Brief Update

Sorry followers!

With moving, traveling, dealing with a wrecked car, and finishing my dissertation, I’ve fallen behind on blogging. I miss it and I will start again soon! In the meantime, instead of sharing my usual impersonal blog posts where I share a tutorial or give tips on teaching or policy communication, I will share some photos from my trip. Hope you enjoy!

Glacier National Park
Sunrise at Glacier National Park


peter lougheed provincial park
From Peter Lougheed Provincial Park in Canada
lake louise
Lake Louise, Banff National Park
Morraine Lake
Moraine Lake, Banff National Park
Interesting halo effect of me taking a picture of the ice fields in Jasper National Park


Unplanned stop in Jasper
Near our unplanned campsite in Jasper–the road was closed because of a large car accident. Sixteen people lost their lives in Jasper that weekend…
Mochi enjoying the serene lake in Jasper National Park
Foggy day on the coast of Washington
Encounter with a banana slug
My closest encounter with a chipmunk at Crater Lake
Where the car broke down–i.e., middle of nowhere Nevada
The salt flats in Utah. The last picture before returning to Colorado.

Fact-checking Vancouver’s Swamp Drainers

Home: Free Sociology!

[co-authored with Jens von Bergmann and cross-posted with MountainMath]

Swampy facts: the dark, broken, and ugly side of housing talk in Vancouver.

Down south of the border, a politician who shall remain nameless campaigned on “draining the swamp” of Washington D.C., trafficked in countless conspiracies, and lied his way into office. His lies painted a picture of a United States turned dark, corrupt and menacing. He promised to fix it, Making American Great Again, mostly by shutting down globalization and kicking out the immigrants.

In Canada, we like to think we’re immune to this kind of rhetoric. But a strain has made its way into discussions concerning Vancouver, where the intersection of real estate, politics, and globalization are increasingly portrayed as a swamp in need of draining. We don’t believe most of those portraying Vancouver as swamp-like are intentionally lying (and in real life they surely favour the preservation…

View original post 1,995 more words

2018 World Population Day!

2018 World Population Day!

Happy World Population Day!

In case you’re unfamiliar with World Population Day, it started in 1989 by the Governing Council of the United Nations Development Programme. July 11th was chosen because in 1987, it marked the approximate date in which the world’s population reached 5 billion people. The purpose of World Population Day is to draw attention to issues related to the global population, including the implications of population growth on the environment, economic development, gender equality, education, poverty, and human rights. The latter issue is the theme celebrated this year. Specifically, family planning as a human right, as this year marks the 50th anniversary of the 1968 International Conference on Human Rights, where family planning was for the first time globally affirmed to be a human right.

This year, the world population is estimated to be around 7,632,819,325 people. If you want to see estimates of the global population in real time, you can visit the Worldometers website, which will also show other interesting estimates of population-related statistics, such as healthcare expenditures, energy consumption, and water use. If you’re interested in where they get their data and their methods, you can visit their FAQ section.

World Population #Rstats Edition

In celebration of World Population Day, I thought I would share an R program that pulls data from the Worldometers site:


and creates a world map that highlights the top 10 countries with the largest total populations:

The top 10 countries with the largest total populations is highlighted in dark green.

R code below:

#Load libraries
#Retrieve data:
html.global_pop <- read_html("")

#Create dataframe
df.global_pop_RAW <- html.global_pop %>%
  html_nodes("table") %>%
  extract2(1) %>%

#Check data

#Check for unnecessary spaces in values

#Check if country names match those in the map package
as.factor(df.global_pop_RAW$`Country (or dependency)`) %>% levels()

#Renaming countries to match how they are named in the package
df.global_pop_RAW$`Country (or dependency)` <- recode(df.global_pop_RAW$`Country (or dependency)`
                                   ,'U.S.' = 'USA'
                                   ,'U.K.' = 'UK')

#Convert population to numeric--you have to remove the "," before converting 
df.global_pop_RAW$`Population (2018)`<-as.numeric(as.vector(unlist(gsub(",", "",df.global_pop_RAW$`Population (2018)` ))))
sapply(df.global_pop_RAW,class) #Check that it worked

#Generate a world map
world_map<- map_data('world')

#Join map data with our data
map.world_joined <- left_join(world_map, df.global_pop_RAW, 
                              by = c('region' = 'Country (or dependency)'))

#Take only top 10 countries
df.global_pop10 <- df.global_pop_RAW %>%

#Printing to check

#Change data to numeric
df.global_pop10$`Population (2018)`<-as.numeric(as.vector(unlist(gsub(",", "",df.global_pop10$`Population (2018)` ))))

#Check if worked correctly

#Join map data to our data
map.world_joined2 <- left_join(world_map, df.global_pop10, 
                              by = c('region' = 'Country (or dependency)'))

#Create Flag to indicate that it will be colored in for the map
map.world_joined2 <- map.world_joined2 %>%
  mutate(tofill2 = ifelse(`#`), F, T))

