Better to be uncertain than certain and wrong

Researchers cannot escape uncertainty

Uncertainty is a paradox. On one hand, it is a potent and powerful force that motivates research, a need to know. The gratifying result of research is evidence used to guide practice and policy.

On the other hand, uncertainty always remains after research because of the inherent complexity and ambiguity of the real world. So policy-makers and practitioners are (or ought to be) troubled about inevitable residual doubt. Examples include what to do about climate change, what body mass index is ideal and whether to test for prostate cancer.

Why uncertainty remains

Research may help reduce uncertainty, but it can never provide certainty. Research is an errorful process that peers into an obscure reality.

A home of your own: dream or delusion?

The appeal of owning a home seems deeply embedded in the psyche of Australians. Yet psychologically, it is not clear the home ownership dream is entirely rational. Achieving the dream may not be all we might have hoped, and chasing it may even do damage.

The psychological reason Australians want to own their own home is perhaps best expressed by Darryl Kerrigan in the uniquely Australian film, The Castle. It continues to be celebrated globally for showing that the house is just a shell that holds heart. To own your own home has a strong sentimental value, as Darryl says: “You can’t buy what I’ve got.”

Obesity, a wicked public health problem

Most people view obesity as an unequivocal bad -- both for the person and the public. But obesity is a wicked problem. That is, the obesity problem is ill-defined, characterized by complex and contradictory evidence, and unresolved due to conflicting judgments of what is "good" or "bad".

Just one example of how the problem has been oversimplified is in identifying "big food" as the enemy, just like big tobacco before. 


Marketers of food are soundly critiqued for creating large, super-sized portions.

However, in a nod to the wicked nature of the problem, marketers are also critiqued for offering smaller portions. 
And rather more ironically, marketers are also criticised for promoting slim, "overly-idealised" body-forms. 

The media, public health and popular views all share the view that obesity is a problem, but the real nature of the problem is more complex than many seem to realise.

Vaccinate or don't - it won't hurt much either way

Is there a middle ground in the debate on vaccines?

The debate about whether to apply more coercive pressure to vaccinate such as the ['no jab no pay' policy] is being clouded by polarised polemic.

Each side appeals to a reasonably valid ethical claim: pro-vaccination to the public good, anti-vaccination to individual rights.  However, these ethical claims sometimes seem to serve vested self-interest rather than public interest. Moreover, that self-interest reflects an over-estimation of the relative risks to health of vaccination and non-vaccination respectively. 

Don't understand statistics? Wanna bet?

Guess the number of 'heads' out of 10 coin tosses, and you can keep this.

I think we tend to belabour the problem of statistical significance testing sometimes. The whole process is very intuitive.

Let me take you through a thought-experiment to show you this.

"I'm going to toss a coin 10 times. And to make it interesting, let me put this pineapple (Australian $50 note) on the table and make you this offer:

"If you guess the exact number of heads in the next ten coin tosses that I make, I will give you the $50. If you don't guess it exactly, I get to keep my $50. Are you willing?"

So assuming that you see that participating in this gamble is a "no-brainer", you say:

"Yes, sure." 

So what number of coin tosses will be heads? Make your guess now.

Use the illusion: see more and eat less

Trompe l'oeil - making portions look bigger can help reduce consumption

Science has revealed a simple and incredible trick that will help you lose weight. 

No fooling! Or rather, by fooling your eye, you may get the desired result.

The trick is to make portions appear bigger than they are. This leads people to serve and eat less.

We know that bigger portions lead us to eat more (bite-size version here), but portions that appear bigger have the reverse effect.

Inductive reasoning or deductive reasoning?

There are two ways that we come to have knowledge. One is by reasoning from repeated observations (inductive), the other is by ensuring the conclusion follows validly from the premises (deductive). 

The two are inextricably linked. Each makes an argument that helps advance our knowledge of the world.

More surprisingly, each argument fails by the standards of the other!

Inductive vs deductive reasoning

Inductive reasoning is about coming to a conclusion from specific instances (empirical data), deductive reasoning is about coming to a conclusion from premises (logical reasoning).

To find smash-hits, ignore statistical significance !

It's not the end of the world if you're wrong !

The biggest mistake in market research is to worry about being wrong.

Ironically, research can learn this lesson from one if its great detractors, Steve Jobs. He who famously dismissed market research saying "People don’t know what they want until you show it to them".

