Tips for Conversational Writing

 

In my two previous posts, I’ve been sharing some tidbits that I learned at the PRB Policy Communication Workshop. In my first post, I aimed to motivate you to think about the broader impacts of research, especially considering the unique role researchers play within the process of policy formation or change. In my second post, I discussed three different outlets–aside from academic journals–where researchers can share their findings with the public. This week, in my third and final post about policy communication, I will share some tips that I learned about conversational writing. Special thanks to Craig Storti for his enlightening presentation about some bad habits that I picked up in grad school!

Disclaimer: This blog post contains several cat puns. This may result in audible groaning and face-palming. Reader discretion is advised.

Academic Jargon and Dense Prose

It may seem obvious that we should avoid academic jargon when writing for non-technical audiences. As I said previously, abstract concepts such as macro- and micro-level processes or statistical methods are not well understood outside a specific discipline. We are also often told that we should stop using words such as ‘utilize’ when we could easily substitute ‘use.’ But even if we are acutely aware of these bad habits, here are two other occupational hazards that I did not consider before the workshop: 1) Nominalization and 2) Noun Compounds:

Nominalization is when we transform a verb into a noun. For example, nominalization  itself is a noun that was derived from a verb–i.e., ‘nominalize.’ Another example is the word ‘investigation’, which is from ‘investigate.’ Sentences that contain nominalized verbs can be weaker and less concise than sentences that use the actual verb.

A Noun Compound is when we use a consecutive string of two or more nouns in a sentence. For example, ‘Policy Communication Workshop Fellowship’ or ‘national community health operations research technical working group.’ Excessive use of noun compounds can result in dense writing that is difficult to understand.

Example

To demonstrate how easy it can be to both nominalize our verbs and string several nouns together, I wrote a hypothetical introduction to the cat meme inequality study<–noun compound!–that I used as an example in my previous post. Nominalizations are underlined; noun compounds are in red (excluding the phrase ‘cat meme’ alone); and jargon is in blue. Puns are italicized 🙂 :

Differences in purr household consumption of cat memes have been dramatically increasing over the past half-century, and research suggests that this growing disparity is due to incongrooment access to cat memes. Informed by this body of research, my study utilized data from the Cat Meme Survey of Households and Families and found that legislative pawlicies have, in part, catapulted these cat meme inequality access issues. Right meow, cat meme pawlicies are littered with supurrrfluous loopholes fur the rich and privileged. However, my research indicates that these catastrophic inequalities in cat meme access can be mitigated if pawlicymakers consider the implementation of laws or clawses that focus on the inadequacy of cat meme access fur more disadvantaged households through the creation of cat meme inclusion zones, which would allow fur the dissemination of more provisions fur those who are in need.

Tips for avoiding dense prose

The simplest way to avoid nominalizations is by restoring the verb. For instance, the first sentence of my example could be changed to “Rich households consume more cat memes than poor households…” Alternatively, the sentence could be changed to “Households are consuming cat memes at a different rate…” The latter example uses the gerund form of the verb.

The benefit of correcting nominalizations is that you will likely break up noun compounds, like I did in my first example:

Original: Differences in purr household consumption of cat memes have been dramatically increasing over the past half-century…

Corrected: Rich households consume more cat memes than poor households, which is a trend that has been increasing over the past half-century.

Another way to fix noun compounds is by including a preposition such as ‘of’, ‘in’, ‘to’, and ‘for’:

Original: However, my research indicates that these catastrophic inequalities in cat meme access can be mitigated if pawlicymakers consider the implementation of laws or clawses that focus on the inadequacy of cat meme access fur more disadvantaged households through the creation of cat meme inclusion zones, which would allow fur the dissemination of more provisions fur those who are in need.

Corrected: My research indicates that access to cat memes across households is inadequate. Policymakers should consider implementing laws that help more disadvantaged households gain access to cat memes. For example, by creating incentives to encourage builders and investors to provide more households with equal access to cat memes, or restricting builders and investors from accessing permits unless they agree to these terms, which is often referred to as “inclusionary zoning.”

