A Math Myth?

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I just finished reading The Math Myth: And Other STEM Delusions by Andrew Hacker. I found the book to be so provocative and interesting that it merits the first ever book review on this blog.

The central thesis of the book is that in the US, we (meaning policy makers, educators, parents, and employers) have become obsessed with raising rigor and academic standards in math. This obsession has reached a point where we are convinced that our national security, international business competitiveness, and hegemony as an economic power rides on improving the math skills of all our high school and college graduates.

Hacker questions this national fixation. First, raising math standards has some serious costs. Not only has it caused significant disruption within schools and among educators and parents (ask any educator about the upheaval the Common Core has caused), but it has also cost significant money. But, most importantly, Hacker makes a strong case that raising of math standards has ensured that many students will be left behind and unprepared for the future.

Currently, about one in four high school students does not complete high school. Once enrolled in college, only a bit more than half of enrollees will graduate. While there are many reasons for these failures, Hacker points out that the chief ACADEMIC reason is math.

I think everyone can think of someone who struggled mightily in math. I personally took Calculus in high school and two further courses in college. I have often wondered why. It seemed to be more of a rite of passage than an academic pursuit with any realistic end in mind for me. It was certainly painful.

Math has humbled many a bright young person. I have a niece who was an outstanding high school student (an honors student, took multiple AP courses, etc.). She went to a reputable four-year college. In her first year at college, she failed a required math course in Calculus. This remains the only course she had gotten below a B in during her entire academic life. Her college-mandated math experience made her feel like a failure and reconsider whether she belonged in college. Fortunately for her she had good supports in place and succeeded in her second go round at the course. Many others are not so lucky.

And to what end? My niece has ended up in a quantitative field and is succeeding nicely. Yet, I doubt she has ever had to calculate the area under a curve, run a derivative, or understand a differential equation.

The reality is very few people do. Hacker, using Bureau of Labor Statistics data, estimates that about 5% of the US workforce currently uses math beyond basic arithmetic in their jobs. This means that only about 1 in 20 of our students will need to know basic algebra or beyond in their employment. 95% will do just fine with the math that most people master by the end of 8th grade.

And, despite the focus on STEM education, Hacker uses BLS data to show that the number of engineering jobs in the US is projected to grow at a slower rate than the economy as a whole. In addition, despite claims by policy makers that there is a dearth of qualified engineers, real wages for engineers have been falling and not rising, implying that supply is exceeding demand.

Yet, our high school standards and college entry standards require a mastery of not just algebra, but also geometry and trigonometry.

Most two-year colleges have a math test that all incoming students must pass – regardless of the program of study they intend to follow. As anyone who has worked with community colleges can attest to, remediation of math skills for incoming students is a major issue two-year institutions face. Hacker questions this. Why, for example, should a student intending to study cosmetology need to master algebra? When is the last time your haircutter needed to understand how to factor a polynomial?

The problem lies in what the requirement that all students master advanced math skills does to people’s lives unnecessarily. Many aspiring cosmetologists won’t pass this test and won’t end up enrolling in the program and will have to find new careers because they cannot get licensed. What interest does this serve?

Market research is a quantitative field. Perhaps not as much as engineering and sciences, but our field is focused on numbers and statistics and making sense of them. However, in about 30 years of working with researchers and hiring them, I can tell you that I have not once encountered a single researcher who doesn’t have the technical math background necessary to succeed. In fact, I’d say that most of the researchers I’ve known have mastered the math necessary for our field by the time they entered high school.

However, I have encountered many researchers who do not have the interpretive skills needed to draw insights from the data sets we gather. And, I’d say that MOST of the researchers I have encountered cannot write well and cannot communicate findings effectively to their clients.

Hacker calls these skills “numeracy” and advocates strongly for them. Numeracy skills are what the vast majority of our graduates truly need to master.  These are practical numerical skills, beyond the life skills that we are often concerned about (e.g. understanding the impact of debt, how compound interest works, how to establish a family budget).  Numeracy (which requires basic arithmetic skills) is making sense of the world by using numbers, and being able to critically understand the increasing amount of numerical data that we are exposed to.

Again, I have worked with researchers who have advanced skills in Calculus and multivariate statistical methods, yet have few skills in numeracy. Can you look at some basic cross-tabs and tell a story? Can you be presented with a marketing situation and think of how we can use research to gather data to make a decision more informed? These skills, rather than advanced mathematical or statistical skills, are what are truly valued in our field. If you are in our field for long, you’ll noticed that the true stars of the field (and the people being paid the most) are rarely the math and statistical jedis – they tend to be the people who have mastered both numeracy and communication.