He was right about customers' lack of self-insight, but wrong about the value of research.

Businesses want blockbusters, to crack the big time, go viral, be trending (upwards). They want and count hits as Steve Jobs knew and proved.

Flying high: sexism, paternalism and sheer idiocracy

What are the dangers to a kid flying alone ?

Airline policies and parents concerned about allowing unaccompanied minors to be seated next to men make a travesty of both reason and justice.

That this fear feeds paternalistic policy and parental concerns is ludicrous.

If you send your child unaccompanied on a plane, your child has more chance of dying in a plane crash than being molested!

Tracey Spicer, journalist and Sky News anchor has recently affirmed her support of this controversial airline policy saying “I don’t want my kids sitting next to a man on a plane.”

Her statement is, as she admits, sexist. It most certainly is, but my major issue is that it is patently wrong and misleading.

It is said that we use only 10% of our brain, that 20% of statistics are made up, and the remaining 90% of the population aren’t any good at proportions.

In defence of uncertainty: Against the wrong of righteousness

Weather to carry an umbrella ?!

In polite conversation, topics like sex, politics, and religion are widely regarded as off-limit. Today, public policy issues like climate change and public health (vaccinations, naturopathic medicines, fast food marketing, etc.) also provoke such polemic that useful debate is impeded.

It is not the topic that is the problem. 

 

It is that positive, meaningful conversation on these topics descends all too quickly from a meaningful dialogue to dogmatism, from disagreement to disagreeableness.

How do you turn an intelligent conversation into a ideological battle? Allow participants to advance confidently held, but opposing beliefs while assiduously denying uncertainty.

Seeing gender differences, blind to individuality

We love looking for gender differences. And then code them as blue and pink.

Marketers do it in order to better target a specific segment.  Humans do it because it is simpler to deal in stereotypes.

The problem is that gender differences are less clear than we are inclined to think. In fact, some 'differences' are completely artificial such as the dress and hair-styles of men and women. It's painted pink and blue, but may be there's no difference underneath.

Men have an outie, women have an innie. Fact.

Women are shorter than men. Sort of. It may be true on average, but it is not universally true.

Gender differences are typically a matter of degree with the distributions of men and women overlapping to a great extent. They are not categorical.

Where to draw the line? Statistics can help, but it is not perfect.

More importantly, generalising about gender differences based on statistically significant mean-differences oversimplifies the reality, and may support stereotypes that discourage or even oppose individual choices.

Seeing gender differences can blind us to individual variety and preferences.

Read more here: Gender differences: more fictions than fact

(If the link does not work, paste the following into our browser: https://theconversation.com/gender-differences-more-fictions-than-fact-11725)

Who holds the power in marketing: the marketer or the customers?

We often like to 'blame'  marketers for pushing people to buy stuff that they do not need. 

On the other hand, customers often demand, and go to great lengths to source stuff which does very little. 

Think of things like rhino horn, homeopathy, anti-aging cosmetics, and status-related items. These items often fail to meet the wants of the customer to a greater or lesser degree. But customers keep on demanding them.

So does marketing succeed because of the marketing efforts or the customer desires?  And if it fails, is it the marketer or the customer who is to blame?

What do you think?

Consider resveratrol, a naturally occurring molecule found in red wine.  Here's my take on the interplay of marketers and customers in the marketing of resveratrol.

(Paste the following link into your browser if the above hyperlink does not work: https://theconversation.com/resveratrol-in-a-red-wine-sauce-fountain-of-youth-or-snake-oil-12743)

In praise of critical thinking

Bertrand Russell's call for you to think for yourself. 

1. Do not feel absolutely certain of anything.
2. Do not think it worth while to proceed by concealing evidence, for the evidence is sure to come to light.
3. Never try to discourage thinking for you are sure to succeed.
4. When you meet with opposition, even if it should be from your husband or your children, endeavor to overcome it by argument and not by authority, for a victory dependent upon authority is unreal and illusory.
5. Have no respect for the authority of others, for there are always contrary authorities to be found.
6. Do not use power to suppress opinions you think pernicious, for if you do the opinions will suppress you.
7. Do not fear to be eccentric in opinion, for every opinion now accepted was once eccentric.
8. Find more pleasure in intelligent dissent than in passive agreement, for, if you value intelligence as you should, the former implies a deeper agreement than the latter.
9. Be scrupulously truthful, even if the truth is inconvenient, for it is more inconvenient when you try to conceal it.
10. Do not feel envious of the happiness of those who live in a fool’s paradise, for only a fool will think that it is happiness.

