BP fails to win protection for green colour

design, news, , ,

BP

The oil giant BP has again failed in its long-running bid to trademark the colour green in Australia.

The intellectual property watchdog, IP Australia, found BP was unable to show “convincing evidence” that it was indelibly linked in the average petrol consumer’s mind to the dark green shade known as Pantone 348C, a spokeswoman for the government agency said.

BP first tried to register a trademark for the colour in 1991, and until 2013 fought legal battles against another corporate titan, Woolworths, to stake its claim to the colour as the dominant shade for its service stations.

For the full story see http://www.theguardian.com/business/2014/jul/03/bp-loses-battle-to-trademark-the-colour-green-in-australia

Making colour!

art, culture, news, ,

Interesting review by Charles Hope of Making Colour exhibition at the National Gallery.

In particular it shows the changes made possible by the introduction of new types of paint after 1800. Most of the exhibits are drawn from the gallery’s own holdings, with a few loans from other museums and private collections in Britain.

Anything that reminds us that paintings are objects whose production required much technical knowledge and manual skill, and often a desire to overcome the physical limitations of the materials used, is to be welcomed.

Runs until 9th September 2014.
See http://www.lrb.co.uk/v36/n14/charles-hope/at-the-national-gallery

high blood pressure affects colour vision

nature, news, , ,

colourblind

Drinking alcohol not only affects your speech and balance. It can also affect your colour vision. Not just alcohol. Various drugs (some contraceptives and analgesics, for example) make you less good at discriminating between colours. And there are a load of medical conditions that also affect your colour vision including MS and diabetes. In fact, often a deterioration in colour vision can be one of the first indications of a problem. This is why it is a good idea, from a health perspective, to have your vision checked by a qualified professional on a regular basis.

Now some research from Japan suggests that deterioration in colour vision may be a predictor of hypertension – a condition in which the arteries have persistently elevated blood pressure. The study looked at 872 men aged between 20 and 60. They found that, when other factors were taken into account, as blood pressure values rose, the odds of having impaired colour vision increased as well.

For further information see here.

grab colour – use it

design, news, opinions/rants, technology, , , , , , ,

colour pen

Many of you will have seen the Scribble Pen which uses a colour sensor to detect colours. The sensor is embedded at the end of the pen opposite the nib. The pen then mixes the required coloured ink (cyan, magenta, yellow, white and black) for drawing, using small refillable ink cartridges that fit inside its body. The device can hold 100,000 unique colours in its internal memory and can reproduce over 16 million unique colours.

But wait. Don’t think that means you will be able to use the pen to write in 16 million different colours. You won’t. A typical phone screen can display about 16 million unique combinations of RGB (red, green and blue). But many of the RGB combinations are indistinguishable. Open up powerpoint and make two squares. Set the RGB values of one to [10 220 10] and of the other to [10 220 11]. I would be amazed if you could really tell the difference between them. And anyone who has read much of my blog will know that I believe that if two colours look the same then they are the same. So the pen might be able to create 16 million combinations of cyan, magenta, yellow, white, and black – but that doesn’t mean 16 million different colours.

The second problem is that just because your pen can grab a colour (using its sensor) doesn’t mean it can create it. There are lots of colours out there in the world that are outside the colour gamut of an ink-based system (even one using five primaries – cyan, magenta, yellow, white and black).

Read more: http://www.dailymail.co.uk/sciencetech/article-2647129/Forget-crayons-Multicolour-pen-lets-pick-colour-draw-16-million-shades.html#ixzz35gJ0racJ
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The dangers of Likert scale data

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Imagine that you want to compare two products A and B and you ask the opinions of 100 users via a survey. The table below shows a summary of the survey and the responses. The numbers under product A and product B show the number of people who gave each of the responses on the left-hand side.

likert

This is known as a Likert scale and this post will give some thoughts on how to analyse these data.

The first thing that is worth mentioning is that there is a simple form of analysis that is relatively uncontentious. This is to say that 60% of people were very satisfied or quite satisfied with product A whereas only 45% of people were similarly very satisfied or quite satisfied with product B. On the one hand this is simple. However, can we use this analysis to say that product A is better than product B? Note one problem straight away, which is that 20% of people are very dissatisfied or quite dissatisfied with product A whereas only 15% of people were similarly very dissatisfied or quite dissatisfied with product B. It seems that product A tends to polarise opinion and it is not clear what conclusions can be drawn.