#Now generate the map
ggplot() +
  geom_polygon(data = map.world_joined2, 
               aes(x = long, y = lat, group = group, fill = tofill2)) +
  scale_fill_manual(values = c("lightcyan2","darkturquoise")) +
  labs(title =  'Top 10 Countries with Largest populations (2018)'
       ,caption = "source:") +
  theme_minimal() +
  theme(text = element_text(family = "Gill Sans")
        ,plot.title = element_text(size = 16)
        ,plot.caption = element_text(size = 5)
        ,axis.text = element_blank()
        ,axis.title = element_blank()
        ,axis.ticks = element_blank()
        ,legend.position = "none"

Alternatively, you could include all the countries and use a gradient to indicate population size. However, China and India’s population is so large relative to other countries that it becomes difficult to see any real comparison.

#Generate map data (again)
world_map<- map_data('world')

#re-join with data
map.world_joined <- left_join(world_map, df.global_pop_RAW, 
                              by = c('region' = 'Country (or dependency)'))

#flag to fill ALL countries that match with the map package
map.world_joined <- map.world_joined %>%
  mutate(tofill = ifelse(`#`), F, T))

#Check that it worked correctly

#Then generate new map
ggplot(data = map.world_joined, aes(x = long, y = lat, group = group), color="white", size=.001) +
  geom_polygon(aes(x = long, y = lat, group = group, fill = `Population (2018)`)) +
  scale_fill_viridis(option = 'magma') +
  labs(title =  'Top 10 Countries with Largest populations'
       ,caption = "source:") +
  theme_minimal() +
  theme(text = element_text(family = "Gill Sans")
        ,plot.title = element_text(size = 18)
        ,plot.caption = element_text(size = 5)
        ,axis.text = element_blank()
        ,axis.title = element_blank()
        ,axis.ticks = element_blank()

Which should produce this map:
You can see that the other countries that made the top 10 list are not black, which reflects the smallest population sizes, but this map really just highlights how large China and India’s population are relative to the other countries.

More population data and viz

If you want to know more about the global population and how it has changed over time, here are some great resources:

Our World in Data— see also their estimates for future population growth

8 min PBS video

Hans Rosling Tedx video (10 min)

20 min Hans Rosling video –this uses the gapminder data I often code with in Python and in R

Kurzgsagt animated video (6.5 min)

If you’re interested in theories and analytical concepts of demography, here are some links to free online class material:

Johns Hopkins Demographic Methods –or here

Johns Hopkins Principles of Population Change



Plotly 3.0.0 in Jupyter Notebook

Plotly 3.0.0 in Jupyter Notebook 3.0.0 was recently released, and I finally got a chance to tinker with it! This is exciting because this release includes features that are specifically designed for Jupyter Notebooks. Namely, JavaScript is directly embedded in the figure that you can now access directly through your notebook. Exciting!

If you haven’t installed plotly or need to upgrade, open your Anaconda command prompt (as Administrator) and follow these directions. After you install plotly, launch Jupyter Notebook (by typing “Jupyter Notebook” into your Anaconda command prompt or by opening Jupyter Notebook using your computer menu). Next, enter your plotly username and api key in your notebook. You can sign up for plotly here. Directions for generating an api key here.

#first import plotly and provide username and api key
import plotly'UserName', api_key='XXXXX')

Now load the following:

import plotly.plotly as py
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import numpy as np
import pandas as pd

init_notebook_mode(connected=True) #tells the notebook to load figures in offline mode

Plotly should now work within your notebook.

Here’s an example of a 2D plot:

        {'x': x, 'y': y, 'type': 'histogram2dcontour'}

newplotExample of a 2D plot with markers:

x = np.random.randn(2000)
y = np.random.randn(2000)
iplot([go.Histogram2dContour(x=x, y=y, contours=dict(coloring='heatmap')),
       go.Scatter(x=x, y=y, mode='markers', marker=dict(color='white', size=3, opacity=0.3))], show_link=False)

newplot(1)Example of a 3D plot:

s = np.linspace(0, 2 * np.pi, 240)
t = np.linspace(0, np.pi, 240)
tGrid, sGrid = np.meshgrid(s, t)

r = 2 + np.sin(7 * sGrid + 5 * tGrid)  # r = 2 + sin(7s+5t)
x = r * np.cos(sGrid) * np.sin(tGrid)  # x = r*cos(s)*sin(t)
y = r * np.sin(sGrid) * np.sin(tGrid)  # y = r*sin(s)*sin(t)
z = r * np.cos(tGrid)                  # z = r*cos(t)

surface = go.Surface(x=x, y=y, z=z)
data = [surface]

layout = go.Layout(
    title='Parametric Plot',
            gridcolor='rgb(255, 255, 255)',
            zerolinecolor='rgb(255, 255, 255)',
            backgroundcolor='rgb(230, 230,230)'
            gridcolor='rgb(255, 255, 255)',
            zerolinecolor='rgb(255, 255, 255)',
            backgroundcolor='rgb(230, 230,230)'
            gridcolor='rgb(255, 255, 255)',
            zerolinecolor='rgb(255, 255, 255)',
            backgroundcolor='rgb(230, 230,230)'

fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='jupyter-parametric_plot')
Interact with it here