Bertrand Russell, A Liberal Decalogue (i.e., Ten Commandments) which appears at the end of his 1951 article "The best answer to fanaticism--liberalism"

He underlines his point by having been avid smoker for most of his life, even claiming it saved his life one time.  

(Thanks to Maria Popova at Brainpickings for leading me here).

Tough lessons for trainers & teachers

TEACHER: Millie, give me a sentence starting with 'I'.

MILLIE: I is..

TEACHER: No, Millie..... Always say, 'I am.'

MILLIE: Alright, I am the ninth letter of the alphabet.

------------------------------

TEACHER: Harold, what do you call a person who keeps on talking when people are no longer interested? 

HAROLD: A teacher.

------------------------------

The risks of immunisation & implications for social marketers

 

Social marketers confront some extra ethical challenges that do not confront commercial marketers.

When you are given a medication by a doctor, you get to read all about the possible side-effects and decide on whether the benefits offset the risks.

When the medication is a vaccination, the same freedom of choice for the individual is more restricted.  An individual decision to not vaccinate may attract criticism, ridicule and even rage.

Yet, vaccinations can be harmful to your health.  Admittedly rare, but when it happens, someone (maybe you, maybe your child) 'takes one for the team.'

This is a tough space.  Public health and social marketers are therefore obliged to tackle the difficult space defined by what is 'good for all' on one side and an individual's right to choose on the other.

------------------------------

See this article at The Conversation covering this issue.
http://theconversation.edu.au/preaching-to-the-unconverted-immunisation-risks-and-public-health-11007

Statistical significance is just like a horse race

The logic of statisticians can seem very complicated and impenetrable to normal folk.

But it really is just a formalised version of our own lay style of how we explain unusual events.

When something unusual happens, there are two possible interpretations.  One is to view the unusual event as a freak occurrence, a chance-result, a coincidence.  The other is to view the event as a sign that our understanding of what is going on is fundamentally wrong.

So, is the unusual event simply surprising or does it stretch credulity?  Did we see a rare occurrence or is there some other explanation?

It's a bit like interpreting the result of a horse race won by a horse with long odds.  Is the win a possibility even if improbable, or is it so improbable as to be considered an 'impossibility' requiring a brand new explanation.

Read more on this idea in this article posted on The Drum / ABC : The Melbourne Cup and Statistical Significance

Building better brand metrics : multi-collinearity as friend rather than foe

Multi-collinearity looks more complicated than it is !

Metrics are hot.  Multi-collinearity is not.  

Multi-collinearity.  It is a big word – and a big mystery to many students of statistics and even practitioners – just like the word, heteroscedasticity!   

The existence of multi-collinearity actually makes the world a simpler place in a practical sense.  

Are your clients currently enthused by Balanced Score Cards, Brand Metrics, Net Promoter Score and various other tools that consist of many apparently independent measures for assessing the health of a company and/or brand?  Well, stay tuned because it does not have to be that hard, as multi-collinearity will show!

Multi-collinearity is simply the problem of two predictor variables being correlated with one another such that the contribution of each to the criterion is difficult to tease apart.  Imagine trying to predict purchase intentions, and we measure both ‘price’ and ‘value.’  Clearly both are useful for predicting purchase intentions, but the two are also very likely to be correlated to one another.  This means that once we know one, the other does not add much to our prediction.

Okay, we understand the problem, but do we understand how often we encounter this situation?  And how often we may be misrepresenting the results to our clients as a consequence of many correlations between the predictor variables we report to our clients?  If you are using any kind of multi-attribute rating models (e.g., Vroom’s expectancy-valence model, Fishbein & Ajzen’s original attitude-model, Gale’s Customer Value Analysis model, etc.), then you are likely encountering this problem.  These are the models where you measure how customers rate various attributes of the brand, and use these ratings to determine what are the ‘drivers’ of brand purchase.

Typically, ratings of any brand on these attributes are highly correlated.  For instance, if you chose to assess ratings of ‘price’ and ‘value’ as two separate attributes, they will typically be highly (negatively) correlated.  In a multiple regression, the result is that one will contribute significantly to the regression, and the other one, because it is highly correlated to the first, will not.

