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Final steps for module #4: Teacher response to module drafts

The difference between a hypothesis and  a research question

The main thing I’m seeing in the drafts that you need to work on is how to frame a good hypothesis. Quantitative researchers use hypotheses to frame the assumptions that they are trying to assess. So, they typically have a hypothesis as well as a research question. At the same time, however, you don’t need to include a hypothesis in this module. You can if you want to. You do need to be clear, however, what assumptions your study is based on, however.

To use an example from one draft:

Missing in the literature is research that explores the potential and tangible deliverables that technical communicators could provide for community partners via service learning assignments. Administrators and instructors need specific details and information for “the scope of audiences, document types, rhetorical purposes, content, styles and outcomes” to understand why service learning supports superior professional development for students
(Henson & Sutliff, 1998). These deliverables obviously evolve with technology and the demands of the nonprofit sector, therefore the research is inherently ongoing. That said, it is information that is of critical importance for robust preparation of the workforce and to understand the impact of the field of TPC at large (McEachern, 2011). Technical writers have much to offer the greater good, and with the goal of balancing “social conscience with technical learning” in mind, more research will lead to a better understanding of those offerings (Sapp & Crabtree, 2002).

Research Questions
RQ1: What types of technical documents and or deliverables do community organizations utilize to execute their missions?

RQ2: What is the scope of the audience(s) for which a community organization’s technical documents are written?

RQ3: What obstacles prevent community organizations from producing or procuring the technical documents they need to execute their missions?

RQ4: Of the technical documents and deliverables utilized by community organizations, for which ones would they prefer to be trained to produce in-house?

It’s very clear what this researcher wants to study and also the assumptions behind why they are studying this. So, there needs to be a prefatory paragraph somewhere in your study that explains, in laymen’s terms, the so-what behind your study.

We need to know:

  • Briefly, what is the state of knowledge leading up to this? What do we know and what don’t we know?
  • Why is the thing you are trying to assess, the central variable you’ve identified, important for researchers to understand more about?
  • Why will your methods help you collect the type of data that will help you assess this variable?

In qualitative research, you can often adopt a more grounded approach that uses fewer assumptions about your target population. In quantitative research, you have to make certain assumptions in the form of a hypothesis or educated guess based on past research. Again, this has more to do with what a quantitative instrument can detect. You have to be more careful as a quantitative researcher regarding what you are detecting and how.

If it helps you, just come up with a hypothesis, or testable statement on your research.

In the above example, for instance, you could say:

Hypothesis 1: Non-profits experience numerous obstacles when attempting to document their internal processes.

Hypothesis 2: These obstacles prevent non-profits from engaging in effective technical communication practice.

These are testable because they can be either true or false. The obstacles either exist or they don’t. They either prevent effective practice or they don/t The researcher has a hunch the obstacles exist and that these obstacles prevent effective technical communication. The researcher may find the obstacles don’t exist, however, or that the non-profits are overcoming them, which means there’s a reason why this research is being conducted: to see if the hypotheses are true or not.

Whether you use a full-blown hypothesis or not, it’s useful to think on these terms when dealing with quantitative research designs: what assumptions are you trying to test out? How will you know if those assumptions are correct?

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