GATHERING DATA

Chapter 4

4.3 What are Good and Poor Ways to Experiment?

Elements of an Experiment

¨  Experimental units: Subjects

¨  Treatment: Conditions imposed on subjects

¨  Explanatory variable: Defines groups and treatments

¨  Response variable: Outcome

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Experiments

¨  Impose treatments on subjects to observe responses

¨  Goal: compare effects of treatments on response

¨  Randomized experiments – subjects randomly assigned to treatments

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Placebo effect

Placebo – fake treatment; sugar pill

Placebo effect –improving not from real treatment but from belief that he or she should improve

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3 Components of a Good Experiment

¨  Control or Comparison Group ¨  Randomization ¨  Replication

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Principle 1: Control or Comparison Group

¨  Helps analyze effectiveness of primary treatment

¨  Placebo removes lurking variables

¨  Control group gets placebo ¤ Clinical trials may

compare new treatment with existing

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¨  Experiments should compare treatments rather than effect of single treatment

¨  Example: 400 volunteers asked to quit smoking with some taking and some not taking antidepressant

Control or Comparison Group

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Principle 2: Randomization

1.  Eliminates bias from researcher assigning subjects

2.  Balances groups on known and lurking variables

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Principle 3: Replication

1.  Reduces difference due to ordinary variation or chance

2.  Increases chance that results show true difference

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Blinding the Experiment

¨  Blind – subjects unaware of which treatment used

¨  Double-Blind Experiment - Neither subjects nor investigators know which treatment ¤ Controls bias from

respondent and experimenter

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Statistically Significant Difference – Observed difference is larger than expected from chance

Statistical Significance

Generalizing Results

¨  Goal of Experimentation – Analyze association between treatment and response for entire population

¨  Generalize only to population represented by study

¨  Page 180 #34, 40

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4.4 Other Ways to Conduct Experimental and Observational Studies

Sample Surveys: Random Sampling Designs

¨  Alternative to experiments 1.  Simple Random

Sampling 2.  Cluster Random

Sampling 3.  Stratified

Random Sampling

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Cluster Random Sample

1.  Divide population into large number of clusters, such as city blocks

2.  Select simple random sample of clusters

3.  Use all subjects in clusters as sample

Advantages ¤ Sampling

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¤ Cost Disadvantage

¤ Need larger sample size for same reliability

Cluster Random Sample

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Stratified Random Sample

1.  Divide the population into groups, strata

2.  Select SRS from each strata

3.  Combine samples from each for total sample

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Advantage ¨  Ensures stratum

representation Disadvantage ¨  Need sampling

frame and to which stratum each subject belongs

Stratified Random Sample www.smh.com.au

Comparing Random Sampling Methods

Types of Observational Studies

1.  Sample Survey: current

2.  Retrospective Study: past

3.  Prospective Study: future

¨  Cause not proven, but studies can support beliefs

Retrospective Case-Control Study

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Studying sunlight exposure and multiple sclerosis connection…

Retrospective

Cases – have MS Controls or don’t

Explanatory variable – low sun or not

Prospective Case-Control Study

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Studying effects of

vegetarian diet on heart

disease…

Prospective

Cases – have heart disease Controls or don’t

Explanatory variable – vegetarian or not

Multifactor Experiments

¨  Single experiment analyzes two or more factors

¨  Learn more since combinations may affect response

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Matched Pairs Design

¨  Subjects are somehow matched ¤ Husband/wife, two plots in

same field, etc. ¤ Same individual – crossover

design ¨  Randomly assign or randomize

order of treatments ¨  Reduces effects of lurking

variables

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Randomized Block Design

¨  Block – subjects with common characteristics ¨  Randomized Block Design, RBD – within each block,

randomly assign to treatments