It gets better with practice

I was surprised by how difficult it was to correct nominalizations and (especially) noun compounds at the workshop. I found that some of my resistance to removing noun compounds is that it can result in longer sentences. But unless I am writing for an academic journal, the value of writing more concisely is lost when my audience does not understand what I am writing about. It’s a skill that I will have to continue to practice and be more thoughtful about in the future. I encourage you to do the same!

 

Aiming Beyond Academic Journals: Where to share your research and what to consider.

Last week, I wrote about making your research more accessible to decision makers. I wanted to follow-up on this post and briefly cover three common mediums of public dissemination, at least among the academic circles that I am apart of: (1) Newspaper/Magazine articles; (2) Blogs; and (3) Policy Briefs. More about each outlet below:

Newspaper/Magazine articles: Publishing an article in a well-known magazine or newspaper is often a coveted achievement because of the level of exposure your research will receive. This will require careful and concise language, ranging between 700 to 1,000 words depending on the outlet. You will also need to come up with attention-grabbing headlines, and immediately open the article with your main message.

Carefully paying attention to your favorite articles is a great way to see this in practice. For example, here’s an article headline from the Washington Post that gets straight to the point: Antarctic ice loss has tripled in a decade. If that continues, we are in serious trouble. And this is the first sentence: “Antarctica’s ice sheet is melting at a rapidly increasing rate, now pouring more than 200 billion tons of ice into the ocean annually and raising sea levels a half-millimeter every year, a team of 80 scientists reported Wednesday.” The empirical study informing this Washington Post article is much more complicated. It focuses more on methods and the specifics of the researchers’ quantitative findings. The empirical article, as written, may not be well understood by non-technical audiences, but the findings and potential implications can still be highlighted such that any reader can understand what these scientists found and why it matters.

Note: op-eds are not the same as articles. They are about 750 words or less and they are based on your opinion; see this link and this link for tips on writing op-eds.

Blog Posts: Blogs are a great way of making your research accessible to niche audiences, and your article should be tailored according to their specific interests. However, make sure that your writing can still be easily understood by audiences who are unfamiliar with the general theme of the blog, keeping in mind that you want to increase readership. Posts should also be short, typically 500 to 800 words. Try to make the language conversational, meaning that you try to write like you speak, but be concise. Lastly, it’s always helpful to include visuals, such as photos or graphics. Graphics should be clearly explained or self-explanatory.

The topics and language featured in personal blogs is less strict, but I recommend writing professionally and cautiously, regardless. You never know who may read your blog and be offended, which may get you fired, set barriers for subsequent employment, and/or discredit you among important groups or decision makers.

My blog, for example, shares what I learn. I explain why I started this blog here. The benefit of sharing what I learn is that it forces me to clearly explain a topic or skill, which reinforces my learning and may help others who wish to learn the same thing. Plus, it invites feedback, which will help me to improve. I highly recommend it! Also, I have a running list of some of my favorite blogs here.

Policy Briefs: Policy briefs are typically aimed at policymakers or advocacy groups who are interested in a specific topic. These can be bit longer, typically 4 pages or less and between 1,500 to 2,000 words. It should provide a concise overview of a specific issue and recommendations for action. Make sure the recommendation is supported by credible research and identify who should perform this recommended action. Implications and recommendations should also be made in the introduction of the policy brief. For example, here’s a policy brief by PRB: Enhancing Family Planning Equity for Inclusive Economic Growth and Development. The implications and recommendations are highlighted in the last sentence of the first paragraph: “Lack of economic opportunity can produce multigenerational cycles of poverty, threaten social cohesion and stability, and even reduce economic competitiveness, but countries can achieve inclusive growth by implementing strategies that promote “broad-based expansion of economic opportunity and prosperity.” You can see that PRB clearly outlined what was at stake and also made a clear call to action. I recommend reviewing more policy briefs to see other examples.

