Over the past several years, I have done quite a bit of work with clients that has used the Kano methodology.  For those of you who are not familiar with this method, it is designed to test the attractiveness of a potential product feature by testing the new feature in comparison against the status quo.  You can find out a bit more about the Kano method by going to this past of the website. 

Lately, for some different clients, we’ve developed a new twist on the Kano methodology.  I’ve taken to calling it the Contrarian Kano.  It is applicable for use when the introduction of one feature may result in some consequences that are less attractive, and you want to test for the down-side risks that are associated with those less attractive consequences.
 
How might such a situation develop?  We’ve seen two different models:

1. In one instance a client serves two different customer groups, and the proposed change will necessarily impact both groups – one groups positively and the other group negatively.  Testing the same feature in both groups allows us to assess the downside risk for Group A, which can then be rationally counter-balanced against the benefit to the Group B.
2. Another situation that warrants the use of a Contrarian Kano feature is with testing a possible side effect that is associated with the introduction of a new feature (e.g., the risk of injuries associated with deployment of a steering column air bag).  The Contrarian Kano feature tests the undesired consequences as a “feature” with the goal of determining the degree of aversion to the negative consequences.

​The implementation of the Kano proceeds identically to a normal Kano, with the feature presented in a straightforward fashion.  At the analysis stage, we focus on looking at the number of Reversals to determine the degree to which the negative feature is producing substantial push back from the target audience.

A recent news brief in Science magazine (Volume 355, Issue 6320, page 16) highlighted concerns that many statisticians have regarding continued data availabilty from the constitutionally-mandated census as well as the American Community Survey (ACS).  Efforts to gear up for the 2020 census are underway, and will require a significant funding authorization from Congress this year.

While eliminating the census is problematic -- simply because it is mandated by the constitution -- the 70 item ACS send to 3.5 million homes annually is perhaps in greater trouble.  This study is the replacement to the old long-form census questionnaire, and is used to allocate almost $500 billion in federal program dollars.  The proposed director of OMB is not a fan of the ACS; he voted to defund the study in the past.  

I know that in my work I have often used census and related data from the Department of Commerce to conduct analyses to assist my clients.  Defunding these efforts is not, in my opinion, a prudent step.

For more information see the original article in Science magazine.

If by some chance you are interested in internship opportunities with a statistics focus, consider taking a look at this set of opportunities published by the American Statistical Association in Amstat News.  There are a lot of good opportunities here.stattrak.amstat.org/2016/12/01/2017internships/

I have been absent from posting here for quite a while.  It is nice to be busy, but that sometimes means that I neglect other things that I really should do.

Today I ran across some material developed by Pierre-Antoine Kremp who is doing work on forecasting the outcome of the 2016 election.  It is interesting analytical work based on Bayesian probability models, and is available on Slate.  See http://www.slate.com/features/pkremp_forecast/report.html for the details.

​What I wanted to show you was this chart where he plots the predicted probabilities, by state, for the candidates.  I may use something like this to present the results of propensity modeling.

This graph really does illustrate how few states really are "in play" for this election.

The following has been directly copied from an email released today by the American Statistical Association.  It represents a position that I heartily endorse.

Today, the American Statistical Association Board of Directors issued a statement on p-values and statistical significance. We intend the statement, developed over many months in consultation with a large panel of experts, to draw renewed and vigorous attention to changing research practices that have contributed to a reproducibility crisis in science.
"Widespread use of 'statistical significance' (generally interpreted as 'p < 0.05') as a license for making a claim of a scientific finding (or implied truth) leads to considerable distortion of the scientific process," says the ASA statement (in part). By putting the authority of the world's largest community of statisticians behind such a statement, we seek to begin a broad-based discussion of how to more effectively and appropriately use statistical methods as part of the scientific reasoning process.
In short, we envision a new era, in which the broad scientific community recognizes what statisticians have been advocating for many years. In this "post p < .05 era," the full power of statistical argumentation in all its nuance will be brought to bear to advance science, rather than making decisions simply by reducing complex models and methods to a single number and its relationship to an arbitrary threshold. This new era would be marked by radical change to how editorial decisions are made regarding what is publishable, removing the temptation to inappropriately hunt for statistical significance as a justification for publication. In such an era, every aspect of the investigative process would have its appropriate weight in the ultimate decision about the value of a research contribution.
Is such an era beyond reach? We think not, but we need your help in making sure this opportunity is not lost.
The statement is available freely online to all at The American Statistician Latest Articles website. You'll find an introduction that describes the reasons for developing the statement and the process by which it was developed. You'll also find a rich set of discussion papers commenting on various aspects of the statement and related matters.

Just a brief note today -- coupled with this lovely sunset -- to wish you and yours a prosperous 2016.

Research released from the National Institute on Retirement Security provides some stark data on the extent to which Americans are relying on Social Security for their economic well-being in  retirement.  Using data from the Federal Reserve's Survey of Consumer Finance, they estimate that 38 million households do not have any assets in retirement accounts.  The full study is available here.

Now some of these differences may be attributed to definitions -- note, for example, that the report focuses on assets in retirement accounts.  If you aren't using a tax-deferred IRA or 401(k) or 403(b) plan your assets -- which could be considerable -- wouldn't count as retirement account assets.  Nonetheless, this result does paint a rather dismal picture.

As the chart above -- taken from the report -- illustrates, even those closest to retirement often have little set aside in retirement savings.

Yes, it is very easy to lie with statistics, but it is perhaps even easier to lie with graphs.  We recently saw a situation where an unscrupulous politician, intent on pandering to one of his interest groups, briefly displayed the following graph on the screen during a committee hearing on the funding of Planned Parenthood.

The display on the screen was brief, and thus sought to communicate that abortions out-number cancer screening and prevention services.  But wait -- look at the actual numbers in the graph (which to their credit they did include):  When did 327,000 become greater than 935,573?  Or, 935,573 approximately equal to 289,750?  Or 2 million approximately equal to 327,000?

This gets my vote as one of the most distorted graphs of the year, and the Bubba who used it should be tossed out of office for either his fundamental ignorance or his crass willingness to distort the data while pursuing his political agenda.