Q1 Flashcards

1
Q

FAILURE TO RANDOMIZE

A

Threats to internal Validity

  • treatment not randomly assigned, based on part of characteristics or preferences
  • ethnic difference last name
  • Can test for if control variables coefficients W are 0 or not. If Random, X will be uncorrelated with W.
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2
Q

Failure to follow treatment protocol / partial complience

A

Threats to Internal Validity of Experiments

  • People in the experiment does not do what they are told.
  • Treatment group supposed to take a pill every day: some doesnt
  • 10% of students switched groups because of behavior problems
  • This failure leads to bias in the OLS estimator.
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3
Q

Attrition

A

Threats to Internal Validity of Experiments

  • Some subjects drop out. If the reason for attribution is related to the treatment itself, then the attrition can result in bias in the OLS
  • Students move out of district
  • Students leave for other schools
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4
Q

Experimental effects / Hawthorne

A

Threats to Internal Validity of Experiments

  • Subjects behaviour might be affected by being in a experiment. (Hawthorne Effect)
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5
Q

Small Sample Sizes

A

a threat to Internal Validity of Experiments

  • small sample does not necessary bias estimator of causal effect
  • raises threat to validity of conf intervalls and hypothesis test
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6
Q

Nonrepresentative Sample

A

External Validity for Idealized Experiments

  • The population studied and the population of interest might differ
  • sample only includes people with one type of characteristics
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7
Q

Nonrepresentative program or policy

A
  • if there is a considerable difference in the program or policy for the population studied vs the population of interest
  • low funding small scale experiment might have different effect than a large experiment
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8
Q

General Equilibrium effects

A

External Validity

  • Turning a small, temporary exp intro a widespread, permanent program
  • sometimes only works with small groups
  • Ac training Zimbabwe, 10 villages 40% increase wages. Nationwide: Different effect, become skilled, decrease in wage gains
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9
Q

Two types of Quasi Experiments

A
  1. If an individual receive treatment “as if” it is randomly determined
  2. “as if” variation only partially determines the treatment
    - causal effect estimated by IV reg
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10
Q

What is Quasi Experiments

A
  • experiment that tries to find causal effect, or relationship between dependent and independent variable
  • main difference: groups not randomly assigned.
  • effect estimated on a targeted population
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11
Q

Quasi Experiment also called

A

Natural Experiment

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12
Q

Quasi Advantages vs Normal

A
  • when it is unethical or impractical to run a true experiment
  • higher external validity, can use real world interventions instead of artificial labratory settings
  • Higher External Validity than most true experiments (lets you involve real-world interventions)
  • Higher internal Validity than other non-experimental types of research, because it allows you to better control for confounding variables
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13
Q

Confounding Variables

A

collect data on sunburns and ice cream consumption. Find a correlation. Does not mean ice cream leads to sun burn. In this case, the Confounding variable is temperature. Temperature leads to both ice cream and sun burn

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14
Q

Disadvantages Quasi

A
  • without randomization it can be difficult to verify that all omitted biases have been accounted for
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15
Q

What is DiD

A
  • type of panel data
  • control & treatment
  • find causal effect of a specific intervention or treatment
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16
Q

DiD Assumptions

A
  • parallell trend

- no spillover effects

17
Q

DiD Strengths

A
  • can obtain causal effect if assumptions met

- comparison groups can start at different level

18
Q

Quasi: Potential Problems (Internal)

A
  1. Fail to randomize
  2. Fail to follow treatment protovol
  3. Attrition
  4. Experimental Effects
  5. Instrument Validity in quasi Experiments
19
Q

Quasi, failure to randomize

A

threat to internal validity

  • failure to produce treatment level X that is random = biased OLS

SOL: check for systematic differences between control group and treatment group

20
Q

Quasi, failure to follow treatment protocol

A

threat to internal validity

21
Q

Quasi Attrition

A

Similar

22
Q

Quasi Experimental

A
  • Advantage that they are not true experiments

- typically no reason for individuals to think they are experiment subjects

23
Q

Instrument validity in quasi experiments

A

threat to internal validity

  • if IV is valid
  • need both IV relevance and exogeneity
24
Q

Quasi, Threats to External Validity

A
  • use observational data, so similar to normal experiments
25
Q

Idealized Experiment

A
  1. The subjects are selected at random from the population.

2. The subjects are randomly assigned to treatment and control group.

26
Q

How can we test if control variabes are random

A
  • Can test for if control variables coefficients W are 0 or not. If Random, X will be uncorrelated with W.