This isn’t the first time our country has become obsessed with STEM achievement. I can think of three phases in the past century where we’ve become similarly single-minded about education. The first was the launch of Sputnik in 1957.This caused a near panic in the US that we were falling behind the Soviets and our educational system changed significantly as a result. The second was the release of the Coleman Report in 1966.This report criticized the way schools are funded and, based on a massive study, concluded that spending additional money on education did not necessarily create greater achievement. It once again produced a near-panic that our schools were not keeping up, and many educational reforms were made. The third “shock” came in the form of A Nation at Risk, which was published during the Gen X era in 1983. This governmental report basically stated that our nation’s schools were failing. Panicked policy makers responded with reforms, perhaps the most important being that the federal government started taking on an activist role in education. We now have the “Common Core Era” – which, if you take a long view, can be seen as history repeating itself.

Throughout all of these shocks, the American economy thrived. While other economies have become more competitive, for some reason we have come to believe that if we can just get more graduates that understand differential equations, we’ll somehow be able to embark on a second American century.

Many of the criticisms Hacker levies towards math have parallels in other subjects. Yes, I am in a highly quantitative field and I haven’t had to know what a quadratic equation is since I was 16 years old. But, I also haven’t had to conjugate French verbs, analyze Shakespearean sonnets, write poetry, or know what Shay’s Rebellion was all about. We study many things that don’t end up being directly applicable to our careers or day-to-day lives. That is part of becoming a well-rounded person and an intelligent citizen. There is nothing wrong with learning for the sake of learning.

However, there are differences in math. Failure to progress sufficiently in math prevents movement forward in our academic system – and prevents pursuit of formal education in fields that don’t require these skills. We don’t stop people from becoming welders, hair-cutters, or auto mechanics because they can’t grasp the nuances of literature, speak a foreign language, or have knowledge of US History. But, if they don’t know algebra, we don’t let them enroll in these programs.

This is in no way a criticism of the need to encourage capable students from studying advanced math. As we can all attest to whenever we drive over a bridge, drive a car, use social media, or receive medical treatment, having incredible engineers is essential to the quality of our life. We should all want the 5% of the workforce that needs advanced math skills to be as well trained as possible.Our future world depends on them. Fortunately, the academic world is set up for them and rewards them.

But, we do have to think of alternative educational paths for the significant number of young people who will, at some point, find math to be a stumbling block to their future.

I highly recommend reading this book. Even if you do not agree with its premise or conclusions, it is a good example of how we need to think critically about our public policy declarations and the unintended consequences they can cause.

If you don’t have the time or inclination to read the entire book, Hacker wrote an editorial for the NY Times that eventually spawned the book. It is linked below.

Is Algebra Necessary?

 

How to Be a Good Research Supplier

Crux Logo Final 2016

A while back, we posted “How to Be a Good Research Client” to help clients understand the makings of a successful partnership from the supplier perspective. Here, we’d like to do the opposite: advise suppliers on how to position for success with their clients.

Being an outstanding supplier goes beyond the technical abilities of understanding statistics, experimental design, business, and marketing. There are many researchers who have these skills, but are not great suppliers. They are necessary, but not sufficient skills.

It starts with empathy – a good supplier will understand not just the business situation the client is facing, but also the internal pressures he/she faces. We’ve found over time that suppliers who have spent time as clients themselves understand what happens to projects after the final presentation in a way that many suppliers just cannot.

So, here goes:  Our 10 tips on how to be a good research supplier.