However, quite often we assign numbers to the categories (such as 5 = very satisfied, 4 = quite satisfied, 3 = neutral, 2 = quite dissatisfied, and 1 = very dissatisfied) and when this is done we can produce a number for each participant’s response; we can then average this to produce the mean values shown in the figure above. According to this we can say that on average the response to product A is 3.6 and to product B is 3.5. Can we now use these numbers to make the following two statements? (1) that product A is better than product B (since 3.6 is bigger than 3.5) and that (2) both products A and B are well received by the participants (since 3.6 and 3.5 are both bigger than 3). What I want to do in this post is discuss the validity of these statements by considering several aspects of Likert scales.

Is it valid to average the numbers?

There is a long-running dispute about whether it is valid to average the scores to produce the mean values as in the table above. To explore this we need to introduce two types of data. The first type are called ordinal data. This is the order in which things are. The Likert scale presented in the table above strictly produces ordinal or rank data. Imagine that three people, Alan, Brian and Clive run a race in which Alan wins, Brian is second, and Clive is third. Knowing the order in which they finished is fine, but it doesn’t tell us whether Alan finished well ahead of the other two or whether, for example, Alan and Brian were involved in a close finish with Clive a long way behind. If, however, we know how many seconds they took to complete the race (Alan = 40 seconds, Brian = 41 seconds, and Clive = 52 seconds) we now know much more information about the race. It turned out that Clive was a long way behind the other two. The race times, in seconds, are called interval data. With interval data the differences between the numbers are meaningful whereas with ordinal (rank) data they are not.

The problem with a Likert scale is that the scale [of very satisfied, quite satisfied, neutral, quite dissatisfied, very dissatisfied, for example] produces ordinal data. We know that very satisfied is better than quite satisfied and quite satisfied is better than neutral, but is the difference between very satisfied and quite satisfied the same as the difference between quite satisfied and neutral? Why am I worrying about this? Because when we assign numbers to the scale (the 1-5 numbers) and then average the responses we are implicitly making the assumption that the scale items are evenly spaced. We are treating the ordinal data as interval data. How can we be sure that the participants treated the scale in this way? Would it have made a difference if we had used satisfied and dissatisfied instead of quite satisfied and quite dissatisfied respectively? So it would seem that is wrong to calculate means from Likert scales. If you click here you will see a post from a PhD student (Achilleas Kostoulas) at the University of Manchester who states categorically that it is wrong to compute means from Likert scale data. I choose this example because it is simply and elegantly explained not because I necessarily agree entirely with his view. It is also worth reading the article by Elaine Allen and Christopher Seaman in Quality Progress (2007) who also take the view that Likert scale data should not be treated as interval data. Interestingly they also suggest some other techniques that don’t suffer from the ‘ordinal-data’ problem; for example, using slider bars to get a response on a continuous scale. However, before you give up detailed analyses of Likert scale data I would urge you to read the paper by Susan Jamieson called Likert scales: how to (ab)use them in Medical Education (2004: 38, 1212-1218). Although Susan is also broadly speaking against treating Likert scale data as interval data she does present the other side of the argument. In another paper, in Advances in Health Sciences Education, Norman (2010, 15 (5), 625-632) argues that the concerns about Likert scales are not serious and we should happily use means and other parametric statistics.

How much bigger do two averages need to be for an effect?

In the table at the start of this article product A and B receive scores of 3.6 and 3.5 respectively. The paragraphs above explain that calculating these means may not be valid. However, assuming that we do calculate means in this way, how different would the mean scores for product A and B need to be for us to conclude that A was better than B? I have come across students (normally in vivas) who would simply state that A is better than B because 3.6 > 3.5. To those students I then would say, would you still take that view if instead of 3.6 and 3.5 it was 3.51 and 3.5? What if it is 3.50001 and 3.5? Would they still maintain that A is better than B? It is clear that we need to consider variance and noise and carry out a proper statistical test to conclude whether 3.6 is significantly greater than 3.5. The test is called a student t-test and anyone can be taught to perform one using Microsoft Excel in a matter of minutes. In the example at the start of this article it turns out that there is no statistically significant difference. We cannot conclude that product A is received better than product B.

However, can we conclude that both products are received favourably? Again, we need a statistical test. It turns out that in this case, both 3.6 and 3.5 are statistically greater than 3 and we can at least conclude that products A and B are received favourably. However, there is the caveat that this assumes that we can treat the Likert scale data as interval data in the first place.

