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"But did it really work?" That's the question that I consistently hear across learning programs, courses, or initiative launches. We invest time, resources, and passion into crafting impactful learning experiences, but how do we definitively know if our interventions are yielding the desired outcomes? The truth is, without rigorous assessment built into the design, we're left with assumptions and anecdotal evidence.

Enter experimental design – a scientific toolkit that transforms our learning initiatives from hopeful endeavors into evidence-based strategies. It's about moving beyond "we think this works" to "we know this works," by systematically comparing different approaches to learning and isolating the impact of specific interventions.

In this article, we'll explore the fundamentals of experimental design, illustrate its application through three practical examples (a corequisite college math class, a new employee leadership course, and a revamped mentorship program), and address the common pushback encountered when advocating for this approach.

What is Experimental Design?

The core purpose of experimental design is to establish a clear cause-and-effect relationship between an intervention and its outcomes. To achieve this, it involves:

• Intervention (Treatment): The new learning method, program, or strategy being tested.
• Control Group: A group that doesn't receive the intervention, providing a baseline for comparison.
• Random Assignment: Participants are randomly allocated to either the intervention or control group, ensuring initial group similarity and minimizing bias.
• Measurement of Outcomes: Quantifiable metrics are used to assess learning outcomes for both groups.
• Comparison and Analysis: Statistical analysis is used to determine if there's a significant difference in outcomes between the intervention and control groups.

The power of experimental design lies in its ability to isolate the effect of the intervention. If the intervention group demonstrates significantly better outcomes, we can confidently attribute the improvement to the intervention.

Practical Examples:

Corequisite College Math Class: You've heard the buzz about flipped classrooms, where students watch lectures outside of class and tackle homework during class. You're curious if this model, coupled with integrating soft skills like collaboration, time management, and study skills, will boost student performance on standardized tests, improve course GPA, and increase retention. The math department decides to experiment, offering a larger-than-usual course section. They utilize two adjacent classrooms, and on the first day, students are randomly assigned to either the traditional model or the flipped classroom model. To mitigate teacher effects, they ensure both instructors have similar student ratings or rotate instructors between sections throughout the semester. By randomly assigning students to these different models, you can determine if this new approach truly impacts the desired outcomes.

Corporate Leadership Training: Your company is developing a new leadership development course for future leaders, aiming to improve upward mobility, employee retention, and 9-box placement scores. With 40 spots available, you want to ensure the program's effectiveness. You ask employees to self-nominate or be nominated by their leaders, aiming for at least 70 qualified applicants. From this pool, you randomly assign about half to the leadership course and the other half to a control group. While some suggest selecting the "best" applicants, random assignment ensures that potential for success is well distributed. You then track the intervention and control groups' performance on the defined KPIs six months and a year post-course. If, for instance, you find 9-box placement improves but not upward mobility or retention, you can use this data for continuous improvement, refining the course to target those specific KPIs, perhaps by adding networking opportunities. Crucially, connect these KPI changes to potential cost savings or avoidance. If retention improves, highlight how the program's cost might be offset by reduced employee turnover and the cost avoidance of that retention.?

Mentorship Program: Your business's mentorship program, with 200 mentor-mentee pairs, is performing well. However, you want to integrate wellness and belonging into the program to improve related scores on the semi-annual employee engagement survey. You hypothesize that adding wellness prompts to monthly check-ins will make a difference. To test this, you randomly assign half of the mentors to an intervention group and half to a control group. Each month, the intervention group receives check-in prompts with wellness components, while the control group receives the standard prompts. By comparing the engagement survey scores at the end of the year, controlling for demographic and other variables, you can determine if the new prompts significantly improved wellness scores. This demonstrates how cause-and-effect testing can be implemented with simple adjustments.

Addressing the Pushback:

Despite the potential benefits, experimental design often faces resistance. Here are two common objections and how to address them:

Ethical Concerns: Isn’t it unethical to deny some people access to a potentially beneficial treatment?

• If we definitely know that the new design will cause better outcomes, then we don’t need to use an experimental design. But, if you don't know if it has an effect or a large enough effect to implement at scale, then the purpose of an experimental design is to demonstrate whether or not the new intervention (program or redesign) causes better outcomes.?

Feasibility Concerns: It’s impossible to isolate cause and effect because there are too many variables that can influence learning outcomes.

• By using random assignment, an experimental design approach distributes variables across the groups, which minimizes the impact of those variables. Moreover, you should have a person skilled in data analysis assist with both design and analysis stages. They will be able to use statistical models that control for those variables. In other words, they can help remove any effects of known variables from the final results.

Conclusion:

Experimental design empowers us to move beyond assumptions and create evidence-based learning experiences. By embracing this approach, we can unlock the true potential of our learning initiatives and drive meaningful outcomes. Data analysis is key, and ensuring a skilled data analyst is involved is critical to the success of any experimental design.