Political Science 101 POLS: Political Methodology - Experimental Methods Survey Experiments Lab Experiments Field Experiments Natural Experiments

1. What This Is & Why It Matters

Experimental Methods in Political Science: Understanding Causal Relationships

Experimental methods are a crucial tool in political science, allowing researchers to establish cause-and-effect relationships between variables. By manipulating one variable and measuring its impact on another, researchers can gain insights into the underlying mechanisms driving political phenomena. Without understanding experimental methods, you can't explain why some policies succeed or fail, why certain institutions are effective, or why some leaders are more popular than others.

Consider the example of the 2019-2020 COVID-19 pandemic. Governments around the world implemented various policies to mitigate the spread of the virus, such as lockdowns, mask mandates, and vaccination campaigns. By using experimental methods, researchers can evaluate the effectiveness of these policies and identify the most effective strategies for controlling the pandemic.

2. Essential Thinkers, Concepts & Models

• *Randomized Controlled Trials (RCTs)*: A research design in which participants are randomly assigned to treatment or control groups to establish causality.
  • Definition: A method for testing the effect of a treatment or intervention on a specific outcome.
  • Why it's still relevant: RCTs are widely used in medicine and social sciences to evaluate the effectiveness of interventions.
• *Survey Experiments*: A research design in which survey respondents are randomly assigned to different treatment conditions to test the effect of a variable on attitudes or behaviors.
  • Definition: A method for testing the effect of a variable on attitudes or behaviors by manipulating the survey question or context.
  • Why it's still relevant: Survey experiments are commonly used in public opinion research to understand how different factors influence attitudes towards policies or politicians.
• *Field Experiments*: A research design in which researchers manipulate a variable in a real-world setting to test its effect on a specific outcome.
  • Definition: A method for testing the effect of a variable on a specific outcome by manipulating it in a real-world setting.
  • Why it's still relevant: Field experiments are used in various fields, including economics, sociology, and politics, to evaluate the effectiveness of policies or interventions.
• *Natural Experiments*: A research design in which researchers take advantage of existing variations in a variable to test its effect on a specific outcome.
  • Definition: A method for testing the effect of a variable on a specific outcome by exploiting existing variations in the variable.
  • Why it's still relevant: Natural experiments are used in various fields, including economics, sociology, and politics, to evaluate the effectiveness of policies or interventions.
• *Instrumental Variables (IV)*: A statistical technique used to identify causal relationships by exploiting the relationship between a variable and a related variable.
  • Definition: A statistical technique used to identify causal relationships by exploiting the relationship between a variable and a related variable.
  • Why it's still relevant: IV is widely used in economics and politics to evaluate the effectiveness of policies or interventions.
• *Regression Discontinuity Design (RDD)*: A research design in which researchers take advantage of a discontinuity in a variable to test its effect on a specific outcome.
  • Definition: A method for testing the effect of a variable on a specific outcome by exploiting a discontinuity in the variable.
  • Why it's still relevant: RDD is used in various fields, including economics, sociology, and politics, to evaluate the effectiveness of policies or interventions.
• *Difference-in-Differences (DiD)*: A statistical technique used to identify causal relationships by comparing the change in outcomes between treatment and control groups over time.
  • Definition: A statistical technique used to identify causal relationships by comparing the change in outcomes between treatment and control groups over time.
  • Why it's still relevant: DiD is widely used in economics and politics to evaluate the effectiveness of policies or interventions.

3. Step-by-Step 'Political Analysis'

1. Define the research question: Clearly articulate the research question or hypothesis to be tested.
2. Choose the research design: Select the most appropriate research design (RCT, survey experiment, field experiment, natural experiment, IV, RDD, or DiD) to test the research question.
3. Collect data: Gather data from the chosen research design, ensuring that the data is reliable and valid.
4. Analyze the data: Use statistical techniques to analyze the data and identify the effect of the variable on the outcome.
5. Interpret the results: Interpret the results in the context of the research question and research design.
6. Draw conclusions: Draw conclusions based on the results, taking into account the limitations of the research design and data.

4. Common Student Mistakes

• Misconception: Experimental methods are only used in laboratory settings.
  • The right view: Experimental methods can be used in various settings, including laboratory, field, and natural experiments.
• Misconception: Survey experiments are only used to test attitudes or behaviors.
  • The right view: Survey experiments can be used to test a wide range of variables, including attitudes, behaviors, and outcomes.
• Misconception: Field experiments are only used to test the effect of policies or interventions.
  • The right view: Field experiments can be used to test the effect of various variables, including policies, interventions, and institutional factors.
• Misconception: Natural experiments are only used to test the effect of variables in the absence of a treatment or intervention.
  • The right view: Natural experiments can be used to test the effect of variables in the presence of a treatment or intervention, as well as in the absence of one.

5. Exam/Essay Tips

• Multiple-choice questions: Be able to identify the most appropriate research design for a given research question or scenario.
• Free-response questions: Be able to explain the strengths and limitations of different research designs and how they are used in political science research.
• Trap distinctions: Be able to distinguish between different research designs, such as RCTs, survey experiments, and field experiments.
• Integrating the topic into an argumentative essay: Be able to use experimental methods to support or challenge a claim or argument in an essay.

6. Quick Practice Scenario

Scenario: A government implements a new policy to increase voter turnout, but the policy is only implemented in half of the districts. Which research design is most appropriate to evaluate the effect of the policy on voter turnout?

Answer: Difference-in-Differences (DiD), because it allows researchers to compare the change in voter turnout between treatment and control groups over time.

Justification: DiD is a statistical technique that is well-suited to evaluate the effect of a policy on an outcome, especially when the policy is only implemented in a subset of the population.

7. Last-Minute Cram Sheet

• RCTs: A research design in which participants are randomly assigned to treatment or control groups to establish causality.
• Survey Experiments: A research design in which survey respondents are randomly assigned to different treatment conditions to test the effect of a variable on attitudes or behaviors.
• Field Experiments: A research design in which researchers manipulate a variable in a real-world setting to test its effect on a specific outcome.
• Natural Experiments: A research design in which researchers take advantage of existing variations in a variable to test its effect on a specific outcome.
• Instrumental Variables (IV): A statistical technique used to identify causal relationships by exploiting the relationship between a variable and a related variable.
• Regression Discontinuity Design (RDD): A research design in which researchers take advantage of a discontinuity in a variable to test its effect on a specific outcome.
• Difference-in-Differences (DiD): A statistical technique used to identify causal relationships by comparing the change in outcomes between treatment and control groups over time.
• Separate but equal was overturned by Brown v. Board – Plessy v. Ferguson was the earlier, racist ruling.
• Random assignment is a key feature of RCTs, but it is not the only way to establish causality.

8. Further Study Resources

• American Government: Stories of a Nation by David M. Kennedy, Lizabeth Cohen, and Thomas A. Bailey
• Khan Academy units on experimental methods and research design
• Crash Course Government YouTube channel
• The Oxford Handbook of Experimental Methods in Political Science edited by Donald P. Green and Benjamin Schneer