I was fussing around with my RSS reader today, cleaning out the blogs that haven’t posted in a year or that never really caught my interest. I realized that the end result might be of interest to folks who are wondering whether there are any good marketing research, data, statistics, charts, neuroscience, etc blogs out there. The answer is yes! Enjoy!
Association for Survey Computing
Blog – Experts in Qualitative Research
Data Science 101
Acuity Eyetracking Blog
The Ad Contrarian
Statistical Modeling, Causal Inference, and Social Science
Voices of CMB: The Chadwick Martin Bailey Research Blog
The Visual Linguist
GfK Insights Blog
Joel Rubinson on Marketing Research
Market Research Blog
MRII | Marketing Research Institute International
MSW Research Blog
Qualitative Research Cafe
BPS Research Digest
BRIAN F. SINGH
Brian Juicer Blog
Business Over Broadway
Chandoo.org – Learn Excel & Charting Online
Chief Customer Officer 2.0
Cogs & Marvel
Data ColadaData Colada
FiveThirtyEightFiveThirtyEight | Politics
I Love Charts
Numbers Rule Your World
KL Communications, Inc.
Learn and Teach Statistics and Operations Research
The LoveStats Blog
LRW Blog – Turning Insight into Impact
Marketing Research Association
Math with Bad Drawings
My Research Rants
Not awful and boring examples for teaching statistics and research methods
Sampling and choosing cases in qualitative research: a realist approach
Research and Reflect
Research Design Review
SayWhat Consumer Research
SmartData Collective – The World’s Best Thinkers on Data
Sociological Methods & Research recent issues
The Advertising Research Foundation
This is Statistics
YouGov US Opinion Center News
Tom Fishburne: Marketoonist
Touchstone Research Blog
Latest blog entries
Kantar.com News Feed
Latest Research from ABI Research
Adweek : Advertising Branding
The Market Research Blog from B2B International
Blog – Behavioraleconomics.com | The BE Hub
Big Data Made Simple
Branding Strategy Insider
Data Science Association blogs
Featured Posts – DataViz
DC’s Improbable Science
E3S European Sensory Science Society
ESOMAR – News
Everyday Sociology Blog
Putting people first
All Gallup Headlines
G&R: Advertising Research & Consulting
Information Marketing Association
UX Daily – User Experience Daily
Interactive Video Productions
Ipsos Knowledge and Ideas
MRS What’s New
Naftali Harris: Statistician, Hacker, and Climber
NatCen Social Research
Paul Long’s Market Research Blog
Pew Research Center » Topics » Social Media
Pew Research Center
Predictive Analytics Times
PRS IN VIVO
Q Research Solutions
Forum: Qualitative Social Research
Research – Government affairs
Research – Latest news
Market Research Training from Research Rockstar
Research Through Gaming
Ruby Cha Cha
Sentient Decision Science
Social Media Research Foundation
StatsLife – Significance magazine
SurveyGizmo – Online Surveys, Polls, & Landing Pages
The Analysis Factor
The insights guy.
Versta Research Blog
Vocal Laboratories Inc. blogs
Vox – Science of Everyday Life
Polling: Political Polls & Surveys – The Washington Post
Datafloq Read Blog Posts
Customer Experience Matters®
Insights about Insights
The Lipstick Economy
New Research Speakers Club
Random Domain Intercept Technology | RIWI
Sweet Insight Blog
Branding Strategy Insider
The IMPACT Blog
People For Research
The entirety of this post is available on the Gender Avenger website.
Why are women underrepresented as speakers?
I’ve pondered this question for years but I never knew if my hypothesis was grounded in fact or in stereotype. Fortunately, or unfortunately as the case may be, the opportunity presented itself and here we are pondering real data from a survey I did of 297 male and 252 female computer or data scientists, and market researchers aged 25 to 49 — people who ought to be on their way to securing spots on the conference circuit.
One of the questions in the survey asked people to imagine speaking at an event and to choose any attributes that would describe themselves as a conference speaker. I was careful to include an equal number of both positive and negative attributes so as to avoid leading people to choose a greater percentage of positive (or negative) items.
Curious how men and women viewed thselves? I know you are. Read the entirety of this post on the Gender Avenger website. If you’re braver enough.
Live note-taking from Viz-Fest, November 2016. Any errors are my own.
