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Data Collection Via Experiments - Coggle Diagram
Data Collection Via Experiments
Slide Information
Remember the two strategies for data collection
Observational Studies (First lecture)
Experiments
Recruit a group of people and assign them a treatment
Can give evidence that treatment causes a response
we control lurking variables to do this
Lurking variables are variables in the study that you haven't controlled for, but may affect the outcome
Experiments try to control for as many lurking variables as possible
Structure of experiments
Experiment Vocab
Subject - Individual to measure
Response variable (y) - outcome of the experiment ie damage
Explanatory variable (x) - variable used to explain the response ( num fire fighters )
Treatment - the condition or conditions applied to a subject or individual in an experiment ( 5 fire fighters)
Control - a treatment with supposedly zero effects
Double-blind study - An experiment where the individual and researcher doesn't know which treatment is applied.
Confounding - a situation where a lurking variable, in addition to the explanatory variable is affecting the response
Principles of Valid experiments
Control/Comparison - Control lurking variable by including comparison treatments, using homogeneous subjects; used to measure placebo effect
Randomization - neutralize effects of lurking variables by randomly assigning subjects to treatments
Replication - assign more than one subject to each treatment group
double blinding
Main classes of good experiments
Randomized controlled experiment
Randomly split all subjects into treatment groups
There is a problem with this solved by randomized block experiment
Used when individuals are similar
Randomized block experiment - matched pairs as a special case
Separate rats by size first then randomize within each size
use when individuals are similar within block , but very different from block to block
removed confounding of lurking variables with response variable
yields more precise estimates of chance variation which makes detection of statistical significance easier
reduces chance variation by removing variation associated with the lurking (blocking) variable
Matched Pairs?
Explanatory variable: 2 level factor?
Block: 2 subjects who are very similar (twins)
Randomly assign 1 subject within each block to treatment
Example
Company boot example
Placebo effect
Diagnostic Effect
Problem: Diagnosis of subjects biased by preconceived notions about effectiveness of treatment
Solution: blind the diagnoser
Problems
Placebo
Diagnostic effect
Lack of realism
Hawthorne Effect
People in an experiment behave differently from how they would normally behave
Ethical hidden observation
Non compliance
Belmont Report - Ethical guidlines
Respect for persons with given informed consent
Beneficence - do no harm
Justice - can't exploit subjects
Equal distributions of burdens and benefits of study
experiments reviewed by an institutional review board (IRB) to ensure they follow ethical guidlines
Key Terms to understand
Causation
Lurking Variables
Subject
Response Variable
Explanatory Variable /factor
Treatment
Control
Double - blind
Placebo effect
Diagnostic Bias
Data ethics
Randomized Controlled Experiment
Randomized Block Experiment
Matched Pairs Experiment
Reading 2.2
Using a coin flip to decide which treatment a patient receives is called a "randomized experiment"
Principles of experimental design
Controlling
Researches assign treatments to cases, and try to control any differences between groups.
So, like if people take a pill by sipping water, or by drinking a whole glass of water. The researcher tells everyone to take the pill in the same way
Randomization
Randomize patients into treatment groups to account for variable that cannot be controlled.
So, like, some patients might be more susceptible to diseased because of diet.
This would be a confounding variable
That means it is associated with both the explanatory and response variables. Randomization of patients in treatment and control groups helps to even things out
Replication
You have to replicate. Size of replication is also important
There is a replication crisis
Pseudoreplication is when individual observations under different treatments are heavily dependent on each other
Blocking