  1. Begin by seeking out the right clients. There is simply too much pressure, especially at larger research firms, to take on every project that comes your way. It really helps if you have guidelines as to which clients you will accept: which ones match with your skills in a unique way, are doing things you are genuinely interested in studying, and have individuals who are good project managers.
  2. Be honest about what you are good at and not so good at. Research isn’t quite like law or medicine where every task seems to devolve to a specialty, but there are specialties. You are not good at everything and neither is your firm. Once you realize this, you can concentrate on where you provide unique value.
  3. Understand what is at stake. Some market research projects influence how millions of dollars are spent. Still others are a part of a substantial initiative within a company. Somewhere, there is somebody whose career hinges on the success of this initiative. While the research project might come and go in a matter of months to you as a supplier and be one of a dozen you are working on, the success of the project might make or break someone’s career. It is good to never lose sight of that.
  4. Price projects to be profitable. You should price projects to make a strong profit for your firm and then not waiver easily on price. Why? Because then you can put all thoughts of profitability out of your mind at the onset and focus on delivering a great project. Never, and we mean never, take on an unprofitable project because of the prospects of further projects coming down the road. It doesn’t serve you or your client well.
  5. Don’t nickel and dime clients. They will ask for things you didn’t bid on or anticipate. Not everything they need will be foreseeable. An extra banner table. A second presentation. A few extra interviews. Follow ups you didn’t expect. Just do it and don’t look back. Larger research firms are prone to charging for every little thing the client asks. After a while, the client stops asking and moves onto another firm. Projects can be expensive. Nickle and diming your clients for small requests is about as frustrating as buying a new car and having the dealer charge you extra to put floor mats in it.
  6. Never be the one your client is waiting on. If there is one rule here that we feel we have mastered at Crux it is this one. There are a lot of moving parts in a project. You often need things from clients and they need things from you along the way. Never be the one people are waiting on. Stay late if you have to, come in early, work from home … do anything but be the “rate limiting factor” on a project. Your clients will love you for it.
  7. Be around “forever” for follow ups. We have seen suppliers put in contracts that they are available for 3 months from when the project is over for follow up discussions. Why? We love it when clients call years after a project is over as it means the project is still having an influence on their business. Be there as long as it takes for them.
  8. Be human. It took us a little time to learn this one. We used to be very workmanlike and professional around clients to the point of being a bit “stiff.” Then we realized clients want to work with people who are professional about the task at hand, but also fun to be around, and well, human. Granted, they don’t want to hear about every stress of your personal life, but relax a little and be who you are. If that doesn’t work out well for you, you might not be in the right career.
  9. Make them feel like they are your only client. You might have a dozen projects on your plate, family commitments tugging at you, coworkers driving you crazy, and a myriad of other things competing for your time. Time management isn’t easy in a deadline driven field such as research. But it also isn’t your client’s problem. They should feel like you have nothing else to do all day but work on their project. The focus needs to be on them, and not your time management. When you are late for a meeting because you had another run over, you are telling your client they aren’t your number one priority.
  10. Follow up. The project might be over for you, but it lives on longer for your client.  Be sure to follow up a few weeks down the line to see if there is anything else you can do. You’ll be surprised at how often the next project comes up during this conversation.

Congratulations to Truth Initiative!

Congratulations again to our client, Truth Initiative!  Last week Truth won 4 Effies and 2 Big Apple awards for its anti-tobacco campaigns.

Read more about these wins here:  truth campaign grabs 4 effies, 2 big apple awards.

Cause Change. Be Changed.

Congratulations to Causewave Community Partners on their successful annual celebration last week.  It was a sellout!

This video does a great job of capturing what the organization is a about and the value of volunteering.  It also includes a cameo from Lisa on our staff!

 

Asking about gender and sexual orientation on surveys

When composing questionnaires, there are times when the simplest of questions have to adjust to fit the times. Questions we draft become catalysts for larger discussions. That has been the case with what was once the most basic of all questions – asking a respondent for their gender.

This is probably the most commonly asked question in the history of survey research. And it seems basic – we typically just ask:

  • Are you… male or female?

Or, if we are working with younger respondents, we ask:

  • Are you … a boy or a girl?

The question is almost never refused and I’ve never seen any research to suggest this is anything other than a highly reliable measure.

Simple, right?

But, we are in the midst of an important shift in the social norms towards alternative gender classifications. Traditionally, meaning up until a couple of years ago, if we wanted to classify homosexual respondents we wouldn’t come right out and ask the question, for fear that it would be refused or be found to be an offensive question for many respondents. Instead, we would tend to ask respondents to check off a list of causes that they support. If they chose “gay rights”, we would then go ahead and ask if they were gay or straight. Perhaps this was too politically correct, but it was an effective way to classify respondents in a way that wasn’t likely to offend.

We no longer ask it that way. We still ask if the respondent is male or female, but we follow up to ask if they are heterosexual, lesbian, gay, bisexual, transgender, etc.

We recently completed a study among 4-year college students where we posed this question.  Results were as follows:

  • Heterosexual = 81%
  • Bisexual = 8%
  • Lesbian = 3%
  • Gay = 2%
  • Transgender = 1%
  • Other = 2%
  • Refused to answer = 3%

First, it should be noted that 3% refused to answer is less than the 4% that refused to answer the race/ethnicity question on the same survey.  Conclusion:  asking today’s college students about sexual orientation is less sensitive than asking them about their race/ethnicity.

Second, it is more important than ever to ask this question. These data show that about 1 in 5 college students identify as NOT being heterosexual. Researchers need to start viewing these students as a segment, just as we do age or race. This is the reality of the Millennial market:  they are more likely to self-identify as not being heterosexual and more likely to be accepting of alternative lifestyles. Failure to understand this group results in a failure to truly understand the generation.