Tom Schlak, E-Tabs, Data Viz for the Visually Impaired
- Most important information visually displayed to achieve objectives and can be monitored at a glance, Charts, graphs, icons, mostly visual
- How can data visualization be made accessible to someone who is blind
- Viewing a musical score versus listening to a score, “seeing” a chart can’t be replicated
- What are the alternatives, Maybe use speech assistive technologies
- 5% of the world has some visual impairment, 11.5% for people over 50 years of age
- Good designs benefit everyone even fully sighted users
- Text and fonts – font size on screen nothing less than 12 to 16 points, Helvetica, Arial
- Font styles more complicated, must consider legibility and readability, legibility is individual characters, readability is overall appearance including spacing and formatting, good fonts have better spacing, less fancy script, less detailed, filled lettering, less heavy, less thin, less serif, Use display fonts carefully because they aren’t accessible
- Contrast – sometimes corporate colours are a problem but readability must come first, use an online tool like adobe color CC, let’s you choose colors that match your target color or complement, matters for typography too, forget white on yellow, yellow on red, grey on green
- Clean user experience – avoid cluttered dashboard, eliminate non-data pixels like background images and watermarks, large logos, etc, think about data-to-ink ratio, avoid dark gridlines or over-labeling, don’t fill up backgrounds just because you can, gradients of grey can be difficult to differentiate, use colors like red and green appropriately, only highlight what must be highlighted, flatten the design by removing shadows and 3D elements that make a flat image look shiny
- Eg Microsoft logo went from shiny colored circle to four simple boxes
- What about color deficiency, blindness – 8% of men and .5% of women have deficiencies, the worst forms can distinguish 20 hues but people without can see about 100 hues
- Avoid red green charts as this can hide the data
- Traffic light indicators are common, red green yellow, and should be used with caution, consider using shapes as well, arrows, checkmarks, faces, and maybe use them only for one state, just the positives or just the negatives
- Use cross hatching or fill patterns so you don’t rely just on color
- Use Colblinder online to test colors, light colors can lose their differentiation, text labels will help, try also ColorBrewer online
- Much of this applies to good design anyway
- Is it still beautiful is someone can’t see it? Communicate to everyone
Marta Blankenberger, redaviZ, Data Driven Infographics
- Infographic is visual representation of data to share information quickly and clearly
- Need to think about position, size, shape, color
- Icons help us understand and interpret information
- Combine a doughnut chart with an icon, or combine a bubble chart with a map behind it
- Masking – cover or uncover part of a chart but placing a shape in front of it, try that with charts, grey out a section or tone down the color of part of it
- Combine icons, put stars over circles to mask different areas
- Try using an arrow as the bars in a bar chart, just paste the image into the bar
- Try putting a grey bar behind the bars to represent the maximum of the bar
- Try using copied icons as the bar (e.g., 5 stars, 8 stars, 3 stars), you can use masking to turn 5 out of 10 into 5.3 out of 10
- Use conditional formatting of data labels, change the color/shape/size of labels if they are less than a specified value, or put checkmarks as the data point
If you’re a grammar grinch, you probably suffered a slight heart attack upon reading that title. It’s a grammar problem that plagues researchers to no end. In fact, it’s a problem that really isn’t a problem as I learned recently.
Below you’ll find the rule as well as a link to the original source with nine other rules you’ve been getting wrong all along. Enjoy!
8. Treating “data” as singular instead of plural: Remember what I said about Latin screwing with your life? “Data” is a word that makes lots of people unhappy. It comes from the Latin word “datum,” a second declension neuter noun that becomes “data” in the nominative and accusative plural. (Latin has different plurals for different parts of speech.) We’ve inherited a lot of Latin plurals, and many of them we no longer treat as plural: for example, we say “the agenda is” rather than “the agendas are” and “opera” is not the plural of “opus” in English.
In some cases, using “data” as plural is legitimately useful. You’re more likely to encounter “data” as plural in scientific and mathematical writing where you might talk about collecting each individual datum. My 2007 copy of the AP Stylebook uses “The data have been collected,” as an example of a sentence where “data” is being treated as a group of individual items. In that case, “data” is being treated as what we call a “count noun.”
While some style guides will recommend always using data as plural, in daily speech we frequently use data as what’s called a “mass noun,” meaning it has no natural boundary, no individual units that we can count. Charles Carson, managing editor of the journal American Speech, uses “butter” as an example of a mass noun. Sure, you can talk about pats of butter or cups of butter, but when you talk about just butter, you say, “How much butter is in the pie crust?” When using data as a mass noun, it is perfectly standard English to treat it as grammatically singular.
Carson employs this handy rule of thumb:
If you wish to use data as a singular mass noun, you should be able to replace it in the sentence with the word information, which is also a mass noun. For example,
Much of this information is useless because of its lack of specifics.
If, however, you want to or need to use data as a plural count noun, you should be able to replace it with the word facts, which is also a plural count noun. For example,
Many of these facts are useless because of their lack of specifics.
O’Conner deems treating data as a grammatical plural a dead rule, writing, “No plural form is necessary, and the old singular, datum, can be left to the Romans.” She also argues that media should be treated as singular when referring to mass communication and as plural only when referring to individual types of communication.
