GATHERING DATA. Chapter 4
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1 GATHERING DATA Chapter 4
2 4.3 What are Good and Poor Ways to Experiment?
3 Elements of an Experiment Experimental units: Subjects Treatment: Conditions imposed on subjects Explanatory variable: Defines groups and treatments Response variable: Outcome
4 Experiments Impose treatments on subjects to observe responses Goal: compare effects of treatments on response Randomized experiments subjects randomly assigned to treatments
5 Placebo effect Placebo fake treatment; sugar pill Placebo effect improving not from real treatment but from belief that he or she should improve
6 3 Components of a Good Experiment Control or Comparison Group Randomization Replication pages.cs.wisc.edu
7 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
8 Control or Comparison Group 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
9 Principle 2: Randomization 1. Eliminates bias from researcher assigning subjects 2. Balances groups on known and lurking variables colorectal.surgery.ucsf.edu
10 Principle 3: Replication 1. Reduces difference due to ordinary variation or chance 2. Increases chance that results show true difference
11 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 gridskipper.com
12 Statistical Significance Statistically Significant Difference Observed difference is larger than expected from chance
13 Generalizing Results static.howstuffworks.com Goal of Experimentation Analyze association between treatment and response for entire population Generalize only to population represented by study Page 180 #34, 40
14 4.4 Other Ways to Conduct Experimental and Observational Studies
15 Sample Surveys: Random Sampling Designs codetechnology.files.wordpress.com Alternative to experiments 1. Simple Random Sampling 2. Cluster Random Sampling 3. Stratified Random Sampling
16 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
17 Cluster Random Sample reactorfire.files.wordpress.com Advantages Sampling frame unavailable Cost Disadvantage Need larger sample size for same reliability
18 Stratified Random Sample 1. Divide the population into groups, strata 2. Select SRS from each strata 3. Combine samples from each for total sample
19 Stratified Random Sample Advantage Ensures stratum representation Disadvantage Need sampling frame and to which stratum each subject belongs
20 Comparing Random Sampling Methods
21 Types of Observational Studies 1. Sample Survey: current 2. Retrospective Study: past 3. Prospective Study: future Cause not proven, but studies can support beliefs
22 Retrospective Case-Control Study Retrospective Studying sunlight exposure and multiple sclerosis connection Cases have MS Controls or don t Explanatory variable low sun or not
23 Prospective Case-Control Study Prospective Studying effects of vegetarian diet on heart disease Cases have heart disease Controls or don t Explanatory variable vegetarian or not
24 Multifactor Experiments Single experiment analyzes two or more factors Learn more since combinations may affect response biobreak.files.wordpress.com
25 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
26 Randomized Block Design Block subjects with common characteristics Randomized Block Design, RBD within each block, randomly assign to treatments
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