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.

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.

Dudley taxi colour

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

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.

why do we value gold?

gold

Could we have developed currency around elements other than gold and silver? Why couldn’t we have coins made out of platinum, for example?

Interesting article today on the BBC website interviewing Professor Sella (University Collage London) about why, of the 118 elements of the periodic table, it is gold (alongside silver) that we value and use for currency.

According to Prof Sella there are reasons to dismiss all the elements apart from gold and silver. For example, you couldn’t use elements that are gas (such as neon) or liquid (mercury) as currency because it would be impractical to carry them around. Several others (such as arsenic and the other liquid, bromine) are poisonous and so could not be practically used. The alkaline metals (those on the left-hand side of the periodic table) are not stable enough (they react with too many other elements). And, of course, say no more about the radioactive elements. Some of the so-called rare earths (such as cerium) could be used but they tend to be even more rare that gold and are actually quite difficult to distinguish from each other.

periodic-table

Prof Sella also postulates reasons for dismissing the 40 transition and post-transition elements such as copper, lead, iron and aluminium. Many are hard to smelt (needing temperatures as high as 1000 deg C) such as titanium and zirconium or hard to extract such as aluminium. Iron is easier to extract and smelt but rusts too easily. Iron is also too abundant.

Prof Sella lists the 8 noble metals (platinum, palladium, rhodium, iridium, osmium and ruthenium, gold and silver) as contenders. However, with the exception of silver and gold they are too rare and have other problems (platinum is hard to extract and has a very high melting point for example). So this leaves gold and silver. The choice of these metals is not arbitrary. It turns out that they have exactly the right properties that we need. They are stable, chemically uninteresting, rare (but not too rare), safe, relatively easy to extract, solid at room temperature and with a reasonably low melting temperature.

The article also explains why gold is golden in colour.

colour and brand identity

union-jewellery-boxes

Just read a really informative article by David Airey – an independent brand identity designer – about colour and brand identity. In his article David reiterates some ideas I heard from Laura Hussey in Design Week (and recently blogged about); that is that some companies such as Oxfam and The Guardian are rebranding with a rainbow colour palette. As David writes: “Multiple colours speak of choice, variety and diversity. Think Google, NBC, eBay, or MSN to name but a few that use more than two colours to express their breadth.”

However, David’s post goes further to describe some of his own work with Halcyon coffee and the use of a range of different colours: “The colours used within the brand and environment were derived from, and act as a subtle nod towards the diverse colour palette used during Britain’s great creative periods of the past — our Halcyon days, mixed with those we see around us today.” Definitely worth having a read of David’s article

new British flag

Some of you may recall that last year – a big year for the UK with the Olympics in London and Queen’s jubilee – there was a lot of waving of British flags. I posted about how the flag was derived historically and noted the absence of any representation by Wales. For those who are less familiar, the United Kingdom is a union of four countries (England, Scotland, Wales and Northern Ireland). By contrast Great Britain is just England, Scotland and Wales (not including Northern Ireland) and the British Isles is a geographical feature that includes the United Kingdom and the Republic of Ireland. Simple?

Next year the Scottish people be asked if they want to be independent. If they vote yes (in my opinion this is not very likely, but possible) it will signal the end of the union of Great Britain and Northern Ireland. Today the BBC ran a feature about possible new designs of the new flag. I wasn’t very impressed by any of them, including the horrible one below. Try reading my post first and then the new BBC article.

flag

3D colour printer

3d-printer_0

An article in Stuff reveals what 3D Systems claims to be the world’s first continuous-tone full colour 3D plastic printer, called the ProJet 4500.The ProJet 4500 offers full-colour parts with colours that are able to blend into each other with gradient transitions.

School of Design hosts Leeds Sustainability Jam 2013

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The weekend of the 22nd November saw the school of Design host the Leeds Sustainability Jam 2013, part of the Global Sustainability Jam taking place simultaneously across the world. A “Design Jam” is similar in concept to a Jam session in music: people come together, bringing their instruments, skills, and open-mindedness. Someone sets up a theme, and everyone starts to Jam around it and together you build something which none of you could have built alone.

The Leeds Sustainability Jam involved a gathering of students and non-students from different areas of study, research, cultures and experiences. The design-based approach to creativity enabled everyone to participate in the discussion of sustainability and consider the environmental, social and economic aspects. Throughout the weekend the emphasis was on “not just talking but actually doing”. By the end of Sunday the teams had turned their ideas into concrete designs or plans of action which could be shared with the global design network and then realistically put into action the next day.

The resulting projects from the Leeds teams included “Be a good Bean”; a network of sustainably sourced local suppliers all connected by a cup, “The Food Experience”; a system of events making lunchtime an experience to enjoy, reconnecting people with the idea of food as being an experience in itself, and “Walk-in Wardrobe”; a university clothes sharing scheme, initially starting with jumpers, where people get to rethink their perception of fashion, consumerism and ownership.

It is now up to the teams to implement these projects across the University campus and local community!

For more information on the projects and the list of Sponsors follow the links:

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