It’s possible that I’ve attended too many conferences in the last few years as I have witnessed more terrible presentations than I would have ever wanted. If you are eager to make it to the top of my WORST PRESENTATION EVER list, here are a few tips to follow.
- Dress to impress. Pick out your crappiest jeans and throw on a wrinkled shirt. This will show everyone that you’re far too important to care how you look at such an inconsequential event like this.
- Do a sound check as soon as you step on stage to begin your talk. This is necessary because the sound team generally forgets to monitor the sound of speakers and they need you to remind them.
- Stand directly behind the podium with your hands firmly clasped to the edge. This way, you will appear in complete control of the podium. Your power and importance will be obvious. And, you will be perfectly positioned with your face hidden behind the microphone .
- Read your speech. Everyone knows that grammar is important. By reading your speech, you will be assured that no one can judge you for misusing a verb tense or uttering an incomplete sentence. Grammar nazis are everywhere.
- Mention your company name not once, not twice, but at least 20 times. People won’t know which company to rush over to and shake their money at if you don’t remind them every 30 seconds. Say things like, “At Company A, we believe that…” and “We used our own high quality research panel, Panel Awesomeness, to conduct this research.”
- Reference your work with as many important people and companies you can. Some people call this name dropping but they’re just jealous. They know that it’s proof you are highly skilled. Specifically, mention a project you plan to conduct with Stan or Diane or Pinterest or Apple. Be sure to refer to people casually so we think you are personal friends with them, and not just picked out from the article you read this morning.
- Use a laser pointer to highlight points that should have been obvious without a laser pointer. Because lasers are cool.
- Let people know that you aren’t good with numbers and your data guy can get back to them if need be. It’s good to show you understand your own weaknesses especially if you don’t want to bother to improve them.
- Be sure to choose good colours in your prezzie. Focus on complementary colours such as red font on green background or yellow font on blue background. They aren’t called complementary for nothing!
- Make sure to use 12 point font. Anyone who can’t read your prezzie from the back of the conference room is just too stupid to move to the front of the room and doesn’t deserve to read it anyways.
- Put equations on every page. It makes you look really smart so it doesn’t matter if people can’t read them due to fonts and layout.
- Don’t show any data. People aren’t concerned with details and they’ll believe everything you say anyways. Besides, numbers are hard to understand. [Insert whiny voice here.]
- Put clip art on every page. It doesn’t matter if you couldn’t find a picture that actually demonstrated the point. People love pictures!
- How women should ask for a raise if they don’t want to follow Microsoft’s CEO advice of Trust Karma (lovestats.wordpress.com)
- Interesting infographic: How your brain sees a logo (lovestats.wordpress.com)
- Missing Data: Whose problem is it anyways? (web.peanutlabs.com)
Enough already. I’m tired of presenters complaining that we show too much data in presentations and I’m tired of research users saying there are too much data in reports.
Data is massively important.
Without data, we would not be able to draw any conclusions. Without data, we would not understand consumers. Without data, researchers would not be able to independently determine whether they agree with someone else’s conclusions. Without data, there is no debate, no difference of opinion, no opportunity to become more comfortable using data, no opportunity to teach others about the use of data.
Data is not the issue. Data presentation is the issue. We need to learn how to choose the data points that best demonstrate the point we wish to make. And, we need to learn how to choose the chart that best presents that point. We need to stop choosing the first chart in Excel and instead choose the best chart, the best colours, the best formatting, the best labeling, and more. Easy and quick is not best. It’s lazy.
As part of your next presentation, INCLUDE DATA. For every single point you want to make. Write out a clear description of the point. Show me a clear representation of the data in a picture. And if you want to appeal to even more people, show me a audio visual component that brings the point to life. Give me all the factors I need to decide for myself whether I believe you. I’m not stupid and I don’t think you’re God. But give me all the pieces I need and I’ll figure it out for myself, perhaps come to the same conclusion as you, and then be impressed with your data.
We’ve gotten over the “DIY sucks” and realized that “Unskilled researchers” are the real problem.
Now it’s time to get over “Data sucks” and realize that “Poor data presentation” is the real problem.
- WAPOR Day 3: Margin of Error is too complicated to understand #AAPOR #MRX (lovestats.wordpress.com)
- When is a relationship not a relationship? #MRX (lovestats.wordpress.com)
- What Do Regression Models Indicate? #MRX (lovestats.wordpress.com)
I just returned from two of the best marketing research conferences out there, ESOMAR and WAPOR, and was flipping through the notebook of rants and raves that I create as I listen to speakers. Interestingly, even at these conferences, where the best of the best speak, I heard a certain phrase repeatedly.
“The regression model indicated…”
“The data indicated…”
“The results indicated…”
Well you know what? The data indicated absolutely nothing. Zip. Zilch. Zero.
