11 August 2017

Research trip to Singapore

We've just returned from a long research trip where we attended the Speciality Fine Foods Asia event in the Suntec Convention Centre in Singapore. We learned a lot of great things about the food and drinks market in Singapore (and south-east Asia!) and are developing the markets in the region for user research and UX design.
We're also planning some workshops in south-east Asia where we'll be training people to undertake UX activities such as research, design and usability testing. If you're interested in participating, you can contact us on alan@thoughtintodesign.com, Twitter at @ThoughtN2Design, or Facebook.

10 August 2017

International research & conferences

I've been invited to give the plenary talk at the PSITE conference in Tacloban, Philippines. It is a real honour to present an outline of user research and user experience design to such an important group of IT educators and I hope I can reward them with a fantastic talk.

The conference is 19–21 October 2017 and I'll be there with my company, Thought Into Design, the whole time.

We're also planning on running some UX workshops / training sessions the following week whilst we're in the Philippines. I can be contacted at alan@thoughtintodesign.com if you'd like to know more.

My partner and I just returned from Singapore and the Philippines after doing some research for DIT. It was a great few weeks and we gained some surprisingly useful insights for this client.

25 February 2017

Statistics Routines

In addition to work on SigmaSwiftStatistics, we're writing some statistics libraries for other languages.

We're a bit sneaky, because we find this a great way to get familiar with languages old and new, but it also provides a useful service for everyone else.

Currently, we're writing some routines in Ada to complement the Swift ones. You can get the Ada code here. We'll be creating a Prolog repository soon.


19 February 2017

Consulting at the Department for International Trade

Thought Into Design Ltd is heavily involved with some research activities at the Department for International Trade (DIT). We're very excited to be collaborating with DIT on a range of research projects. We've already undertaken some usability testing and we're hoping to add our skills and knowledge to all the rest that the DIT has available.

This is coming very soon after our stint at helping the Office for National Statistics improve their Interdepartmental Business Register (IDBR), which is a register of 2.1 million UK businesses and fully compliant with the European Union regulation on harmonisation of business registers for statistical purposes (EC No 177/2008).

18 February 2017

Trying out Swift

Lately, at Thought Into Design, we've been trying out Swift. This was partially so that we could write apps for iOS and OSX (or is that MacOS now?) but it is also good to learn the foibles of a new language's syntax.

Our most recent work has myself (Alan) contributing towards an open source statistics library called SigmaSwiftStatistics. It's been great fun so far and we've contributed a few descriptive functions (to be cleaned up and made better by the project's maintainer, Evgenii Neumerzhitckii) such as the coefficient of variation, geometric mean, harmonic mean, skewness, kurtosis, Hyndman & Fans 9 quantile methods and a handful of nonparametric tests like a decent mode routine and a routine to rank data.

The mode is more useful than, say, that found in SPSS because it reports not just the modal value but all the indices at which it occurs. It's been some years since I used SPSS, but when I did, I recall it only provided the first index of occurrence. Sometimes, it's useful to know everywhere that it occurs.

Anyway, we've been committing to this project and are sure you'll find it useful. It's in Cocoapods so it should be simple to use into a project.

Over the last few evenings, we've been coding up a routine for univariate analysis of variance (both within and between subjects). It's passed its initial tests (using ANOVA tables generated from SPSS) so the code is almost ready to go. We hope to get it committed sometime this weekend.

We're also keen to produce some decent nonparametric tests. When people write routines, there is a tendency to focus on tests for parametric data and tests for nonparametric data are the forgotten children. We've found nonparametric tests to be excellent in many real-life circumstances (heavily skewed or kurtotic data, no normal distribution, scales of less than 11 items if I remember my Nunally correctly) so we want to ensure they are included.

In the future, I would like to investigate putting a Swift wrapper of some kind conforming to BLAS so that a massive range of linear algebraic routines (often heavily optimised) will be available. When using Swift, I find I miss Numpy and SciPy, and a layer on top of BLAS would help bring some of that serious power over.

Here's to statistics on Swift!

