Welcome to my blog

I am passionate about sharing my knowledge about colour to anyone who is prepared to listen. I work as a professor of colour science at the University of Leeds, in the School of Design, but I have held academic posts in departments of Chemistry, Physics, Neuroscience, and Engineering. Sounds like a mixed bag, but my interest was colour chemistry, colour physics, colour neuroscience, colour engineering and colour design. You see I have come to believe that colour is the perfect meta-discipline and that to understand colour you need to be able to understand (but not necessarily be an expert in) different fields of knowledge.

One way to use this blog is to just browse through it and dip in here or there. However, another way is to click on one of the categories (that interest you) such as culture, design, fun, and technology and see posts in that area. You can find the categories on the right-hand side of the page if you scroll down.

You can also comment on the blogs. I really like this, even if you disagree with me. Someone once said to me if you put ten colour physicists in a room and ask them a question (presumably about colour physics) you’ll get 10 different answers. Well, I guess not all of you reading this are colour physicists. Given our different interests and backgrounds, and given the complexity of colour, it’s not surprising that we will disagree from time to time. And that is rather the fun part.

If you have a technical question you’d love me to answer you can click on Ask Me and post it there. You can also email me at s.westland@leeds.ac.uk

The Wizard of Oz

This week I had to mark about 50 essays that had been submitted for the Colour: Art and Science module I teach at the University of Leeds. One essay looks rather like another after the first 10 or so. So it was a delight to discover that one student had decided to focus on a movie – The Wizard of Oz – and demonstrate her understanding of colour by analysing this classic movie.

It reminded me of a story my mother told me. When she went to see the Wizard of Oz in the cinema (she would have been about 8 at the time) she had never seen a colour movie before. She was so much looking forward to this new-fangled and exciting technology. It’s hard to imagine how exciting that would have been – if every movie you had ever seen had been in black and white!!

Well, imagine her disappointment when the movie started and the movie was black and white after all. For those who don’t know, the movie starts off in black and white (in the Kansas scenes) and only turns coloured when Dorothy is whisked off by the tornado and dropped off in the land of Oz. It must have been a wonderful moment when the screen just turned full colour!!

Indigo – a colour of the rainbow?

From time to time I come across web pages and groups of people who get irrate about indigo being in the rainbow. There is even a facebook group called “Get Indigo out of the rainbow”. It was Newton who suggested that the rainbow contains seven colours: red, orange, yellow, green, blue, indigo and violet. It has been suggested that, at the time, Newton was trying make some anology with the musical scale and the octave (with its seven intervals) and hence was keen to identify seven colours in the rainbow or visible spectrum. Many modern commentators claim that only six distinct colours can be observed in the rainbow.

Interestingly, the facebook group referred to above would like to eject indigo from the spectrum on the basis that it is not a primary or secondary colour but rather a tertiary colour. The group shows the following colour wheel:

colour wheel

In this so-called painters’ wheel the primary colours are red, yellow and blue and the secondary colours are orange, green and violet. It is argued that since six of the colours in the rainbow are primary or secondary colours in the colour wheel and indigo is not, then indigo has no right to be there. This is wrong on so many levels it is hard to know where to start.

The first thing I would have to say is that this argument seems to ignore the difference between additive and subtractive mixing. Additive mixing – http://colourware.wordpress.com/2009/07/13/additive-colour-mixing/ - describes how light is mixed and the additive primaries are red, green and blue. The additive secondaries are cyan, magenta and yellow. Orange is not in sight – and yet surely if we are to make an argument for inclusion in the spectrum based on primaries (and/or secondaries) then it is the additive system that we should be using since the spectrum is emitted light.  

The optimal subtractive system primaries are cyan, magenta and yellow (with the secondaries being red, green and blue) though the artists’ colour wheel (which is like the painters’ wheel above) has red, blue and yellow as the primaries. 

In my opinion there is nothing special about the colours that we see in the spectrum. Indeed, orange is clearly a mixture of red and yellow and does not seem to me to be a particularly pure colour. I just do not think that arguments to exclude indigo from the spectrum based upon colour wheels or primary colours is valid. That said, I have already mentioned that many people believe that indigo cannot be seen in the spectrum as a separate colour; but this is a phenomenological observation not dogma. I am one of those who believe that indigo and violet cannot be distinguished in the spectrum and therefore I agree with the aims of the facebook group even if I do not agree with their arguments.

The really interesting question is why we see six (or even seven) distinct colour bands in the spectrum when the wavelengths of the spectrum vary smoothly and continuously? I have postulated some possible reasons for this in an earlier post – http://colourware.wordpress.com/2009/07/20/colour-names-affect-consumer-buying/ - but it is far from a complete and convincing explanation. It may explain why we see distinct colours in the rainbow, but why six and why those six in particular. Comments on this would be very very welcome.

new colour blog

I found this interesting blog about colour – it’s called Stories behind Colours and is written by Susan Mathen. It contains interesting posts that relate to meanings of colour and, particularly, the stories about where those meanings come from. Please visit it.

http://www.storiesbehindcolours.blogspot.co.uk/

different views of Leeds

Leeds

This year I hosted two Italian students as part of a European project. Silvia and Enrico both had the most fantastic design skills and both undertook projects about how to promote or represent a city – in their case, the city of Leeds where I work at the University of Leeds.

Here are two videos they produced at the end of their work here.

And here is a small diary about their time here in Leeds – http://colourdocks.wordpress.com/

BP fails to win protection for green colour

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!

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

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

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

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!

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.