 

The main takeaway is that when you look to share your research in different public outlet, make sure to consider the general format associated with publishing in through that medium. The format is very different from what I have a learned in grad school. It will definitely take a lot practice, but as an additional incentive, this kind of writing, especially the policy brief, is great for grants!

Stay tuned for an upcoming blog post on tips for effective communication for non-technical audiences!

Links to more resources:

UNC Writing Center: What is a policy brief

The Guardian on News Writing

Books by Craig Storti on Communicating Across Cultures

Inside Higher Ed: Communicating Research to a General Audience

 

 

Your research matters. Why not make it more accessible to others?

^image: 2018 Population Reference Bureau (PRB) Policy Communication Workshop Trainees at CRD in Washington DC

Last week, I was offered the opportunity to attend a workshop that prepares graduate students to influence policy and practice through effective communication. The workshop was held by the Population Reference Bureau (PRB), an influential nonprofit organization that specializes in demographic research domestically and internationally, in addition to teaching researchers and journalists more effective communication strategies. This week, I thought I would share a little bit about what I learned at the workshop because I hope to encourage others to think about the potential changes that they can enact through their research.

Policy changes are often incremental

As researchers, we are trained to be careful about what we say about our research, especially when making recommendations related to policies. At most, at least within my discipline, we simply suggest areas for future research. Because of this, the idea of changing policies can seem daunting and even uncomfortable, particularly if what you envision is large sweeping changes at the federal or state level. If this idea doesn’t intimidate you, then you can probably stop reading this blog post 😉 . For those of us who approach consequential actions like this with more hesitation, you should instead, think of these large policy changes as an overall goal, but know that small steps along the way are equally as important in achieving these goals. The smaller steps are also where we will likely have the greatest impact as junior researchers.

To make my point more clear, here’s an example–bear with me here because I want to avoid a political topic. Let’s say that your research has indicated that there is a large proportion of households that do not have access to cat memes. What an injustice right?! I mean, look!

Awwwwwwwwwwwwwwwwwwwwwww! I could do this all day.

Okay, but seriously. Simply researching cat meme inequality and saying that it is a problem does not guarantee that your research will get acknowledged by decision makers. Your senator, for example, would likely laugh at you if you walked up to them and said “We need to pass a bill about cat meme inequality!” And even if they took you seriously, they would probably ask about the evidence, what should be done to fix it, and how much it would cost. You need to be able to answer these questions or have a good response before approaching your senator. If you can do this, awesome! Call your senator right now. If not, you’ll need to do more groundwork to convince others to join your cause.

Instead, it may be more within your reach to (1) begin organizing meetings with a coalition of researchers who also believe that unequal access to cat memes is a societal problem. Once you have your group members, (2) the next step may be to identify other well-known organizations with fellow cat meme lovers who would like to begin holding conferences about the benefits of equal access to cat memes. Together, (3) you can draw attention to the issue and get more people to reach out to influential decision-makers to address cat meme inequality. This increased attention may even convince others to fund your project, which is often a necessary step in the process of enacting change. As a result, you may get a question about cat meme access added to a Census survey such as the American Community Survey. Congratulations! This is a policy change!

Also, thinking back to the bigger picture. Getting a question about cat meme access is important because data on cat meme access will allow you to provide credible benchmarks to policymakers, who can then set targets to reduce the prevalence of cat meme deserts by 30% over the next 5 years–which may be the overall goal.

Silliness of my example topic aside, this was loosely based on a case study in Bangladesh. Just substitute cat meme inequality with infant mortality. The example is also meant to demonstrate that these smaller changes are completely within reach. We don’t have to aim for federal level changes in laws that require companies to expand access to cat memes. You just need to consider doing the following:

1. Set a goal. Preferable one that you can measure. This way, you and others involved can determine if the goal has been met.
2. Identify who your goal will benefit and who can enact the changes. You also want to keep in mind those who will oppose you. Make sure you have strategies in place to either convince them to join your cause or at least minimize any roadblocks.
3. Figure out who can help you achieve your goal. People or organizations that can either help find/test solutions, fund the project, and/or draw attention to the issue.
4. Identify any windows of opportunity. Is there a summit or research conference that is covering your issue? Does your goal overlap with any nationally recognized goals–for example, the United Nations Millennium Development Goals. Are your policymakers voting on a bill that involves your goal(s)?

