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Final Steps for the Final Project: Teacher Response to Module #5

Make sure you read your individual feedback for Module #5 on Canvas as you revise for the final project. Almost everyone has suggested revisions.

Identifying a Research Gap for an External Audience: Make It Simple

When identifying a research gap in a proposal, you need to explain that gap in laymen’s terms. You need to start from where the audience is, which is that of the “educated non-specialist.” So, the person reading your proposal (i.e. a funder, departmental colleague, or administrator) might not even know your field exists. Or if they do, they know nothing about it.

So: you have to start from that place with your research gap. Explain why communication is important to healthcare. Don’t assume the audience will understand that. Explain why communication is important to fostering a diverse classroom. Explain, explain, explain.


The field of technical communication (TC) and the role of the technical professional communicator (TPC) would appear to experience some indecision from what the academic requirements to TPC research are, to TPC in practice, to an overall inability to validate the role by officially defining the work or career (Henning & Berner, 2016; Jones et al. 2016; Spilka, 2002). In citing Turner and Rainey (2004), Baehr (2015) presents early “core competencies” which included advocacy as one of eight competencies to assist in defining the role (p 106) while Durá (2018) challenges approaches to advocacy in technical communication and designs an asset-map in how to implement participatory advocacy efforts as a TPC for several health, education, and business areas.
Back in 2011, the technical communicator was made official by the US Department of Labor’s Occupational Outlook Handbook (OOH) (Henning & Bemer, 2016). The career description for a technical communicator is described the same as for a technical writer and in lacking distinction, as well as definition, the TC then lacks legitimacy and power. Moreover, the responsibilities of a TPC as defined by the U.S. Department of Labor are not those of the graduated or certified TPC. Several principles guide instruction and engage students in service learning and introduce basic principles of social justice, yet there is often a disconnect between the academic fulfillment, hiring expectations of a company or an organization and its mission or vision messaging (Moeller, 2018). I have claimed that the literature on advocacy in the professional field is thin, and that principally it is linked to efforts of social justice within pedagogy and research. Further, because I understand advocacy can happen in any political leaning advocacy is not social justice in a leftist leaning sense. I came up with the idea because I was challenged by the undefined role versus seeing the role as flexible or versatile. The data has the potential to inform the Department of Labor to rewrite the position description for the TPC and managers could better attract graduates to positions familiar to curricular practice and service-learning endeavors. Ultimately the results from this mixed-methods study will exemplify social justice in that the responses come from TPC participants and could compliment the original survey Baehr (2015) generously explores.

What Theory Is: Working Assumptions Described Through Central Terms

Where the rubber really meets the road of this explanation is in your theory/methodology section. A theory in relation to empirical reseach is a series of working assumptions, often expressed through key terms. So, if your central term is multiculturalism, you need to define that term and how it impacts communication. Again, you need to explain this in terms that anyone with a Ph.D. can understand.

In order to make your central term(s) apparent, you need to explain what assumptions you’re drawing from and what sources those assumptions are drawn from.

See below how the following author frames the assumptions of their research as an issue of technological literacy:

Technological Literacy

Within the existing body of literature, there is limited direct exploration of the technology in demand for employers. More often, software knowledge and tools are addressed as a single unit or, as with Lanier, by subcategories rather than specific software titles. As with the tension between the TPC practitioner’s positioning a Jack of all trades or SME, so too the literature holds up two separate arguments about technology use. One side of the argument is that no specific software titles have emerged as definitive tools for the industry (Blythe, Lauer, & Curran, 2014) or even for specific genres (Kimball 2015 and Shalamova, Rice-Bailey, and Wikoff). The other
side of the argument (Lanier, 2009; Carnegie & Crane, 2018, Mallette & Gehrke, 2019) identifes specific titles and speculates that some of those have claimed enough market share to be, for all
intents and purposes, the go-to software. Lanier cautions, however, that even though software titles may fall under the same domain, they often have distinct purposes. Using the specific graphic design and document publishing software that Lanier references (p. 59), we see that
– Microsoft Visio is vector-based graphics software for diagramming. Adobe PhotoShop is raster graphics software that “has become the industry standard not only in raster graphics editing, but in digital art as a whole.”
– Adobe FrameMaker is a document processor designed for writing and editing large or complex documents, including structured documents. Adobe InDesign is a desktop publishing and page layout designing software used primarily by graphics designers
and production artists. Adobe Acrobat is used to view, create, manipulate, print and manage PDF files.

Other public attempts to guide TPC practitioners to the best software choices have their own problems. The Bureau of Labor Statistics entry for Technical Communication makes
generalizations. O*NET, which has been referenced in research used to develop TPC curricula (Carnegie & Crane, 2018), may not prove the most reliable source despite reportedly reliable methods, as evidenced by their inclusion of Adobe Flash as a tool currently in-demand by TPC employers 1 (National Center for O*NET Development).

Feel Free To Ask Me Questions

I’ll be checking the website and my email through 12/8, of course. If you have questions about my comments, please ask.

Final Steps for Final Project

The following must be posted to Canvas by midnight on Friday, 12/8/21:

  1. Cover Letter, which should answer the following questions a) what have you learned about the processes of conducting a sound research study in this class? c) what have these processes taught you about your future roles as a technical and professional communicator?
  2. A copy of your final Research Proposal

This Is Teaching


If you did, please please evaluate me.

