performance
the presence of the headphones is a confound. There is now uncertainty about which of these causal flows is correct.
There’s another possible confound. The expectation of the participants. If you are doing a study, the researchers give you headphones and you are listening to music that makes sense, but if nothing is playing you might be confused which could also impact your performance
What threatens internal validity? Example?
What is helpful to internal validity?
Foe:
- Confound: Manipulated variable that is unrelated to the hypothesis
- Example: Experiment testing relaxing music and test performance § Intended IV: music vs. no music
- Accidental IVs:
- headphones vs. no headphones
- ambient noise vs. no ambient noise
(have we correctly identified the causal variable? if we are not confident that it is the music and no t the headphones impacting the DV, we have low internal validity. )
Foe:
Confound: Participant variable – preexisting individual differences
Intended IV: relaxing music vs. no music
Accidental IV: Participant music preferences
Friend:
- Experimental Control: Only IV changes/varies
- Everyone wears headphones
- Everyone hears no ambient noise
(if you replicate the same effect with a variety of different confounds, then we can be more confident that the IV is impacting DV and not any one of the compounds. )
Friends:
- Random assignment: Eliminate participant variable confound
- Assigning conditions usually equates participant variables
- Matched pairs design: Pretest known or suspected confound
- “Do you LIKE or HATE listening to relaxing music?”
- Pair up, or match, participants
(you pair 2 people with the same preferences and you give them each different conditons. Therefore, you have for sure controlled that pethis could not possibly be a confound )
What is the question related to internal validity?
is the cause valid?
How could you change the method in the music listening study to avoid the confound of headphones?
Method: Headphone for all, participants choose whether to listen to music or not
When is the failure of random assignment higher?
the failure of random assignment in a normal experimental study is higher in smaller sample groups so you should replicae to ensure it hasnt failed.
Why is it risky to let people pick their own conditions? Give example from music experiment relaxing music and no music
the confound is present because you allowed people to select their own conditions, if you like relaxing music, then you will select that condition and vice versa. Also, your music preference also probably indicates other things about you, and you could have other factors that are common to music likers that makes them score higher.
Provide a confound example for an experiment testing if caffeine increases athletic performance.
200 high-performance track runners completed 4 different races and were timed for each. The runners were either assigned to the 100mg caffeine condition or the control condition.
During the first 2 races, each of the runners were timed to make sure there were no differences between the caffeine group and control group. Before the last 2 races, the caffeine group was given a small pill containing 100mg of caffeine.
Runners in the caffeine group ran 3.85 seconds faster on average than did runners in the control group.
could be a placebo affect because we gave one group a pill that is percieved to make a difference and the other group didn’t recieve a pill. This could lead to a placebo. By being given a pill you might think its going to do something which creates pre-existing ideas on how you will perform which could impact it. If they saw the other people get pills, the control group might think they are going to lose.
Provide a confound example testing the question Does the “SuperSmarty” program enhance reading comprehension?
50 elementary school students enrolled in the study. First, students all took an exam to measure their baseline scores on reading comprehension. Next students completed 3 weeks of the “SuperSmarty Program!”
§ After completing the program, students took another exam designed to measure reading comprehension. Results showed that students scored 10 points higher after they completed the “SuperSmarty Program!”
the name of the program creates expectations. There’s also no control group so they are all learning which already makes it more likely that they will score higher.
How do you control participant variables?
- Randomly assign Ss to ONE condition: Between-subjects design
- Pair Ss based on pretest: Matched pairs design (this isn’t necessarily only 2 peopel it is just the amount of conditions you have)
- Ss experience ALL conditions: Within-subjects design (on some days the runners would take the caffeine pill. On other days they would take the placebo control pill)
What are the 3 possible types of between subjects designs?
- Independent groups
- Between-groups
- Posttest-only
What are the 2 possib le types of the matched pairs design?
- Dependent samples
- Pretest-Posttest
