Why Is A Between-Subjects Design Preferred For Footwear Performance Testing? Key Benefits For Material Integrity

The primary reason Between-subjects design is preferred for footwear material testing is its ability to serve as a high-integrity "anti-interference" control. By ensuring each participant evaluates only one specific material or design scenario, the study eliminates the risk of participants making direct comparisons that skew data. This approach protects the independence of each evaluation point, ensuring the results reflect the material’s actual performance rather than the user's psychological or physical reaction to previous trials.

The Core Takeaway: Between-subjects design preserves the technical validity of performance testing by isolating the variable of the material itself. It removes the confounding variables of physical fatigue and cognitive bias that naturally occur when a single user tests multiple footwear options.

Eliminating the Interference of Direct Comparison

Preventing Intent Guessing

In a within-subjects design, a participant tries multiple shoes and often begins to guess the research goal. This "guessing" leads to artificial logical biases, where the participant may consciously or subconsciously adjust their feedback to be "helpful" or consistent with what they think the researcher wants.

Ensuring Data Independence

Testing only one material per person ensures that every data point is independent. This is critical for safety and performance footwear, where the material properties must be evaluated based on genuine user response rather than a comparative preference.

Managing the Physical Variables of Testing

Mitigating Fatigue Effects

Physical performance testing often involves repetitive tasks that can lead to fatigue effects. If a participant tests Material A and then Material B, their physical state during the second test is fundamentally different, potentially leading to inaccurate performance data for the second material.

Removing Order Effects

The sequence in which materials are tested can drastically change a user’s perception. By using a Between-subjects design, you eliminate order effects, where the first material tested sets a baseline that unfairly influences the perception of all subsequent materials.

Understanding the Trade-offs

The Requirement for Larger Sample Sizes

The most significant limitation of a Between-subjects design is that it requires a larger number of participants. Because each person only tests one variable, you need more individuals to achieve statistical power compared to a within-subjects design where one person generates multiple data points.

Accounting for Individual Variability

In this design, individual differences (such as foot shape, gait, or weight) are not naturally controlled within the person. This requires rigorous screening and randomization to ensure that variations in the data are truly caused by the footwear material and not by the inherent differences between the participants in Group A versus Group B.

Making the Right Choice for Your Goal

When deciding on a testing framework, your choice should align with the specific rigor required for your footwear development phase.

If your primary focus is objective material validation: Utilize a Between-subjects design to ensure that data points are independent and free from the bias of comparative testing.
If your primary focus is rapid, early-stage prototyping: A Within-subjects design may be acceptable to gather quick, subjective feedback from a small internal team, provided you acknowledge the risk of fatigue.
If your primary focus is identifying subtle performance deltas: Stick to a Between-subjects design with a large, randomized sample to ensure that the noise of individual differences does not drown out the signal of the material properties.

Selecting the right experimental design ensures that your footwear performance data is a reflection of the material's engineering rather than the participant's psychology.

Summary Table:

Feature	Between-subjects Design	Within-subjects Design
Data Independence	High (No cross-contamination)	Low (Direct comparison bias)
Fatigue Factor	Minimal (One test per person)	High (Order effects/exhaustion)
Participant Guessing	Low (Blind to other variables)	High (May guess research goals)
Sample Size Req.	Large (More participants needed)	Small (One person tests all)
Primary Use Case	Rigorous Material Validation	Early-stage Prototyping

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References

Volker Lingnau, Florian Beham. The link between corporate sustainability and willingness to invest: new evidence from the field of ethical investments. DOI: 10.1007/s00187-022-00340-z

This article is also based on technical information from 3515 Knowledge Base .