ECU has a strict policy that our evaluations don’t count in our favor unless we get a 60% response rate. If you enjoyed this class and want to reward teachers like me, the best thing you can do is evaluate us.

If you didn’t enjoy this class, you should also evaluate me so I know why :).

FYI, I can also see my response rate, so I’ll keep reminding you until I get to 60% :).

Where to Find the Evaluations

You should’ve received an email for all of your classes, reminding you to evaluate your instructors.

You can also log-in to PiratePort and search for “Student Survey.”


Grades on Canvas.

Speaking the language of your audience

Research proposals have to reach an audience, and often an important one. You present a proposal to your Master’s or Ph.D. adviser, your boss, or a potential funder. The goal of any proposal is to sell this decision maker on your research, that: it’s necessary, it’s a good idea, and you are equipped to do it.

This means that jargon is a bad idea, unless you know for certain the audience will understand it, which is always risky. Here’s a link to a Plain Language resource on jargon to help you avoid it:

Solid precedents

A lot of people who are new to research proposals think that presenting something that is entirely new is a good idea. This is rarely the case. If no one has ever done a research project like yours, chances are you’re going too far out on a limb. Either that, or you haven’t found other research that is like yours.

Precedents are research studies that you’re building on, and they are important. Mine your literature review for birds of a feather: researchers that you want to imitate. Showing that you are aware of other studies like yours also shows that you’re building your study on a solid foundation, rather than creating an entirely new methodology that may or may not work

Solid research design

Along with precedents, you need to make sure that the overall design of your study conforms to best practices. These best practices can be found in your precedents. They are the things that experienced researchers do with studies like yours. Look especially to the ways that more experienced craft research questions, explain their own precedents, and talk about how they used their research methods to collect and analyze data and . These three areas are the ones that new researchers tend to have the most difficulty explaining properly.

Connecting Module #4 to Module #5: Teacher Response to Module #4

Your grades are on Canvas, so check for them there.

I also provide individualized feedback on modules that connect what you individually did to what you need to do in the next module, so be sure to check on that as well.

Now you know two ways of doing research; make them one

Now that you’ve been introduced to both qualitative and quantitative methods, you need to think beyond them. As I’ve mentioned before, researchers who just think of themselves as one of these methods worry me, because I think they’re treating methods as a way to produce findings, rather than as ways to think through complex research problems.

I fell into this trap early on in this class by describing myself as more “qualitative.” As I thought back though, there’s not a single study I’ve done which didn’t involve some quantitative component, whether that was during my data collection, data analysis, or even how I connected one study to the next. Both qualitative and quantitative methods have rich traditions that can be useful in variety of studies. Exploit them both regularly to become the best researcher you can be.

Communicate why you’re doing what you’re doing

In light of the above, you may employ quantitative methods, qualitative methods, or a mix of the two (called “mixed methods” research) in your Research Proposal draft for Module #5. The goal of a proposal is always communication to a decision-making audience, however: you need to translate what you intend to do in a coherent way that is free from jargon and that demonstrates sound research skills.

To do so: think of justifying everything you’re doing for the toughest critic you can think of. That voice in your head that always makes you doubt yourself? Give that voice free reign. Let it critique every procedure, every source, every sentence. Then respond to that critique to make your proposal unassailable.

The feedback you receive from myself and your peers as part of Module #5 will help you do this.

Content design internship at ibm


An industry partner forwarded this internship opportunity:


In joining our content experience team, you will collaborate with fellow content creators, subject matter experts, developers, designers, architects, and offering managers to deliver high-quality content in the IBM Cloud user interface, product documentation, and beyond.

Job Responsibilities

  • Deliver accurate customer-facing content on time and with high quality. This might include embedded assistance, documentation, blog posts, videos, tutorials, or API reference docs.
  • Work closely with development, design, marketing, and offering management supporting the design and delivery of new features

If you’re interested in breaking into industry, this is an excellent way to get started. Feel free to reach out to me if interested.

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?

Connecting Module #3 to Module #4: Teacher Response to Module #3

Your grades are on Canvas, so check for them there.

I also provide individualized feedback on modules that connect what you individually did to what you need to do in the next module, so be sure to check on that as well.

Ask yourself the “so what?” question

Whenever you create a research instrument, it should arise naturally from questions you are trying to answer. It should provide a natural means of answering those questions, in other words.

This isn’t always what happens, though. It’s easy to think of methods as solutions to research questions. Have a question? Through a survey or interview at it? But why that method? How does that method help you collect data that will be useful to answering your questions?

Answering that last question starts with asking the broader question of why people should care about what you’re researching. This is the “so what” question that every researcher should ask themselves when beginning a new inquiry. And the answer should never be: because you think it’s important. It has to be because you think it’s important to other people.

Don’t think in (quantitative or qualitative) silos

Another trap is to fall into the “I’m this kind of researcher” mindset. I just did that in my post about Homework #4. It’s easy to do.

The danger in doing so is that you start thinking like a robot. Again: you’re putting the method before the inquiry. Just because you’re unfamiliar with a method, doesn’t mean it isn’t exactly you need for a current inquiry. Similarly: you may be the best ethnographer in the world, but maybe ethnography isn’t going to be useful in the current inquiry.

Answering questions, questions that provide solutions to problems, should always be the fundamental aim of every researcher. This is especially true when those questions are hard to answer and when their attendant problems seem unsolvable. That’s where good research comes from: the “wicked problems” that can never be definitely solved.