3.9 Quasi-experimental designs | Quantitative methods | Research Designs | UvA

Introduction

This tutorial provides a comprehensive overview of quasi-experimental designs, which are essential in research when random assignment of participants is not possible. Understanding these designs is crucial for researchers in fields where manipulation is not feasible, allowing for valid comparisons and insights into cause-and-effect relationships.

Step 1: Understand Quasi-Experimental Designs

Quasi-experimental designs are used when researchers cannot randomly assign participants to groups. This can occur in situations where:

• Ethical considerations prevent randomization.
• Practical constraints limit the ability to assign participants randomly.
• The nature of the study does not allow for manipulation of independent variables.

Recognizing when to use these designs is the first step in conducting effective research.

Step 2: Explore Different Types of Quasi-Experimental Designs

There are several types of quasi-experimental designs, each with its unique features and applications:

1. Static Group Comparison

   • Involves two or more groups that are not randomly assigned.
   • Researchers compare outcomes between groups that already exist.
   • Example: Comparing test scores between two different classrooms.

2. Pre/Posttest Non-Equivalent Control Group

   • Involves measuring participants before and after an intervention.
   • Includes a control group that does not receive the intervention.
   • Important to note that the groups may differ in significant ways.
   • Example: Measuring student performance before and after a new teaching method is applied.

3. Interrupted Time-Series Design

   • Involves repeated measurements over time before and after an intervention.
   • This design helps to identify trends and changes due to the intervention.
   • Example: Analyzing crime rates before and after the implementation of a new law.

4. Replicated Interrupted Time-Series Design

   • Similar to interrupted time-series but includes multiple replications.
   • Involves multiple groups or settings to strengthen the findings.
   • Useful for establishing reliability in the results.
   • Example: Studying the effects of a public health campaign across different cities over time.

Step 3: Consider the Strengths and Weaknesses of Quasi-Experimental Designs

It’s important to weigh the pros and cons of quasi-experimental designs:

Strengths

• More ethical and practical in real-world settings.
• Can provide valuable insights when randomization is not possible.

Weaknesses

• Higher risk of confounding variables affecting results.
• Limited ability to infer causation compared to true experimental designs.

Step 4: Apply Quasi-Experimental Designs to Research Questions

When designing your study, consider how a quasi-experimental design can help address your research question. Use the following steps:

• Define your research question clearly.
• Choose the appropriate quasi-experimental design based on your context.
• Identify your groups and ensure you have comparable samples.
• Collect data systematically and analyze with appropriate statistical methods.

Conclusion

Quasi-experimental designs are vital tools for researchers in many fields, allowing for the exploration of relationships when random assignment is not feasible. By understanding the different types of designs and their applications, researchers can effectively address their questions and contribute valuable findings to their fields. Consider your specific context and select the most suitable design for your study to improve the validity of your results.