Data is data. Numbers in a table. Points in a chart. Pretty diagrams and statistical output.
The only thing that indicated anything is you. YOU looked at the data and the statistical output and interpreted it based on your limited or extensive skills, knowledge, and experience. If I were to review your data, My skills, knowledge, and experience might say that it indicates something completely different.
Data are objective and indicate nothing. Take responsibility for your own interpretations.
Welcome to Really Simple Statistics (RSS). There are lots of places online where you can ponder over the minute details of complicated equations but very few places that make statistics understandable to everyone. I won’t explain exceptions to the rule or special cases here. Let’s just get comfortable with the fundamentals.
Today we tackle another kind of number. Unlike nominal numbers, ordinal numbers have real meaning behind them. The name itself hints at the meaning. Ordinal numbers portray ordered numbers.
But, the only thing we know about the numbers is that there is an order to them. For example, there are more cookies in the first picture than there are in the second. But, we can’t see the whole picture, so we don’t know how many more cookies are in the first picture. We could assign a a 2 to the first picture and a 1 to the second picture, but we wouldn’t be able to say that there are twice as many cookies in the first picture. Just that there are more. Here are some examples of ordinal data.
- A big handful of rice vs a small handful of rice. Why: We don’t know how much rice is in each hand but we can see there is more in one than the other.
- Someone who is a bit shy vs someone who is really shy. Why: We don’t how much more shy the really shy person is, but we know they are more shy.
- Questions on surveys where the answers look like: Strongly agree, somewhat agree, somewhat disagree, strongly disagree. Why: We don’t know how much more “strongly” is compared to “somewhat” but we do know it’s more.
- This is more than that. This is lighter than that. This is heavier than that. This is taller than that. This is bluer than that. This is tastier than that. This feels more rough than that. This smells worse than that. This is longer than that. This is earlier than that. This is faster than that.
- Something is more or less than the other thing
- We don’t know how much more or less it is
The scientist inside each of us demands factual, solid, indisputable proof. Facts are golden truths that tell us which brands people buy, how often they buy them, and where they buy them. Facts come from scientific research that abides by established validated methods.
But facts are boring. We often already know the facts but we do our research to confirm them. We already know that girls like Hello Kitty and boys like Transformers but the research must be done. So when crazy people come along and make decisions without regard for research findings or without doing any research at all, we are appalled.
In fact, I don’t believe that decisions without research are possible. Even companies that “do no research” (I’m talking to you Steve Jobs) are constantly doing research. They’re just using a different methodology.
Their datasets are decades of experience with real consumers, thousands of sales figures tied to market events, intuition applied to raw data, opinions based on boring datatables. All of these things are simply alternate forms of insight tools analyzed and interpreted by insight generators, people. They just happen to be insight tools that survey researchers dream about but rarely have access to.
So don’t be fooled. The next time someone says they didn’t do any research before coming to their conclusions, think about what datasets they did have the privilege of using.
- Surprise, surprise! A non-rep sample is as good as a ‘rep’ sample (lovestats.wordpress.com)
- Mobile Research Conference 2011 – Take Aways (natashaallden.wordpress.com)
- Statistics Speak Louder Than The Estimations! (seodoz.com)
[tweetmeme source=”lovestats” only_single=false]
Well, once you manage to catch your breath after laughing solid for 4 minutes, let’s really think about all the people involved in this little prank.
1: Interviewer: First of all, this interviewer deserves a raise, a bonus, and a promotion for going through this interview without laughing, getting upset, or antagonizing the survey responder. I’m sure he deals with this sort of thing, whether real or fake, all day long every day. And yet, the utmost professionalism on his part. Kudos for a great job.
2: Responder: How did our industry get to such a state where surveys are written so poorly that people leave a tape recorder at their telephone waiting for researchers to call in order to make fun of them? This is nothing for us to be proud of.
3: Data Analyst: How exactly is the data analyst going to handle data which is clearly horrible quality? Will the analyst think of checking for outliers in each question? Will the analyst review the entire set of responses to recognize that it is an across the board outlier and probably a troublemaker? Will these responses lead to completely invalid analysis and conclusions?
4: Survey Author: Of course, we understand the need to use standardized questions in surveys. But, no matter how convinced you are, the world does not consist of people who know how surveys work. There are absolutely people out there who need to be taken through a survey with far more care than what we
permit when writing surveys. Telephone surveys need to be written so that interviewers can speak naturally and help those people who actually need some help. That’s where good data comes from. I’m really curious if the survey author left a place for the interviewer to indicate that this instance was possibly an outlier.
So, enjoy. But the next time you write a survey, keep this in mind. Are you antagonizing yet another survey responder or are you responsible for creating a more positive market research experience?