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Quantitative Methods for Lawyers - Class #4 - Research Design Part IV - Professor Daniel Martin Katz

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Quantitative Methods for Lawyers - Class #4 - Research Design Part IV - Professor Daniel Martin Katz

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Quantitative Methods for Lawyers - Class #4 - Research Design Part IV - Professor Daniel Martin Katz

  1. 1. Quantitative Methods for Lawyers Research Design - Part IV Class #4 @ computational professor daniel martin katz
  2. 2. In our last session, we were discussing randomized control trials
  3. 3. Randomized Control Trial Control Group Treatment Group Follow Up Evaluation Follow Up Evaluation
  4. 4. RCT’s Are Often Considered the Gold Standard in Science
  5. 5. Because if properly executed there is a fairly clean relationship between cause and effect
  6. 6. Randomized Control Trial Control Group Treatment Group Follow Up Evaluation Follow Up Evaluation
  7. 7. Lets go through RCT’s and related forms of experiments
  8. 8. Experimental Data
  9. 9. Experiments are a great way to attempt to isolate causal effects
  10. 10. Major Weakness is External Validity (due to unknown interactions between variables, etc.)
  11. 11. Key Ideas: Random Assignment Representativeness Experimental Control Experimental Manipulation Factorial Design Double Blind
  12. 12. Random Assignment P r o b a b i l i t y o f B e i n g i n Treatment or Control Group Should Be Equal Composition of the Treatment or Control Group Should Be Similar Under Ideal Conditions this Eliminates other Confounds that could undermine Validity
  13. 13. Would like to overall subject group to mirror the population of interest Example: If we are interested in studying juveniles than the composition of bot h our treatment and control groups should be juveniles Representativeness
  14. 14. Experimental Control Classic Example is Medical Trial Involving a New Drug Experimental Group Given the New Drug Control Group Given the Sugar Pill How would Double Blind work in this context? What is a Placebo Effect?
  15. 15. Experimental Manipulation Under Ideal Conditions this would be the only difference between treatment and control group Experimental Manipulation and Factorial Design Watch out for too many Manipulations at one time
  16. 16. Variables
  17. 17. Concept: A variable is an attribute which describes a part of the makeup of an individual.
 Examples are gender, age, employment status, income level, race, or education level.
  18. 18. Studies are usually designed to collect and then compute the distribution and variation between and among the variables.
  19. 19. It should be noted that a variable, by definition, must possess variation; if all of the studied population have the same attribute, for example they are all employed, that attribute is a constant rather than a variable. 

  20. 20. There are different types of variables. One important division is between independent variables and dependent variables.

  21. 21. Independent variables act as the potential cause. They influence or predict an outcome from the dependent variable. They are the X’s on the right side of the equation.

  22. 22. Dependent variables act as the effect (or potential effect). They may change because of the influence of the independent variable. This is the Y on the left hand side of the equation.
  23. 23. Other Types of Variables
  24. 24. Categorical variables can take on one of a limited, and usually fixed, number of possible values
  25. 25. Nominal variables are variables that have two or more categories but which do not have an intrinsic order. For example, a real estate agent could classify their types of property into distinct categories such as houses, condos, co-ops or bungalows.
  26. 26. Dichotomous variables are nominal variables which have only two categories or levels. For example, we could categorize somebody as either Treated or Not Treated as either "Yes" or “No”. In the real estate agent example, if type of property had been classified as either residential or commercial then "type of property" would be a dichotomous variable.
  27. 27. Ordinal variables are variables that have two or more categories just like nominal variables only the categories can also be ordered or ranked. Large, Medium, Small, etc.
  28. 28. Describe some variables could that could predict/determine the price of a house? How Are They Coded?
  29. 29. School Quality New or Used Pool Garage Distance from City Center ... etc. BedRooms BathRooms Square Feet Lot Size Age of House Crime Rate
  30. 30. Bias in Scientific Study
  31. 31. Please note that “bias” in research terms is different. In normal language, bias is a prejudicial look at someone or something. 
 In research, bias is an action or inaction which can skew the outcome. 
 It does not have to be intentionally done. Bias in Scientific Study
  32. 32. What is a Correlation?
  33. 33. What is a Correlation?
  34. 34. Causality
  35. 35. Sometimes the statistical test shows a clear and significant relationship called a correlation between two variables. 
 There is a tendency to then conclude that the correlation shows causation. It may (or may not). 
 It could have nothing to do with causation or it could only have an indirect affect on the causation.
  36. 36. Daniel Martin Katz @ computational illinois tech - chicago kent college of law@