‘Aha,’ you say, ‘but I take explicit measures of the importance ratings.’  Yes, well unfortunately this does not solve the problem.  As most of us know, respondents will typically tell us that all attributes are pretty important.  You can play games with constant-sum scales that helps differentiate importance, but you are still not dealing with the problem of what I might call non-statistical multi-collinearity.  The problem is that if you ask a respondent how important is ‘value’ and how important is ‘price’, they will probably give a fairly equal importance rating to both.  Why wouldn’t they – they are really much the same!

What is the solution?  One suggestion is to retain just one of the multiple correlated items.  This is certainly one solutionn – and links to a tangential issue about better quality drafting of questions.  If we can anticipate ahead of time that two attributes are going to be highly correlated, we can consider measuring just one or the other.

However, I am also a great believer in combining separate items collected on a questionnaire as they provide a more stable (reliable) measure than using a single item.  That is, I measure multiple attributes, even if they are likely to be correlated.  Then, I conduct an examination of the intercorrelations of the various attributes to see if I can simply combine two or more items into one scale.  If I want to be really sophisticated, I could conduct a factor analysis for guiding the combination of items. This allows for a sophisticated weighting of each variable in the final ‘scale.’  However, I generally find that clients (and analysts) find simple, averaged scales much easier to interpret than factor scores. 

However, one rather disturbing result that I have found in examining these intercorrelations among attribute ratings is many of the ratings are correlated with many of the others!  Even among sophisticated respondents such as doctors, I find that the ratings they give to a drug in terms of potency, efficacy, side-effect profile, drug-interactions, cost and value are all likely to be correlated.  More broadly, I find that on many research projects, many of the intangible qualities of the brand that we might measure (brand awareness, attribute ratings, overall evaluations, satisfaction, usage, etc.) are all highly correlated.

Many researchers appear to be unaware of or unwilling to acknowledge such correlations, and will happily make recommendations to tweak a particular quality of the brand in order to improve overall image, satisfaction, purchase intentions, etc.   However, if all these attributes are so highly correlated, advice to tweak one or the other is at best rather meaningless and at worst, rather misleading. 

However, to offset the bad news, there is some good news.  The good news is that the intercorrelations between the many predictor variables means that we do not need to consider a screed of so-called independent measures to assess the health of a brand.  Some clients have had me explore various brand metrics, and what I find is that often-times, we need only look at relatively few numbers rather than many to assess the health of our brand!  Why?  Because of multi-collinearity.  The independent measures are often so highly correlated that they can all be combined into one or at least relatively few scales which capture most of the important intangibles. 

For instance, in research we have conducted in both pharmaceutical and agricultural domains, we have found that we can reduce many of the measures of the intangibles (such as customer attribute ratings of the brand among other things) down to perhaps three dimensions which operate as very strong predictors of brand purchase. 

Of course, you might like to know what those dimensions are, right?  Unfortunately, that would be telling!  Nevertheless, I have given you the key to simplifying the intangible assets of the brand.  You can work it out. 

And as to heteroscedasticity, I will leave that to another day.

Multicollinearity - the magic behind brand metrics

Reducing two problems to one solution 

Metrics are hot.  Multi-collinearity is not! 

There are multiple metrics for measuring the health of the brand and/or company, but simple interpretations seem to be sparse.

Meanwhile, multi-collinearity is generally identified as a statistical problem in which two or more predictor variables are correlated with one another which muddies the interpretations of what variables predict important outcomes such as purchase intentions.

However, if the two are put together, the world of marketing can become very much simpler and clearer.    

The existence of multi-collinearity actually makes the world a simpler place in a practical sense, and by understanding the simplifying properties of multi-collinearity, we can simplify the metrics that we use to measure things such as brand-health.

Problem of Multiple Metrics

Market researchers have built their empire on multiple metrics; call them measures or items or questions if you prefer.  And modern marketing is encouraging this through the pursuit of ‘metrics’ such as the Balanced Score Card, Brand Metrics, Net Promoter Score, etc. 

And in turn, each of these metrics is made up of many apparently independent measures.  For instance, most brand metrics will include one or more measures of dimensions such as awareness, familiarity, liking, attribute-rating, attribute-importance, attitude, satisfaction, purchase intentions, etc. 

The notion is that what is measured is managed, and so if we are measuring something, then we are on the way to managing it.

