Experimental Control Psych 231: Research Methods in Psychology.
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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?
Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green List 1
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?
Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green List 2
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.
Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green List 2 Right side Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green List 1 Left side MatchedMis-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 Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red 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. (NR exp ) 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 = NR exp + NR other + R
Sources of variability (noise) Nonrandom (NR) Variability - systematic variation B. (NR other ) 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 = NR exp + NR other + R
Sources of variability (noise) Non-systematic variation C. Random (R) Variability Imprecision in manipulation (IV) and/or measurement (DV) Randomly varying extraneous variables (EV) Sources of Total (T) Variability: T = NR exp + NR other + R
Sources of variability (noise) Sources of Total (T) Variability: T = NR exp + NR other + R Goal: to reduce R and NR other so that we can detect NR exp. 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 R NR exp NR other R NR other Treatment groupcontrol group The effect of the treatment
Weight analogy If NR other and R are large relative to NR exp then detecting a difference may be difficult R NR exp NR other R NR other Difference Detector
Weight analogy But if we reduce the size of NR other and R relative to NR exp then detecting gets easier R NR other R NR exp NR 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 R NR exp NR other NR other R Hard to detect the effect of NR exp 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 IV DV EV Co-vary together
Confounding R NR exp NR other Hard to detect the effect of NR exp because the effect looks like it could be from NR exp but is really (mostly) due to the NR other 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-test Measure Independent 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 Training group No training (Control) group Measure Self Assignment Independent Variable Dependent Variable
“Well designed” experiments Post-test only designs participants Experimental group Control group Measure Random Assignment Independent Variable Dependent Variable
“Well designed” experiments Pretest-posttest design participants Experimental group Control group Measure Random Assignment Independent Variable Dependent Variable Measure Dependent Variable