Survey Research and Causation: Exploring Brady’s Approaches
Survey Research and Causation
Alignment with Brady’s Approaches
Survey research aligns best with the Quantitative/Probability approach to causation described by Brady. This approach emphasizes probability sampling, where random samples are drawn from a population to make inferences about the larger group. The goal is to establish external validity, ensuring the findings can be generalized beyond the sample.
Key characteristics of the Quantitative/Probability approach include:
- Deductive reasoning
- Emphasis on probability samples (e.g., cluster, random, stratified)
- Operationalization of theoretical concepts into measurable variables
- Hypothesis testing
Limitations of Causal Inference with Survey Data
While survey research offers valuable insights, there are limitations to making causal inferences using survey data:
- Lack of Manipulation: Surveys typically observe existing conditions rather than manipulating variables, making it challenging to establish clear cause-and-effect relationships.
- Potential for Bias: Issues like self-selection bias, social desirability bias, and measurement error can influence survey responses and affect the validity of causal conclusions.
Exploring Other Approaches
Brady discusses additional approaches to causation, including:
Quasi-Experimentation
This approach draws inspiration from controlled experiments but often lacks the same level of control over variables. It can be useful when true experiments are not feasible.
Regularity Approach
This approach focuses on identifying regularities or patterns of association between variables. However, it can be difficult to establish causation solely based on observed regularities.
INUS Conditions
Brady introduces the concept of INUS conditions, which are insufficient but non-redundant parts of a condition necessary for an effect to occur. This framework helps understand complex causal relationships.
The Pairing Problem and Mechanistic Theory
Brady highlights the “pairing problem,” which refers to the challenge of determining which specific cause is responsible for a particular effect. He argues that the Mechanistic Theory offers the best solution. This theory emphasizes understanding the underlying causal mechanisms and processes that connect causes and effects.
The Mechanistic Theory provides a more robust framework for causal inference by focusing on the chain of events and processes leading to an outcome. It acknowledges the complexity of causal relationships and moves beyond simple associations or probabilities.
Deterministic vs. Probabilistic Causation
Brady leans towards a deterministic view of causation, suggesting that events have specific causes and that probabilities can sometimes obscure the true causal mechanisms. He emphasizes the importance of identifying the underlying processes that drive outcomes.
In conclusion, while survey research aligns well with the Quantitative/Probability approach, it’s crucial to be aware of its limitations and consider alternative approaches like the Mechanistic Theory to strengthen causal inferences.