The output from studies is multiple metrics, multiple charts, multiple tables, multiple pages, and multiple options but few clear solutions.

Problem of Multi-collinearity

For most researchers, multi-collinearity is a disaster.  More disturbingly, for some researchers, it is a bit of a mystery. 

Multi-collinearity is simply the way in which two or more predictor variables in a multiple regression are related to one another.  For instance, many companies are keen to know what ‘drives’ the customers' decision making.  In predicting what drives doctors' prescribing behaviors, three of the attributes that are often included for consideration are ‘efficacy,’ ‘side-effect profile’ and ‘drug interactions.’  It is not uncommon to find that all three attributes are positively correlated to prescribing intentions. 

A simplistic interpretation is that each attribute is an independent driver of prescribing.  However, if a regression is run, it may be found that only one attribute predicts intentions, the weights for the other two being not significantly different from zero.  This is a clue that multi-collinearity maybe in operation.

Multi-collinearity can be seen if intercorrelations of the independent variables are examined.  If two or more are significantly intercorrelated, we probably have multi-collinearity issues.

More simply, if three attributes such as efficacy, side-effect profile and drug interactions are correlated with one another, and only one of them is significant in a multiple regression model predicting prescribing, then we can conclude that there is really probably only one dimension underlying all three attributes which is driving prescribing intentions. 

Implications

Multiple metrics sound great, but too many make simple interpretation difficult.  Most market research studies are built based on multiple metrics (or more simply, measures or items or questions).  How often do we misrepresent the results by reporting the many correlations between the predictor variables and a dependent variable such prescribing – without allowing for the possibility that the various predictor variables are correlated or simply multiple restatements of one underlying relationship?

What is the solution?  Well, first, we can examine the relationships between the key metrics. 

Then we have two options for handling metrics that are correlated with one another.  One solution is to retain just one of the multiple correlated items.  At a more practical level, if we can anticipate ahead of time that two (or more) metrics are going to be highly correlated, we can consider measuring just one or the other.

Another solution is one that combines the separate metrics into a single index as this provides a more stable or reliable estimate.  That is, we use multiple measures, even if they are likely to be correlated, and then combine items that are correlated with one another into one scale. 

If we want to be really sophisticated, we could conduct a factor analysis for guiding the combination of items. This allows for a sophisticated weighting of each variable in the final ‘scale.’  However, in my experience, clients (and analysts) find simple, averaged scales much easier to interpret than factor scores. 

Multi-collinear metrics

For some, multi-collinearity is understood to be a problem, and something we would prefer to avoid.  However, multi-collinearity is not a problem, it is reality.  Multi-collinearity tells us that in many senses the world is a simpler place than we originally thought – and when business is as complicated as it is today, this is a very good thing.

My own exploration in this area came from the initially disturbing finding in examining intercorrelations among many of the ratings of brands on various attributes.  Even among sophisticated respondents such as doctors and specialists, the ratings they give to a specific drug in terms of potency, efficacy, side-effect profile, drug-interactions, cost and value are all likely to be correlated.  More broadly, on many research projects, I have found that many of the intangible qualities of the brand that we might measure (brand awareness, attribute ratings, overall evaluations, satisfaction, usage, etc.) are all highly correlated.

Many researchers appear to be unaware of or unwilling to acknowledge such correlations, and will happily make recommendations to tweak a particular quality of the brand in order to improve overall image, satisfaction, purchase intentions, etc.   However, if all these measures are so highly correlated, advice to tweak one or the other is at best rather meaningless and at worst, rather misleading. 

However, to offset the bad news, there is some good news.  The good news is that the intercorrelations between the many predictor variables means that we do not need to consider a screed of so-called independent measures to assess the health of a brand.  Some clients have had me explore various brand metrics, and what I find is that often-times, we need only look at relatively few numbers rather than many to assess the health of our brand!  Why?  Because of multi-collinearity.  The independent measures are often so highly correlated that they can all be combined into relatively few scales which capture most of the important intangibles. 

For instance, in research I have conducted in both pharmaceutical and agricultural domains, I have found that we can reduce many of the measures of the intangibles down to three dimensions that operate as very strong predictors of brand purchase. 

Of course, you might like to know what those dimensions are, right?  Unfortunately, that would be telling!  Nevertheless, I have given you the key to simplifying the intangible assets of the brand.  You can work it out.