As I was told during the workshop, the point is that you should think about changing policies, but necessarily POLICIES. You can eventually enact POLICY changes if you prepare and strategically take advantage of opportunities that may arise, especially if you are working within a network that shares a similar vision and is equally passionate about making these changes.

Okay. Think small. Now what?

As researchers, we usually play an important role in creating credible evidence for a problem or solution. This helps to draw attention to the issue or at least helps to lay a groundwork of research that others can use to influence key decision makers. Knowing this, here are two things that we can consider to more effectively participate in this process:

Access. People need to have access to our research in order to do something about it. You can make your research more visible in many ways, including presentations, blogs, op-eds, social media, and newspaper or magazine articles. Publishing in peer-reviewed journals is great for building your credibility and the credibility of the issue, but most articles are behind a paywall. Even if they are freely accessible, academic writing is not well understood by a general audience. You should consider sharing your research through different mediums.

Clarity. Your audience also needs to understand your research when they have access to it. This means avoid technical jargon, such as statistical language, abstract concepts such as “macro- and micro-level,” and any terms that are mostly used within your discipline. Of course, the degree that you edit your language will depend on your audience. Always research your audience and tailor your message accordingly. Regardless, the simpler the language, the more accessible it is. Also, the easier your research is to understand, the more likely you are to keep your audience’s attention. I’ll admit, this is surprisingly difficult when put into practice. I plan to write another blog post about tips for writing more effectively for a broader audience next week.

Conclusion

There is a lot that goes into policy communication. In this post, I barely touched on the topic. My primary goal was to try and convince you that your research has the potential to make an impact, especially if you strategically think about where your expertise fits within the process of enacting change.

Your research is important! So, why not consider making it more accessible to a broader audience? Who knows. Maybe it will end up in the hands of someone who can actually do something about the topic that you are so passionate about.

 

 

 

 

 

Advice on Serving as a Conference Discussant

My advisor invited me to serve as the conference discussant for her session on education and health at the annual conferences of the Population Association of America, and I decided share what I did to prepare and my thoughts about the experience. This post is mostly geared toward junior-level discussants (i.e., graduate students or new professors). The approach will be different because, although no one likely wants to offend the presenters, as an early-career researcher, we are potentially in the most precarious position if we do happen to offend anyone with our comments.

Preparation:

The first thing you should do is check if the conference provides some guidelines for discussants. The PAA does provide guidelines. To briefly summarize here, the PAA states that the primarily role of the discussant is to  provide the audience with perspective and insight about the substance and significance of the papers, particularly by highlighting similar themes and emphasizing each paper’s individual contribution to the literature. They suggest reading the papers multiple times in order to note the weaknesses and strengths of the paper, while critically assessing the paper’s assumptions, methods, and the conclusions. Do not focus on too many specifics of the paper because the audience has not necessarily read the entire paper. The overall idea is to be constructive. Offer comments and suggestions that help the author improve their study, while also encouraging the audience to engage in a thoughtful discussion about the paper.

In addition to reading the suggested guidelines for discussants, conduct a search on the internet to see if you can find anything from previous discussants. The blogs that I found mostly gave generic suggestions. For example, “say more with less,” meaning don’t talk too long. Let the audience do most of the talking. I did not find this sort of advice helpful. The best advice I found was from Duck of Minerva. Although this post was written in 2007, I think he does a great job of offering more specific advice about what discussants should say. For example, he suggests that “sometimes [being a good discussant]… involves setting the papers in a broader disciplinary context so as to invite other parts of the discipline into the discussion.” He goes on to suggest however, that “sometimes it just involves going to town against a paper and demolishing its absurdity.” But he recognizes that this is not something a graduate student should do. I agree. Although, I also question if it would matter if a graduate student did this. First, we often lack the experience and insight to even see such shortfalls–therefore doing this would only make us look stupid. Second, this is not how you want to be received in the research community, even if you were brilliant enough to find huge deficits in their papers. Basically, don’t be an uppity early-researcher!