Other considerations

An interesting question is whether we should use 5-point scales at all. Would we get different results if we used a 7-, 9- or 11-point scale? I have found one website that suggests that a 7-point scale is better than a 5-point scale but not by much. A paper by Dawes in International Journal of Market Research (2008: 55 (1)) looked at 5-, 7- and 10-point scales and concluded that the results from a 10-point scale would be different from a 5- or 7-point scale (after suitable normalisation).

Although odd-number scales (with a neutral point) are almost always used. A paper by Garland (Marketing Bulletin, 1991: 2, 66-70) suggest that using a four-point scale (and removing the neutral point) might remove the social desirabiity bias that comes from respondents wanting to please the interviewer. I am not sure what current thinking is on this matter though and I would normally use odd-number scales.

I am not providing any definitive views on these points but rather raising awareness of issues. If you want to use a Likert scale then these are issues you need to familiarise yourself with.

My view

I will confess to having treated Likert scale data as interval data and carrying out parametric statistics (these are statistics that use statistical parameters such as standard deviations). However, deep down I know it is wrong. I am coming to the view that the best thing is not to use a Likert scale at all. I think people often use this sort of scale because it seems simple. There are ways to statistically analyse data like these and I would refer readers to categorical judgement which is a well-used psychophysical technique. My colleague Ronnier Luo at Leeds University has used this technique extensively for decades. However, it is far from simple to analyse the results. I think there are better ways of obtaining information. I think use sliders bars and allowing users to indicate using the slider bar their view between two extremes (e.g. between very satisfied and very dissatisfied) is probably better and I will encourage my students to use this technique in the future.

check your urine colour!

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urine

Just key urine colour chart into google images and prepare to be amazed. There are so many different charts and blogs and experts. Who would have thought it!! Today I saw an article in The Guardian that inspired to be to make this search. It turns out that there is a new urine colour chart from a clinic in USA that allows you to make a self diagnosis of your health based on the colour of your wee. A case of cross-media colour reproduction if ever I saw one (a poor joke that, for colour imaging scientists who may come across this blog).

I’m not sure it’s news though since there are a plethora of interesting charts for this already in existence and according to The Guardian the philosopher Theophilus noted the medical value in looking at the colour of urine as long ago as 700AD. However, if you have strangely coloured urine you might want to have a quick peek at The Guardian article to put your mind at rest (or not, as the case may be). Mine, for those who are interested, is sometimes clear but sometimes yellow verging on orange which is, I believe, because I don’t drink enough water. If you have blue urine it’s time to worry apparently.

Dudley taxi colour

culture, design, news, , , , , , , , ,

I don’t just blog about flags and taxis – it just seems like it sometimes.

But today I came across a news story in the Express and Star (a newspaper in the Wolverhampton area of the UK) about a review of rules permitting taxi drivers in Dudley to use only white-coloured cars. The taxi association says that white cars are more expensive because of the popularity of the colour – with some even forced to respray their vehicles to comply with the rule. The single-colour scheme was introduced in 1996.

taxi

Just put taxi in the search box (at the bottom of the page) to see my other posts about taxi colour controversies. Or don’t, if you have a life to live.

new designs for UK flag colour

culture, design, fun, news, opinions/rants, , , , , , , , , ,

I blog about anything related to colour and I am interested in all sorts of aspects of colour whether they be based in arts and design, cultural studies, evolution, chemistry, physics, biology or technology. But a couple of themes keep cropping up and I end up posting about them at regular intervals. So, in 2012 I posted about the historical development of the UK flag – the union jack. And then earlier this year I posted about an article on the BBC about the possible redesign on the union jack is Scotland votes to leave the United Kingdom in the forthcoming referendum there. Some of the designs that were being put forward were really horrible. Perhaps I am too attached to the union jack. A few days ago I came across another BBC story which included 25 readers’ designs for the union jack should Scotland leave. . I must say I much prefer the readers’ designs rather than those previously proposed by experts – the BBC reliably informs me that such experts are known as vexillologists. I like this flag (by David and Gwyneth Parker) – where the blue of Scotland has simply been swapped for the green of Wales, thus preserving the traditional look. (If you wonder why the green of Wales is not in the current flag see my earlier post.)

flag1

And I also like the following design (by Matthew Welch), where England and Wales are represented in the top left and bottom right corners respectively and the diagonal stripe represents Northern Ireland of course.

flag2

You probably have to be from the UK to understand this humorous design (by Al Main).

flag3

You can see all 25 readers’ designs at the BBC here.

If you are interested in vexillology (is that a word?) you may like to read another BBC story about a potential new flag for Norther Ireland. And finally, I was interested that the CIA apparently has a flag database that it makes available to the public.