Experimental Designs Psych 231: Research Methods in Psychology.
Experimental Control Psych 231: Research Methods in Psychology.
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Transcript of Experimental Control Psych 231: Research Methods in Psychology.
Colors and words Divide into two groups:
left side of room right side of room
Instructions: Read aloud the COLOR that the words are presented in. When done raise your hand.
Left side first. Right side people please close your eyes.
Okay ready?
Okay, now it is the right side’s turn.
Remember the instructions: Read aloud the COLOR that the words are presented in. When done raise your hand.
Okay ready?
Our results So why the difference between the results for the people on the right side of the room versus the left side of the room?
Is this support for a theory that proposes: “good color identifiers usually sit on the left side of a room”
Why or why not? Let’s look at the two lists.
BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen
List 2Right side
BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen
List 1
Left side
Matched
Mis-Matched
What resulted in the perfomance difference? Our manipulated independent variable
The other variable match/mis-match?
Because the two variables are perfectly correlated we can’t tell
This is the problem with confounds
BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen
BlueGreenRed
PurpleYellowGreenPurpleBlueRed
YellowBlueRed
Green
Experimental Control Our goal:
To test the possibility of a relationship between the variability in our IV and how that affects the variability of our DV. • Control is used to minimize excessive variability.
• To reduce the potential of confounds.
Sources of variability (noise)
Nonrandom (NR) Variability - systematic variation
A. (NRexp) manipulated independent variables (IV)
i. our hypothesis is that changes in the IV will result in changes in the DV
Sources of Total (T) Variability:
T = NRexp + NRother + R
Sources of variability (noise)
Nonrandom (NR) Variability - systematic variation
B. (NRother) extraneous variables (EV) which covary with IV
i.other variables that also vary along with the changes in the IV, which may in turn influence changes in the DV (Condfounds)
Sources of Total (T) Variability:
T = NRexp + NRother + R
Sources of variability (noise)
Non-systematic variationC. Random (R) Variability
• Imprecision in manipulation (IV) and/or measurement (DV) • Randomly varying extraneous variables (EV)
Sources of Total (T) Variability:
T = NRexp + NRother + R
Sources of variability (noise) Sources of Total (T) Variability:
T = NRexp + NRother + R
Goal: to reduce R and NRother so that we can detect NRexp.
That is, so we can see the changes in the DV that are due to the changes in the independent variable(s).
Weight analogy Imagine the different sources of variability as weights
RNRexp
NRother
RNRother
Treatment group
control group
The effect of the treatment
Weight analogy If NRother and R are large relative to NRexp then detecting a difference may be difficult
RNRexp
NRother
RNRother
Difference Detector
Weight analogy But if we reduce the size of NRother and R relative to NRexp then detecting gets easier
RNRother
RNRexpNR
other
Difference Detector
Things making detection difficult
Potential Problems Excessive random variability Confounding Dissimulation
Potential Problems Excessive random variability
If control procedures are not applied• then R component of data will be excessively large, and may make NR undetectable
So try to minimize this by using good measures of DV, good manipulations of IV, etc.
Excessive random variability
RNRexp
NRother
NRother
R
Hard to detect the effect of NRexp
Difference Detector
Potential Problems
Confounding If relevant EV co-varies with IV, then NR component of data will be "significantly" large, and may lead to misattribution of effect to IV
IVDV
EV
Co-vary together
Confounding
RNRexp
NRother
Hard to detect the effect of NRexp because the effect looks like it could be from NRexp but is really (mostly) due to the NRother
R
Difference Detector
Potential Problems Potential problem caused by experimental control Dissimulation
• If EV which interacts with IV is held constant, then effect of IV is known only for that level of EV, and may lead to overgeneralization of IV effect
This is a potential problem that affects the external validity
Controlling Variability
Methods of Experimental Control Comparison Production Constancy/Randomization
Methods of Controlling Variability Comparison
An experiment always makes a comparison, so it must have at least two groups• Sometimes there are control groups
• This is typically the absence of the treatment• Without control groups if is harder to see what is really happening in the experiment
• It is easier to be swayed by plausibility or inappropriate comparisons
• Sometimes there are just a range of values of the IV
Methods of Controlling Variability Production
The experimenter selects the specific values of the Independent Variables • Need to do this carefully
• Suppose that you don’t find a difference in the DV across your different groups
• Is this because the IV and DV aren’t related?• Or is it because your levels of IV weren’t different enough
Methods of Controlling Variability Constancy/Randomization
If there is a variable that may be related to the DV that you can’t (or don’t want to) manipulate• Control variable: hold it constant• Random variable: let it vary randomly across all of the experimental conditions
But beware confounds, variables that are related to both the IV and DV but aren’t controlled
Experimental designs So far we’ve covered a lot of the about details experiments generally
Now let’s consider some specific experimental designs. Some bad designs Some good designs
• 1 Factor, two levels• 1 Factor, multi-levels• Factorial (more than 1 factor)• Between & within factors
Poorly designed experiments
Example: Does standing close to somebody cause them to move? So you stand closely to people and see how long before they move
Problem: no control group to establish the comparison group (this design is sometimes called “one-shot case study design”)
Poorly designed experiments Does a relaxation program decrease the urge to smoke? One group pretest-posttest design Pretest desire level – give relaxation program – posttest desire to smoke
Poorly designed experiments
One group pretest-posttest design
Problems include: history, maturation, testing, instrument decay, statistical regression, and more
participantsPre-test Training group
Post-testMeasure
Independent Variable
Dependent Variable
Dependent Variable
Poorly designed experiments Example: Smoking example again, but with two groups. The subjects get to choose which group (relaxation or no program) to be in
Non-equivalent control groups Problem: selection bias for the two groups, need to do random assignment to groups
Poorly designed experiments
Non-equivalent control groups
participants
Traininggroup
No training (Control) group
Measure
Measure
Self Assignment
Independent Variable
Dependent Variable
“Well designed” experiments
Post-test only designs
participants
Experimentalgroup
Controlgroup
Measure
Measure
Random Assignment
Independent Variable
Dependent Variable