Lecture on Quasi-Experimental Research Designs

Introduction

• Quasi-experimental designs: Nearly experimental but lack some components.
• Challenges include lack of random sampling and control groups.
• Focus on threats to internal validity and how experimental control can be compromised.

Characteristics of Quasi-Experimental Designs

• Lack of control over independent variables.
• Ethical limitations in manipulating certain variables (e.g., family dynamics).
• Difficulty in measuring dependent variables (e.g., longitudinal studies).
• Lack of random assignment is a key difference from true experiments.

Importance of Randomization

• Random assignment controls for individual differences.
• Non-equivalent groups can arise without randomization.

Types of Quasi-Experimental Designs

1. One-Group Post-Test Only Design

   • Simplistic, lacks a control group for comparison.
   • Vulnerable to threats like history, maturation, and testing.

2. One-Group Pre-Test Post-Test Design

   • Measures before and after treatment to provide a comparison.
   • Still vulnerable to confounds like history and testing.

3. Interrupted Time Series Design

   • Multiple measurements before and after treatment show trends.
   • Helps discern treatment effect but still vulnerable to long-term threats.

4. Non-Equivalent Control Group Designs

   • Groups formed without random assignment.
   • Vulnerable to selection bias and differential history.

5. Interrupted Time Series with Non-Equivalent Control Group

   • Combines time-series design with a control group.
   • Allows for pattern recognition before and after treatment.

6. Switching Replication Designs

   • Treatment administered to different groups at different times.
   • Examines if treatment effect is consistent across time and groups.

Threats to Internal Validity

• History: Events occurring during the study affect outcomes.
• Maturation: Natural changes in participants over time.
• Testing: Changes due to repeated testing.
• Instrumentation: Changes in measurement instruments.
• Regression to the Mean: Extreme scores tend to normalize.
• Attrition: Loss of participants over time.
• Selection: Bias in how groups are formed.

Program Evaluation

• Use of quasi-experimental designs to assess needs for social programs.
• Components include assessing needs, evaluating design and theory, process, outcomes, and efficiency.
• Challenges include constraints, costs, and ethical issues.

Key Takeaways

• Quasi-experimental designs are valuable when true experiments are not possible.
• They provide methodological approaches to study effects systematically.
• Various designs and their vulnerabilities need careful consideration.

• Note: Quasi-experimental designs, while not as robust as true experiments, are essential tools in research where randomization is infeasible. Understanding different designs and their applications can significantly enhance the quality of conclusions drawn from research studies.