It may also be useful to find videos to view live examples of what others have done (e.g., example 1; example 2). I’ll admit that finding videos is surprisingly much more difficult. You will likely have to watch a lot of bad videos before you find one that will be relevant to your role. I was mainly looking for things like the seriousness of the discussant’s tone, how much time they spent summarizing versus critiquing/evaluating, and if they engaged with authors directly after they made comments or if they just left it up to the author to address the comments at the end. Some discussants also opened with discussing the broader state of the field.

My discussion:

After a couple day of conducting Google searches, I decided to open with some facts about the broader state of the field, emphasizing the importance of the research topic (Two sentences). I then briefly summarized the main purpose of each study and their main findings. I left it up to the authors to decide if they wanted to address my comments and suggestions, after I finished discussing their papers in the order that they presented–meaning I didn’t ask them direct questions. I mostly said something to the effect of, “it may be interesting to examine [enter specific topic here] in the future, given that the literature indicates [enter trend of finding here].” If I thought that something was not very clear, I said something like “the study could offer some more details on [the thing].” For example, one of the papers that I read did not offer much detail on their sample–like how they were sampled. Another author was not as clear as they could have been about the way they categorized a certain measure. I limited myself to pointing out two weaknesses and also tried to make two suggestions for future extensions of the study.

Also, at the suggestion of my advisor (who was the chair of the session), I also made a presentation. This is uncommon I think, but I didn’t want to go against the advice of my advisor. Because I included a presentation, I decided to have some fun with it and include animated gifs as my slides (see example below). I like to do this because I feel like it keeps the attention off of me.

Post-Discussion:

Overall, my research paid off and my discussion went well. If I had to do it again though, I wouldn’t have walked into the discussion with a two sentence introduction on the state of the field. I thought that it would a nice way to acknowledge the importance of the topic–i.e., their life’s research–but the audience didn’t seem very receptive to it. I think they already know how important their field is. I also wish I focused more on offering suggestions or interpretations for unexpected findings. I hesitated to do this because this is not my research area. Audience members, however, were happy to do this, regardless of whether or not this is their field.

Responding to the article, “Why Are Data Science Leaders Running for the Exit?”

I have some objections to an article that was written by guest contributor Edward Chenard and posted on Data Science Central. The purpose of his opinion piece is to present an argument for why Data Science Leaders are leaving the field. He makes three points: (1) Academia can’t do for-profit, (2) Wrong Expectations, and (3) Bad Methods. I don’t have much of an opinion on the last two claims. I mainly take issue with his second point. I would also like more evidence that supports the issue that inspired his article. Full disclaimer: I’m a graduate student.

With respect to why he is writing this article: Drawing from anecdotes, Mr. Chenard states that he knows “a lot of people currently running data science teams at large organizations and the vast majority of them…want to leave their jobs.” While I don’t doubt that he has had these conversations, surveys don’t seem to support his observation. For example—assuming that Data Science Leaders consider themselves Data Scientists, although Mr. Chenard is not clear about the particularly job title he is referring to—Glassdoor ranks Data Scientist as the number 1 job in America, as measured by median salary, job satisfaction, and labor opportunity. In fact, Data Scientist has been ranked the number 1 job for the past 3 consecutive years.

