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?

BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen

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?

BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen

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.

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

“Well designed” experiments

Pretest-posttest design

participants

Experimentalgroup

Controlgroup

Measure

Measure

Random Assignment

Independent Variable

Dependent Variable

Measure

Measure

Dependent Variable