Experimental Designs

Psych 231: Research Methods in Psychology

Announcements

Exam 2 coming up: Monday Oct 27th Review session Thursday 6:30 DeGarmo 463

Piloting experiments in lab this week

Experimental Designs

1 Factor - two levels Advantages:

• Simple, relatively easy to interpret the results, good first step, sometimes all you need

Disadvantages:• “True” shape of the function is hard to see

1 Factor - more than two levels Advantages

• Better picture of the function• Less worry about your range of the independent variable

Disadvantages• Needs more resources (participants and/or stimuli)• Requires more complex statistical analysis (analysis of variance

and pair-wise comparisons)

Factorial experiments

Factorial Designs: Two or more factors Advantages

• Interaction effects

• Adding factors decreases the variability• Because you’re controlling more of the variables that influence the

dependent variable• This increases the statistical Power of the statistical tests

• Increases generalizability of the results• Because you have a situation closer to the real world (where all sorts

of variables are interacting)

Disadvantages• Experiments become very large, and can become unwieldy

• The statistical analyses get much more complex• Interpretation of the results can get hard• Need more resources

Example

What is the effect of presenting words in color on memory for those words?

Two different designs to examine this question

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So you present lists of words for recall either in color or in black-and-white.

participants

Coloredwords

BWwords

Test

2-levels

Each of the participants is in only one level of the IV

Between-Groups Factor

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levelslevels

participantsColoredwords

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2-levels, All of the participants are in both levels of the IV

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levelslevels

Sometimes called “repeated measures” design

Within-Groups Factor

Between vs. Within Subjects Designs

Within-subjects designs

All participants participate in all of the conditions of the experiment.

participants

Coloredwords

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Test participantsColoredwords

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Between-subjects designs Each participant

participates in one and only one condition of the experiment.

Within-subjects designs

All participants participate in all of the conditions of the experiment.

participants

Coloredwords

BWwords

Test participantsColoredwords

BWwords

TestTest

Between-subjects designs Each participant

participates in one and only one condition of the experiment.

Between vs. Within Subjects Designs

Between subjects designs

Advantages:

Independence of groups (levels of the IV)• Harder to guess what the experiment is about without

experiencing the other levels of IV • Exposure to different levels of the independent variable(s)

cannot “contaminate” the dependent variable• Sometimes this is a ‘must,’ because you can’t reverse the

effects of prior exposure to other levels of the IV• No order effects to worry about

• Counterbalancing is not required

participants

Coloredwords

BWwords

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Between subjects designs

Disadvantages

Individual differences between the people in the groups

• Excessive variability• Non-Equivalent groups

participants

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Individual differences

The groups are composed of different individuals

participants

Coloredwords

BWwords

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Individual differences

The groups are composed of different individuals

participants

Coloredwords

BWwords

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Excessive variability due to individual differences Harder to detect the effect of the IV if there

is one

RNR R

Individual differences

The groups are composed of different individuals

participants

Coloredwords

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Non-Equivalent groups (possible confound) The groups may differ not only because of the IV, but also because the

groups are composed of different individuals

Dealing with Individual Differences

Strive for Equivalent groups Created equally - use the same process to

create both groups Treated equally - keep the experience as

similar as possible for the two groups Composed of equivalent individuals

• Random assignment to groups - eliminate bias• Matching groups - match each individuals in one

group to an individual in the other group on relevant characteristics

Matching groups

Group A Group B Matched groups Trying to create

equivalent groups Also trying to reduce

some of the overall variability

• Eliminating variability from the variables that you matched people on

RedShort21yrs

Bluetall

23yrs

Greenaverage

22yrs

Browntall

22yrs

ColorHeight

Age

matchedRedShort21yrs

matched Bluetall

23yrs

matchedGreen

average22yrs

matchedBrown

tall22yrs

Within-subjects designs

All participants participate in all of the conditions of the experiment.

participants

Coloredwords

BWwords

Testparticipants

Coloredwords

BWwords

TestTest

Between-subjects designs Each participant

participates in one and only one condition of the experiment.

Between vs. Within Subjects Designs

Within subjects designs

Advantages: Don’t have to worry about individual differences

• Same people in all the conditions• Variability between conditions is smaller (statistical

advantage)

Fewer participants are required

Within subjects designs

Disadvantages Order effects:

• Carry-over effects • Progressive error

Counterbalancing is probably necessary to address these order effects

testCondition 2Condition 1

test

Order effects

Carry-over effects Transfer between conditions is possible Effects may persist from one condition into

another• e.g. Alcohol vs no alcohol experiment on the effects on

hand-eye coordination. Hard to know how long the effects of alcohol may persist.

How long do we wait for the effects to wear off?

Order effects

Progressive error Practice effects – improvement due to repeated

practice Fatigue effects – performance deteriorates as

participants get bored, tired, distracted

Dealing with order effects

Counterbalancing is probably necessary This is used to control for “order effects”

• Ideally, use every possible order • n! (e.g., AB = 2! = 2 orders; ABC = 3! = 6 orders, ABCD = 4! = 24 orders, etc).

All counterbalancing assumes Symmetrical Transfer

• The assumption that AB and BA have reverse effects and thus cancel out in a counterbalanced design

Counterbalancing

Simple case Two conditions A & B Two counterbalanced orders:

• AB• BA

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Not so simple analysis: need to analyze as a factorial design, with 2 factors (word color & order)

Counterbalancing

More than two conditions: Often it is not practical to use every possible

ordering• n! (e.g., AB = 2! = 2 orders; ABC = 3! = 6 orders, ABCD = 4! = 24 orders, etc)

• Common Solution: Partial counterbalancing • Latin square designs – a form of partial counterbalancing, so

that each group of trials occur in each position an equal number of times

Simple case

Partial counterbalancing

Example: consider four conditions Recall: ABCD = 4! = 24 possible orders

1) Unbalanced Latin square: each condition appears in each position (4 orders)

DCBA

ADCB

BADC

CBAD

Order 1

Order 2

Order 3

Order 4

Partial counterbalancing

2) Balanced Latin square: each condition appears before and after all others (8 orders)

A B D C

B C A D

C D B A

D A C B

A B C D

B C D A

C D A B

D A B C

Example: consider four conditions Recall: ABCD = 4! = 24 possible orders

Mixed factorial designs

Mixed designs Treat some factors as within-subjects

(participants get all levels of that factor) and others as between-subjects (each level of this factor gets a different group of participants).

This only works with factorial (multi-factor) designs

Class experiment results

Source SS df Mean Square F Sig.Word Type 22.4 1 22.4 9.3 .003Depth of Proc 120.3 1 120.3 39.0 < .001Word * Depth 10.5 1 10.5 4.3 .039Error (within) 357.1 148 2.4Error (btwn) 456.4 148 3.1

4.03

4.92

4.2

5.84

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Shallow Deep

Depth of Processing

Mean number of recalled

words

AbstractConcrete

• Main effect of both variables• An interaction

Describing your design

You need to describe: How many factors How many levels of each factor Whether the factors are within or between groups

• e.g., 2 (shallow/deep processing) x 2 (abstract/concrete) mixed groups factorial design

abstract concrete

Shallow 4.0 4.2

Deep 4.9 5.8