I can’t find evidence that supports his point, which is that data scientists are unsatisfied with their jobs. There is certainly some evidence that indicates that there is volatility in this field, but we cannot assume that this is because these employees are dissatisfied. For example, it could be that these individuals may have decided to return to school (or plan to return to school in the near future) or that they started working in a tech firm that failed or cut employees, which is quite common for tech startups (e.g., Forbes 2015). Despite this, in support of his claim, I did find an article in the Financial Times that states, “According to Kaggle’s survey, most people working in the field spend 1-2 hours a week looking for a new job.” Most is not an objective number that can be debated, however, and the article did not link to the source, so there is no way to investigate what the author meant by “most.” The statistics that the article does go on to report, undermine Mr. Chenard’s argument. The article goes on to say that, based on Stack Overflow data (n=64,000 developers), machine learning specialists top the list (at 14.3%) of employees that reported looking for a new job. Data scientist were second on the list, at 13.2%. This is far from “a lot.” The evidence that I found therefore suggests that most data scientists are in fact, satisfied with their jobs, which makes the premise of his article a bit dubious.

But that isn’t the main issue that I have with Mr. Chenard’s post. I would like to dispute his second claim, which is that data scientists with PhDs (who possess an academic mindset) “can be more of a liability than an asset,” particularly when “your drive is profits and customer satisfaction.” I’m not sure who he is referring to when he says “your,” which I assume to mean the employer. The statement suggests that academics are somehow less adept at both considering profit margins and customers satisfaction. Furthermore, with the disclaimer that he doesn’t have a PhD, he argues that the type of work PhD students excel in isn’t useful in the private market. This is a bold and ridiculous claim. I believe that Mr. Chenard is misattributing what he believes to be one of the primary reasons for the “mass exodus” from data scientists, which is the elevation or privileging of Data Scientist (Leaders) who have PhDs by employers. Although I do not know why employers may be doing this, I’m sure they have their (well-researched) reasons.

To respond to this argument: I’m not sure why Mr. Chenard solely targets PhDs over someone with a Master’s degree or a Bachelor’s. What is the threshold? When does more education become a liability? He does not make this clear. Regardless of his point. I disagree. Graduate students are dedicated and disciplined workers. Indeed, general statistics on the positive relationship between higher levels of education and positive outcomes, such as longevity, income, and cognitive ability, support that broadly speaking, more education generally results in positive returns. (The argument has more to do with selection, but that’s neither here nor there.)

With respect to data science, I concede that not all PhDs would be spectacular in Data Science, just as not every person looking for a job would excel as a Data Scientist. But just considering the type of graduate student who may be interested in data science (e.g., those who study statistic; computer science; physics; economists; demographers; sociologists), I think that it’s ridiculous to make such strong claims about their characteristics and their ability to excel in a particular field. Consider the two main arguments that he made: inability or limited ability to consider profits and customer satisfaction. All graduate students are skilled researchers, meaning that they are trained to critically consider all aspects of projects, including budgets. As someone who has written for grants (and successfully won them), writing a grant proposal—which is a lot like a business proposal—requires carefully budgeting out the research project, which includes things like calculating the cost of data collection, compensating researchers and/or research participants, and data storage. Grant writing is a vital part of conducting research. In many ways, a lot of our careers depend on it, which is why I think it’s outrageous that Mr. Chenard is suggesting that PhDs are somehow bad at considering the monetary aspects of managing projects and teams. Also, side note: graduate students are generally poor! Learning to carefully manage money is a critical part of earning a PhD. Therefore, I’m pretty confident in saying that most graduate students are probably very capable of managing profits, at least as effectively—if not more—than a data scientist without a PhD, in an apples-to-apples comparison.

As for dealing with customers, graduate students have to deal with some of the most critical and cantankerous “customers” one could ever deal with, such as highly opinionated researchers, policymakers with an agenda, national and international grant institutions, university deans, and of course, college students and their guardians. These groups absolutely “consume” our products, and we are obliged to consider their satisfaction of our projects. In that respect, graduate students and those who have earned their PhDs certainly know how to respond to customers. I think what Mr. Chenard may be conflating is any observed differences that result from the knowledge accrued while working in a particular field. Sure, someone who has been working in industry rather than spending the last few years earning their PhD will of course be more familiar with handling “customers” that consume their products. However, they likely learned this skill over time on the job. Someone with a PhD will likely also gain or sharpen these skills, over time on the job. Training in academia does not hinder this ability, and one cannot assume so.