POL222 Exam

Key Concepts:

  1. Types of Analysis:

    • Qualitative Analysis:

      • In-depth study of a few cases (historical accounts, interviews).

    • Quantitative Analysis:

      • Use statistical methods to study large datasets.

      • Benefits: Identifying patterns, testing theories on many cases.

  2. Why Quantitative Methods?

    • Build generalizable knowledge about politics.

    • Avoid selection bias by examining diverse datasets.

    • Example: Understanding why some countries democratize using global data rather than selective case studies.

  3. Methodology Overview:

    • Theory and Hypothesis:

      • Develop causal theories and testable hypotheses.

    • Empirical Analysis:

      • Collect and analyze data to validate hypotheses.

 

Week 2: How Do We Study Politics Quantitatively?

Applications of Quantitative Methods:

  1. Forecasting:

    • Predict election outcomes, economic trends.

  2. Description:

    • Use surveys to measure public opinion, ideology, and democracy.

  3. Causal Questions:

    • Evaluate policies and their outcomes using causal theories.

Scientific Method for Causal Questions:

  1. Theory:

    • Framework explaining causal relationships (e.g., economic development leads to democratization).

  2. Hypothesis:

    • A testable prediction based on theory.

  3. Empirical Analysis:

    • Use real-world data to confirm or refute hypotheses.

 

Week 3: Evaluating Causal Relationships – Part 1

Challenges in Causal Inference:

  1. Confounding Variables:

    • Factors (Z) that influence both the dependent (Y) and independent (X) variables.

  2. Reverse Causality:

    • Difficulty in determining whether X causes Y or vice versa.

Key Terms:

  • Omitted Variable Bias:

    • Excluding confounders leads to misleading conclusions.

  • Spurious Relationships:

    • False associations that disappear when confounders are controlled.

Examples:

  • Misinterpreting educational attainment as the sole cause of political participation without accounting for socioeconomic background.

 

Week 4: Evaluating Causal Relationships – Part 2【16†source**

Biases from Omitted Confounders:

  1. Overestimation:

    • Effect appears stronger than it is.

  2. Underestimation:

    • Effect appears weaker than it is.

  3. Sign Reversal:

    • Effect appears in the opposite direction due to confounding bias.

Approach:

  • Account for confounders using statistical controls or advanced modeling.

 

Week 7: Experimental Research Basics

What Makes Experiments Powerful?

  1. Random Assignment:

    • Balances confounders across groups.

  2. Controlled Environment:

    • Isolates the effect of independent variables on outcomes.

Challenges in Political Science:

  • Difficulty replicating real-world complexity in experiments.

Example:

  • Testing the impact of negative political ads on voter turnout:

    • Control group sees neutral ads, treatment group sees negative ads.

    • Compare voting intentions.

 

Week 8: Validity in Experimental Research

Types of Validity:

  1. Internal Validity:

    • Confidence in causal relationships.

    • Enhanced by random assignment.

  2. External Validity:

    • Generalizability to real-world scenarios.

    • Often limited in lab settings but stronger in field experiments.

Example Studies:

  1. Garramone et al. (1990):

    • Lab study on political ads with controlled stimuli.

    • Strong internal but weak external validity.

  2. Ansolabehere et al. (1994):

    • Field study with real election ads.

    • Better external validity.

 

Week 9: Survey Experiments & Discussion【19†source**

Survey Methods:

  • Representative Sampling:

    • Randomly selected participants allow generalization to the population.

  • Non-Representative Sampling:

    • Convenience sampling is less generalizable.

Survey Experiments:

  • Treat survey questions as experimental stimuli.

  • Example: Testing support for military action under conscription vs. voluntary service.

Ethical Considerations:

  • Ensure participants are informed and not harmed.

 

Week 10: Observational Research【20†source**

Why Observational Studies?

  • When random assignment is infeasible, use real-world data to examine X → Y relationships.

Approach:

  1. Conditioning:

    • Compare outcomes (Y) under different independent variable values (X).

  2. Controlling for Z:

    • Adjust for confounders statistically to reveal true effects.

Examples:

  • Economic voting in Canada:

    • Perceptions of the economy’s health predict voter preferences.

 

Week 11: Linear Regression Analysis【21†source**

Purpose of Regression:

  • Model relationships between variables.

  • Predict outcomes (Y) based on independent variables (X, Z).

Key Concepts:

  1. Simple Regression:

    • Examines one independent variable.

  2. Multiple Regression:

    • Accounts for multiple confounders.

Interpretation:

  • Coefficients indicate the impact of X on Y.

  • Example:

    • Improving economic perception increases support for the incumbent party by a measurable amount.

 

Week 12: Validity of Observational Research & Natural Experiments

Internal and External Validity:

  • Internal validity may be weaker than experiments.

  • External validity often stronger than lab experiments due to real-world context.

Natural Experiments:

  • Exploit naturally occurring variations resembling random assignment.

  • Example:

    • Study effects of policy changes or natural disasters.

  • Can provide insights into causal relationships when controlled experiments are not feasible. This approach can help identify the impact of interventions by comparing outcomes between affected and unaffected groups. Additionally, it allows researchers to leverage existing data sets to analyze trends over time, further enhancing the validity of the findings.