Feasibility of Research Questions

The Three Pillars of Achievability

A research question is achievable only if the plan to answer it is feasible. For your students, this is where theoretical statistical knowledge meets the practical limitations of the real world. We can break down achievability into three main considerations:

1. Data Availability & Access: Can we get the necessary data? This involves two key challenges:

   • Sample Size: Is the target population large enough to yield a statistically meaningful sample?
   • Recruitment: Can we actually find and persuade individuals from our target population to participate in the study?

2. Resources: Do we have the necessary means to conduct the study? This includes:

   • Financial: Funding for participant compensation, equipment, or software.
   • Technical: Access to specialized tools (like 3D scanners) or specific materials.
   • Personnel: The availability of researchers or assistants with the required skills (e.g., trained bra fitters, interviewers skilled in sensitive topics).

3. Time: Is the proposed timeline for data collection, analysis, and reporting realistic? A longitudinal study tracking participants for a year has very different time constraints than a one-off cross-sectional survey.

Let's apply this framework to our research questions.

Application 1: Achievability in Bra Design Research

Consider our bra design question. It is specific and its variables are measurable. But is it achievable?

• Data Availability: The primary challenge is finding a sufficient number of women who identify with or can be classified as having an "omega breast shape." This is a niche population. Before launching a full study, a pilot study would be essential to estimate the prevalence of this shape and test recruitment strategies.
• Resources: This study requires trained personnel who can perform both the standard sizing formula and the BRT method accurately and consistently. It also requires the physical underwires and materials to conduct the fittings.
• Time: The data collection for each participant might be relatively quick (an initial fitting session), but recruitment could take months.

The following paper, while focused on "new senior women," provides a superb case study in the practicalities of conducting research on a very specific demographic for bra design. It gives us a window into how to assess achievability.

This paper offers several valuable insights into achievability:

• Niche Sample Recruitment is Possible: The researchers successfully recruited a sample for their target group: 42 adult women in their 50s who wear a size 80A. This demonstrates that even with very specific criteria, recruitment is achievable, though it often results in modest sample sizes. This informs us that our study on "omega shapes" would likely involve a similar small-to-medium sample size, which must be factored into the planned statistical analysis.
• Technical Constraints Define Feasibility: The discussion of the 80% reduction rate is critical. The authors didn't just design a bra; they had to balance the competing goals of aesthetic shaping (which requires higher pressure) and comfort (which requires lower pressure). They leaned on previous research to determine that an 80% reduction was optimal. This is a perfect example of a resource/technical constraint. For our question, this means we must ask: Is the BRT method a well-documented, repeatable technique? Do we have the resources to apply it correctly? The achievability of our study depends on a "yes" to those questions.
• From Virtual to Reality: The study shows the process of moving from a 3D virtual model to a physical, commercial-grade product. This confirms the practical achievability of producing custom garments for a research study, provided the right expertise and technology are available.

Application 2: Achievability in Relationship Research

Now let's evaluate our second question on polyamorous triads.

• Data Availability: The central challenge is recruitment. Our question specifies a non-hierarchical triad with a particular gender configuration (one female, two males). This is a highly specific subgroup within an already niche population. Furthermore, the stigma surrounding non-monogamy can make people hesitant to participate in research.
• Resources: The primary resource here is skilled personnel. Researchers must be able to build rapport and trust to collect honest data about sensitive topics like communication and intimacy. Financial incentives may also be necessary to encourage participation, especially if the study requires a significant time commitment.
• Time: A simple correlational study could be done cross-sectionally. However, to draw more meaningful conclusions about causality (i.e., do date nights cause an increase in satisfaction?), a longitudinal study tracking the triads over several months would be required. This would dramatically increase the time and cost, potentially making it unachievable for a small-scale project.

So, is it even possible to study such a group? The following paper provides an answer by showing how researchers estimated the prevalence of polyamory in the U.S., giving us a baseline for assessing data availability.

This research provides crucial evidence for achievability:

• The Population is Accessible: The study found that 1 out of 9 people (10.7%) have engaged in polyamory at some point. While this is a minority, it is a substantial number, comparable to the percentage of the US population holding a graduate degree. This suggests that the population, while niche, is large enough to be studied. Our question's focus on a specific triad formation means our pool is much smaller, but not necessarily zero.
• Strategic Recruitment is Key: The authors did not simply post an ad on a social media site. They used a professional service (ResearchNow) to access large, pre-vetted research panels and deliberately oversampled certain demographics. This shows that achieving a good sample in a hard-to-reach population requires a deliberate, well-resourced strategy. It's not a matter of luck; it's a matter of planning and budget.
• Prevalence Informs Scope: Knowing that roughly 10% of people have experience with polyamory allows a researcher to make a "back of the envelope" calculation. If we need 50 triads for our study, and we assume our specific 1F/2M structure is, say, 1% of all polyamorous configurations, we can start to estimate how many thousands of people we would need to screen to find our sample. This calculation is fundamental to determining if a project is achievable within a given budget and timeline.

Conclusion

In this lesson, we moved from the abstract world of question-crafting to the practical realities of research execution. We've established that the 'A' for Achievable is a critical checkpoint in the SMART framework.

Key Takeaways:

• Achievability is a Practical Filter: It forces us to confront the real-world constraints of data availability, resources, and time. A question can be perfectly specific and measurable but fail this test.
• Assess Niche Populations: For studies involving specific groups (like women with omega-shaped breasts or people in 1F/2M triads), the first step is to investigate their prevalence and the feasibility of recruitment. Existing research can provide crucial clues.
• Plan Your Resources: Achievability is often a direct function of your resources. Having a bigger budget, more time, or access to specialized tools and personnel can make a previously unachievable question achievable.

Preview of the Next Lesson:

We now have questions that are Specific, Measurable, and (plausibly) Achievable. But one final question remains before we begin collecting data: So what? In our next lesson, we will tackle the 'R' in SMART: Relevant. We will explore how to ensure that a research question, even if well-formed and achievable, is actually relevant to the broader research objective, business goal, or field of study.