We have had three different clients ask us if we should start asking this question younger – to high school or middle school students. For now, we are advising against it unless the study has clear objectives that point to a need. Our reasoning for this is not that we feel the kids will find the question to be offensive, but that their parents and educators (whom we are often reliant on to be able to survey minors) might. We think that will change over time as well.

So, perhaps nothing is as simple as it seems.

Crux Research is Going to the Ogilvy’s!

Crux Research is excited to announce that our client, Truth Initiative, is a finalist for two David Ogilvy Awards. These awards are presented by the Advertising Research Foundation (ARF) annually to recognize excellence in advertising research. Ogilvy Awards honor the creative use of research in the advertising development process by research firms, advertising agencies and advertisers.

Truth Initiative is a longstanding client of Crux Research. Truth Initiative is America’s largest non-profit public health organization dedicated to making tobacco use a thing of the past. Truth is a finalist in two Ogilvy categories:

For both of these campaigns, Crux Research worked closely with CommSight and Truth Initiative to test the effectiveness of the approaches and executions prior to launch and to track the efficacy of the campaigns once in market.

We are honored and proud to be a part of these campaigns, to have had the opportunity to work with Truth Initiative and CommSight, and most importantly, to have played a supporting role in Truth’s mission to make youth smoking a thing of the past.

The 2016 ARF David Ogilvy Awards Ceremony will be held March 15 in New York.  More information can be found Ogilvy Awards.

How can you predict an election by interviewing only 400 people?

This might be the most commonly asked question researchers get at cocktail parties (to the extent that researchers go to cocktail parties). It is also a commonly unasked question among researchers themselves: how can we predict an election by only talking to 400 people? 

The short answer is we can’t. We can never predict anything with 100% certainty from a research study or poll. The only way we could predict the election with 100% certainty would be to interview every person who will end up voting. Even then, since people might change their mind between the poll and the election we couldn’t say our prediction was 100% likely to come true.

To provide an example, if I want to flip a coin 100 times, my best estimate before I do it would be that I will get “heads” 50 times. But, it isn’t 100% certain the coin will land on heads 50 times.

The reason it is hard to comprehend how we predict elections by talking to so few people is our brains aren’t trained to understand probability. If we interview 400 people and find that 53% will vote for Hillary Clinton and 47% for Donald Trump, as long as the poll was conducted well, this result becomes our best prediction for what the vote will be. It is similar to predicting we will get 50 heads out of 100 coin tosses.  53% is our best prediction given the information we have. But, it isn’t an infallible prediction.

Pollsters provide a sampling error, which is +/-5% in this case. 400 is a bit of a magic number. It results in a maximum possible sampling error of +/-5% which has long been an acceptable standard. (Actually, we need 384 interviews for that, but researchers will use 400 instead because it sounds better.)

What that means is that if we repeated this poll over and over, we would expect to find Clinton to receive between 48% and 58% of the intended vote, 95% of the time. We’d expect Trump to receive between 42% and 52% of the intended vote, 95% of the time. On average though, if we kept doing poll after poll, our best guess would be if we averaged Clinton’s result it would be 53%.

In the coin flipping example, if we repeatedly flipped the coin 400 times, we should get between 45% and 55% heads 95% of the time. But, our average and most common result will be 50% heads.

Because the ranges of the election poll (48%-58% for Clinton and 42%-52% for Trump) overlap, you will often see reporters (and the candidate that is in second place) say that the poll is a “statistical dead heat.” There is no such thing as a statistical dead heat in polling unless the exact number of respondents prefer each candidate, which may never have actually happened in the history of polling.

There is a much better way to report the findings of the poll. We can statistically determine the “odds” that the 53% for Clinton is actually higher than the 47% for Trump. If we repeated the poll many times, what is the probability that the percentage we found for Clinton would be higher than what we found for Trump? In other words, what is the probability that Clinton is going to win?

The answer in this case is 91%.  Based on our example poll, Clinton has a 91% chance of winning the election. Say that instead of 400 people we interviewed 1,000. The same finding would imply that Clinton has a 99% chance of winning. This is a much more powerful and interesting way to report polling results, and we are surprised we have never seen a news organization use polling data in this way.

Returning to our coin flipping example, if we flip a coin 400 times and get heads 53% of the time, there is a 91% chance that we have a coin that is unfair, and biased towards heads. If we did it 1,000 times and got heads 53% of the time, there would be a 99% chance that the coin is unfair. Of course, a poll is a snapshot in time. The closer it is to the election, the more likely it is that the numbers will not change.  And, polling predictions assume many things that are rarely true:  that we have a perfect random sample, that all subgroups respond at the same rate, that questions are clear, that people won’t change their mind on Election Day, etc.

So, I guess the correct answer to “how can we predict the election from surveying 400 people” is “we can’t, but we can make a pretty good guess.”