13 February 2015

Fast prime numbers in Python

I spent some time recently on Project Euler and got side-tracked by the efficient calculation of prime numbers. After using a brute force method (iterating through a range of numbers and trying to find their factors), I read around and found a nice page at http://rebrained.com/?p=458, I found a good Stack Overflow page at http://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n. These inspired me to try again and I came up with the following routine. It's faster than most but not as fast as primes6 on the first page I linked to when generating more than approximately prime numbers up to 350,000-400,000. Below that, nothing seems to touch it.

It's also different. It uses numpy (which is cheating, in a way) but does the job well. I like it because it seems more understandable once you've grasped that the routine doesn't do any division. Instead, it's pure sieve operations on a vector of booleans. Anything found to be divisible by anything other than 1 and itself is marked as False, and the routine finishes by returning the indices of True values - which are primes.

I've triangulated the results by summing them and comparing the sums against those of other routines and there's no differences I've noticed yet.

import numpy as np
from math import sqrt

def ajs_primes3a(upto):
  mat = np.ones((upto), dtype=bool) # set up a long boolean array
  mat[0] = False # remove 0
  mat[1] = False # remove 1
  mat[4::2] = False # remove anything divisible by 2
  for idx in range(3, int(sqrt(upto))+1, 2): # remove anything else divisible
      mat[idx*2::idx] = False 
  return np.where(mat == True)[0] # return the indices which are the primes

I'm quite pleased with this early foray into optimising a routine but there's work to do compared to prime6. What I like is that it has no division and instead seems to be a pure sieve and doesn't create a long list of numbers.

I tried other versions with a half-series so that anything divisible by 2 just wasn't considered, but what I came up with just weren't as fast.

Times (msecs, same machine, best of 3-6 multiple runs)

             10k      100k     500k     1m       20m
prime6       0.001258 0.002722 0.007229 0.001229 0.22388
erat         0.005414 0.059047 0.333737 0.673749 15+ seconds
ajs_primes3a 0.000360 0.001897 0.008540 0.016952 0.70135

Up to 100k, mine leads but prime6 takes over strongly after that. Mine doesn't lose too much ground, considering, so it's best to think of mine as fast-ish but nicely understandable. 

23 August 2013

Crowd-sourcing research

One idea I had a few years ago was to use the various crowd-sourcing websites as a source of willing and cheaply-paid participants for UX research.

Wait, you fool! You cannot do an hour-long usability session like that!

Well, the keyword is reductionism. UX research is discovering brain and behaviour. It's psychology. And a few psychologists have already been using crowd-source sites as sources of participants. [1, 2]

The overall results are quite promising: It is possible to undertake such experiments with a crowd-sourced experiment sample. There are, however, precautions to be taken, and it's only fair to pay participants a decent amount if only to ensure a low drop-out rate and faster participation.

So it's not cheaper but it is faster. One study [2] said, "Performing a full-sized replication of the Nosofsky et al. [40] data set in under 96 hours is revolutionary." For UX research, it shows a wonderful promise for particular questions as long as the experiment is designed well.

But I also want to make sure that I'm dealing with an ethical company. In my new role as a research lead for Vodafone, there is a reputation risk to the company. This means that I've been participating in some of these sites as a worker to check out conditions from within. A lot depends upon the micro-tasks conditions; but company also have their own attitudes to workers which was taken into account. We will not work with companies that argue about or unnecessarily delay payments to workers; or use petty reasoning to 'trick' workers out of their money.

To guard against reputational risk, we will engage only those companies that treat workers with at least some respect.

I don't expect this post to make any waves, at least not amongst the crowd-sourcing sites, because our work is fairly small potatoes for them. But we are a large company with expanding requirements, and it's always good to remember that those on the bottom can also, with a slight change in context, become the one who pays the piper.

References

[1]   Paolacci, Chandler, Ipeirotis (2010) Running experiments on Amazon Mechanical Turk. Judgement and Decision Making, Vol. 5, no. 5.

[2]   Crump MJC, McDonnell JV, Gureckis TM (2013) Evaluating Amazon's Mechanical Turk as a Tool for Experimental Behavioral Research. PLoS ONE 8(3): e57410. doi:10.1371/journal.pone.0057410