Types of Experiments in Behavioral Economics
Behavioral economics relies heavily on experimental methods to understand how people make decisions in real-world (or simulated real-world) scenarios. Choosing the right experimental design is crucial for generating valid and generalizable results. This module explores four common types of experiments: Lab, Field, Survey, and Online.
Laboratory Experiments
Laboratory experiments offer a high degree of control over variables. Participants are brought into a controlled environment where researchers can precisely manipulate conditions and measure outcomes. This allows for strong causal inference but may raise questions about ecological validity (how well the results generalize to real-world settings).
Lab experiments provide maximum control but may sacrifice real-world applicability.
In a lab setting, researchers can isolate specific factors influencing behavior. This is ideal for testing precise hypotheses about decision-making under controlled conditions.
Participants are typically recruited and brought to a dedicated research facility. The environment is standardized to minimize external influences. Researchers can control aspects like information provided, incentives, and the presence of other participants. This high level of control is excellent for establishing causal relationships between variables. However, the artificiality of the setting might lead participants to behave differently than they would in their natural environment, a phenomenon known as the Hawthorne effect or demand characteristics.
Field Experiments
Field experiments take place in natural settings, such as stores, workplaces, or public spaces. While they offer greater ecological validity than lab experiments, they often come with less control over extraneous variables. Researchers introduce interventions or changes and observe their impact on behavior.
Field experiments test behavior in natural settings, increasing real-world relevance.
By observing behavior where it naturally occurs, field experiments provide insights into how interventions perform outside the lab. This can involve changes to pricing, product placement, or communication strategies.
These experiments are conducted in the real world, where participants are unaware they are part of an experiment. For example, a researcher might test different pricing strategies in a supermarket or introduce a new communication method in an office. The advantage is that behavior observed is likely to be more representative of actual behavior. The challenge lies in controlling confounding variables that might influence the outcome, such as weather, competitor actions, or unrelated events. Random assignment of treatments to different groups or locations is crucial for establishing causality.
Survey Experiments
Survey experiments embed experimental manipulations within surveys. Participants are randomly assigned to different versions of a survey question or scenario, allowing researchers to test the impact of framing, information, or context on stated preferences or attitudes.
Survey experiments leverage surveys to test causal effects on attitudes and stated preferences.
Researchers can manipulate how questions are asked or what information is presented to different survey respondents to see how it affects their answers. This is efficient for exploring a wide range of factors.
In this design, participants respond to questions that vary systematically across different groups. For instance, one group might be asked about a policy with positive framing, while another receives the same question with negative framing. The researcher then compares the responses to understand the effect of framing. While efficient and scalable, survey experiments are limited to measuring stated preferences or beliefs, which may not always translate to actual behavior. Social desirability bias can also influence responses.
Online Experiments
Online experiments leverage digital platforms to conduct research. They can encompass lab-style experiments run via web interfaces, field experiments conducted through online platforms, or survey experiments. Their scalability and reach are significant advantages.
Online experiments offer scalability and broad reach for behavioral research.
Using websites or apps, researchers can recruit large, diverse participant pools and run experiments efficiently. This allows for rapid testing of hypotheses across various demographics.
These experiments are conducted using internet-based platforms. This can include online versions of lab experiments where participants interact with software, or field experiments where interventions are delivered digitally. Advantages include the ability to recruit large and diverse samples quickly and cost-effectively, and the potential for real-time data collection. However, researchers must contend with issues like participant attention, data quality, and the representativeness of the online population.
Comparing Experimental Designs
| Experiment Type | Control Level | Ecological Validity | Scalability | Typical Use Case |
|---|---|---|---|---|
| Laboratory | High | Low to Medium | Medium | Testing precise causal hypotheses, understanding fundamental decision mechanisms. |
| Field | Medium to Low | High | Medium to High | Testing interventions in real-world settings, observing actual behavior. |
| Survey | Medium | Low to Medium | High | Measuring attitudes, beliefs, and stated preferences; testing framing effects. |
| Online | Variable (depends on implementation) | Variable (depends on implementation) | Very High | Broad reach, efficient data collection, testing a wide range of hypotheses. |
The choice of experimental design involves a trade-off between control and realism. Often, researchers combine insights from different types of experiments to build a robust understanding of a phenomenon.
Key Considerations for Experimental Design
When designing an experiment, several factors are critical: defining clear research questions, identifying appropriate variables, ensuring random assignment to treatment groups, selecting the right participant pool, and choosing the most suitable experimental setting. Each type of experiment has its strengths and weaknesses, and the best choice depends on the specific research goals and constraints.
High degree of control over variables.
Field experiments.
They often measure stated preferences, which may not reflect actual behavior.
Scalability and broad reach.
Learning Resources
Provides an overview of experimental methods used in behavioral economics, discussing the rationale and application of different designs.
A seminal paper detailing the methodology, advantages, and challenges of conducting field experiments in economic research.
Discusses the practicalities and considerations for designing and conducting behavioral experiments using online platforms.
Offers guidance on the design and implementation of effective survey experiments, focusing on political science but applicable broadly.
A comprehensive reference covering the foundations and applications of experimental economics, including various experimental designs.
A concise introduction to the field, often touching upon the experimental methods used to study economic behavior.
Provides lecture notes and materials that often cover experimental design principles within behavioral economics.
A TED talk by Dan Ariely, a prominent behavioral economist, demonstrating how simple experiments reveal insights into human decision-making.
A short video explaining the concept and application of field experiments in social sciences.
A video discussing the differences, pros, and cons of conducting behavioral economics experiments in lab versus field settings.