Surveys, Experiments, and SimulationsUnit 3

Part 3Experimental Design

More Complex Experimental Design

Experimental Design

Multiple Factors Blind Double Blind Block Design Matched Pairs Design

Multiple FactorsExperimental Design

ExperimentalUnits or Subjects

Treatment Applied

ObservedResponse

Group A: SRS of 40 individuals

1 hour of exercise daily

200 mg of caffeine daily

Measured Response:Weight LossObserved Rate:3 lbs / month

Population ofInterestIndividuals 40 to 60 years of age.

Group C: SRS of 40 individuals

0 hours of exercise daily

200 mg of caffeine daily

Measured Response:Weight LossObserved Rate:-3 lbs / month

Group B: SRS of 40 individuals

1 hour of exercise daily

0 mg of caffeine daily

Measured Response:Weight LossObserved Rate:2 lbs / month

2 factors, 2 levels of each= 4 treatment groups

Group D: SRS of 40 individuals

0 hours of exercise daily

0 mg of caffeine daily

Measured Response:Weight LossObserved Rate:-2 lbs / month

Single Blind DesignExperimental Design

In a blind experiment the participants do not know which treatment they are receiving and often don’t know the response being measured.

The purpose is to remove participant response bias which might be either intentional or unconscious.

Ex. A taste test is a classic example of a single blind design where a tester prepares two sets of cups of cola labeled "A" and "B". For example, one set of cups is filled with Pepsi, while the other is filled with Coca-Cola. The tester knows which soda is in which cup but is not supposed to reveal that information to the subjects. Volunteer subjects are encouraged to try the two cups of soda and polled for which ones they prefer.

Problems with a Single Blind Design

Experimental Design

In some cases this simply might not be possible.

In the case of the taste test, the tester might be biased and give unconscious hints – for example, overfilling one glass, pushing it closer to the taster, etc.

Double Blind DesignExperimental Design

In a double blind experiment the participants do not know which treatment they are receiving or the response being measured. Additionally, the experimenter doesn’t know which treatment is being applied.

The purpose is to remove response bias which might be either intentional or unconscious by both the participant AND experimenter.

Ex. Expanding on the taste test, the two cups can be provided by an independent 3rd party who labels the cups on the bottom w/ the contents. After the testing and rating, the cups are turned over to reveal which is which.

Experimental Design

Sometimes this is even more difficult to conduct (or impossible).

Random assignment by a computer, processing of treatments by 3rd parties (ie. Filling and labeling pill containers, etc.) is required which can be difficult.

In the most well designed cases, the results will be completely gathered AND analyzed before the revelation is ever made to the tester. For example, in the taste test, the final result might be that Soda A had 120 people prefer the soda and Soda B had 30. Only after the conclusion of data collection and analysis would the tester learn that Soda A = Pepsi and Soda B = Coca Cola. That way they wouldn’t apply their bias at any point during the experiment or analysis.

Problems with Double Blind Design

Block DesignExperimental Design

A block design is simply a stratified random sample, but for EXPERIMENTS.

block design: experiment

stratified random sample: observational study

In both cases you are first dividing potential participants by some strata (characteristic) of the participants before either measuring something or applying treatments.

Matched Pairs DesignExperimental Design

This is a very special style of experiment where you try to make sure that every participant in treatment group A has a corresponding and congruent (yay geometry) participant in treatment group B (and other treatment groups if there are 3+).

Example: Experiment to measure the impact of Drug 23 vs PlaceboTreatment Group A Participants Treatment Group B Participants

Name Age Gender Occupation

Henry 22 M Policeman

Tabitha 23 F Military

Mick 35 M Politician

Matthew 31 M Sales Person

Samantha 27 F Teacher

Name Age Gender Occupation

Rick 22 M Policeman

Tammi 23 F Military

Carl 35 M Politician

Hank 31 M Sales Person

Christina 27 F Teacher

Double Treatment DesignExperimental Design

This is a very special style of experiment where you try to make sure that every participant in treatment group A has a corresponding and congruent (yay geometry) participant in treatment group B (and other treatment groups if there are 3+).

Example: Experiment to measure the impact of Drug 23 vs Placebo

Name Age Gender Occupation

Henry 22 M Policeman

Tabitha 23 F Military

Mick 35 M Politician

Matthew 31 M Sales Person

Samantha 27 F Teacher

20 mg Drug 23 daily

Measured Response:Cholesterol LevelObserved Rate:20% improvement

Placebo

daily

Measured Response:Cholesterol LevelObserved Rate:5% improvement

Participants Second TreatmentFirst Treatment Wai

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