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Data Collection Via Experiments - Coggle Diagram

- - - - 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
- - - - 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
    - - 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
    - - 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