Lastly, an animated plot:

from plotly.offline import init_notebook_mode, iplot
from IPython.display import display, HTML


url = ''
dataset = pd.read_csv(url)

years = ['1952', '1962', '1967', '1972', '1977', '1982', '1987', '1992', '1997', '2002', '2007']

# make list of continents
continents = []
for continent in dataset['continent']:
    if continent not in continents:
# make figure
figure = {
    'data': [],
    'layout': {},
    'frames': []

# fill in most of layout
figure['layout']['xaxis'] = {'range': [30, 85], 'title': 'Life Expectancy'}
figure['layout']['yaxis'] = {'title': 'GDP per Capita', 'type': 'log'}
figure['layout']['hovermode'] = 'closest'
figure['layout']['sliders'] = {
    'args': [
        'transition', {
            'duration': 400,
            'easing': 'cubic-in-out'
    'initialValue': '1952',
    'plotlycommand': 'animate',
    'values': years,
    'visible': True
figure['layout']['updatemenus'] = [
        'buttons': [
                'args': [None, {'frame': {'duration': 500, 'redraw': False},
                         'fromcurrent': True, 'transition': {'duration': 300, 'easing': 'quadratic-in-out'}}],
                'label': 'Play',
                'method': 'animate'
                'args': [[None], {'frame': {'duration': 0, 'redraw': False}, 'mode': 'immediate',
                'transition': {'duration': 0}}],
                'label': 'Pause',
                'method': 'animate'
        'direction': 'left',
        'pad': {'r': 10, 't': 87},
        'showactive': False,
        'type': 'buttons',
        'x': 0.1,
        'xanchor': 'right',
        'y': 0,
        'yanchor': 'top'
#custom colors
custom_colors = {
    'Asia': 'rgb(171, 99, 250)',
    'Europe': 'rgb(230, 99, 250)',
    'Africa': 'rgb(99, 110, 250)',
    'Americas': 'rgb(25, 211, 243)',
    'Oceania': 'rgb(50, 170, 255)'
sliders_dict = {
    'active': 0,
    'yanchor': 'top',
    'xanchor': 'left',
    'currentvalue': {
        'font': {'size': 20},
        'prefix': 'Year:',
        'visible': True,
        'xanchor': 'right'
    'transition': {'duration': 300, 'easing': 'cubic-in-out'},
    'pad': {'b': 10, 't': 50},
    'len': 0.9,
    'x': 0.1,
    'y': 0,
    'steps': []

# make data
year = 1952
for continent in continents:
    dataset_by_year = dataset[dataset['year'] == year]
    dataset_by_year_and_cont = dataset_by_year[dataset_by_year['continent'] == continent]

    data_dict = {
        'x': list(dataset_by_year_and_cont['lifeExp']),
        'y': list(dataset_by_year_and_cont['gdpPercap']),
        'mode': 'markers',
        'text': list(dataset_by_year_and_cont['country']),
        'marker': {
            'sizemode': 'area',
            'sizeref': 200000,
            'size': list(dataset_by_year_and_cont['pop'])
        'name': continent
# make frames
for year in years:
    frame = {'data': [], 'name': str(year)}
    for continent in continents:
        dataset_by_year = dataset[dataset['year'] == int(year)]
        dataset_by_year_and_cont = dataset_by_year[dataset_by_year['continent'] == continent]

        data_dict = {
            'x': list(dataset_by_year_and_cont['lifeExp']),
            'y': list(dataset_by_year_and_cont['gdpPercap']),
            'mode': 'markers',
            'text': list(dataset_by_year_and_cont['country']),
            'marker': {
                'sizemode': 'area',
                'sizeref': 200000,
                'size': list(dataset_by_year_and_cont['pop'])
            'name': continent

    slider_step = {'args': [
        {'frame': {'duration': 300, 'redraw': False},
         'mode': 'immediate',
       'transition': {'duration': 300}}
     'label': year,
     'method': 'animate'}

figure['layout']['sliders'] = [sliders_dict]

Interact with it here

Neat, right?!


Overall, everything ran smoothly except the last plot. I actually initially tried to make this one:

From: (scroll to the bottom)

but I kept getting an error:


Update: Jon commented and pointed out that I was using an older version of plotly (3.0.0rc10) instead of 3.0.0rc11. You can check which version you have by typing the following:

import plotly

After I updated plotly, I successfully made the last graph!

Interact with it here

Special thanks to Jon! I sincerely appreciate your help!

Tips for Conversational Writing

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.


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?

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.


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.