Archive for November 17th, 2011

Social Psychology – the second discipline   no comments

Posted at 12:47 pm in Psychology

After much havering I have settled on Social Psychology as my second discipline (which is where I started).  One important reason is that Gemma has lent me a really good textbook!  Also it is extremely relevant.  I contemplated Film Studies and Ecology because I wanted to try something out of the ordinary – but Film Studies turns out to be a bit of a non-subject and Ecology (although fascinating) was just too hard to relate to my research question.

Social Psychology seems to resonate in all sorts of ways.

I am primarily interested in scientists public engagement with science and over the last couple of weeks this has become a bit more precise – how can the scientific community use the web to help non-experts distinguish good science from non-science.

Social psychology hits because:

1) Subject matter – it studies (among other things) how people form opinions and attitudes and how they communicate

2) Methodology – social psychology combines qualitative and quantitative methods in a way I find convincing.  In particular it recognises the primacy of the experiment as a method and that  with only qualitative data you have ideas but not evidence.

3) It is an example – it is itself a science which needs undertake public engagement and differentiate science from non-science.  In fact it is more prone than most sciences to misinterpretation.

So Social Psychology here we go.

Written by mtf1c08 on November 17th, 2011

Marketing Lessons for the Web   no comments

Posted at 12:36 pm in Uncategorized

I have been reading a marketing introduction (Armstrong, Kotler, Harker, & Brennan, 2009) which certainly makes for much easier reading than academic papers. These are initial notes on how marketing might throws light on the user of the web for public engagement with science.
Marketing as a discipline:
• Marketing is primarily prescriptive not descriptive. The book tells or advises people on how to do it.
• The evidence to back up the advice is almost entirely based on case studies. In this sense it does not come close to the rigour of a science or even the social sciences.
Marketing, public engagement with science, and the web
• Clearly marketing uses the web – digital marketing is a new and important branch of marketing – but the web can also use marketing. To be more precise people using the web can benefit from marketing concepts and attitudes.
• Most importantly marketing has at its heart “creating and maintaining profitable long term customer relationships”. The concepts of customer and profitable need to be expanded (you might even say twisted) beyond their normal meaning if they are to apply generally to the web – “customer” translates into “user” and “profitable” translates into something like “satisfactory”. Taking into account this translation, this is a mind-set that ought to pervade anyone trying to offer services via the web and therefore the underlying technology and standards. The big success stories Apple, Google, Facebook and Amazon are very much aware of this (Amazon is the first case study in the book). Other institutions less so. In particular scientists typically do not see the consumers of their product (research) as customers or users.
• On page 12 the book describes five different marketing orientations:
o The production concept – focuses on producing and distributing my goods and service as efficiently as possible. The vast majority of scientists see the web in this light. What an efficient way to make research available.
o The product concept – focuses on quality and innovation. Some scientists, to their credit, see the web in this light. It gives an opportunity to demonstrate or present their discoveries in imaginative or exciting ways. Science museums are particularly adept at this.
o The Selling concept – partially shifts the focus from the product to the customer – getting customers to buy the product or service – but concentrates on the short term and looks for a customer to match the product rather the reverse. Scientists probably come closest to this attitude at conferences or other events when personally presenting their research, it is hard to see its equivalent on the web. This is partly because there is no well-defined transaction to record success as there is when a commercial organisation makes a sale.
o The Marketing concept – this completes the shift to customer focus. The organisation defines itself in terms of customer needs that it has the potential to satisfy – the products and services are responses to these needs. The best way to identify and meet these needs is to develop long term relationships. This is an approach that is alien to most scientists and is likely to cause a negative response. Science should be pure and about discovering how the world is – not about meeting needs. The idea that the web would be vehicle for creating long term relationships with customers to meet their needs would be very hard to take.
o The Social Marketing concept – this goes one further than the marketing concept and takes into account not only potential customers but also social forces such as environmental considerations. Scientists are better disposed to respond to this attitude than a pure marketing approach – climate change is the obvious example. Nevertheless there is a still a presumption that society should respond to their science and the web is a tool for doing this – rather than a tool for building relationships and understanding the users’ needs and viewpoints.

Written by mtf1c08 on November 17th, 2011

04 – IT Modelling / Reporting Experiments (Statistics)   no comments

Posted at 11:26 am in Uncategorized

Hypothesis and Experimentation

The scientific method

The hypothetico-deductive aspect of the scientific method focuses on the observation.  This observation leads to a guess or logical guess called the hypothesis that tries to explain how a system works.  From there, some predictions are made from this hypothesis and the experimentation or tests begin to try to prove it.

After the experimentation, the results can only be either consistent or inconsistent with the hypothesis.

These sets of experimentation will allow the hypothesis to be more consistent with the implementation of the project.  But it is important to link the results properly with the hypothesis.  This is where the statistics come in.


Statistics are use in many different industries. Statistics will allow us to make decisions about large numbers of subjects which we can be able to group into some sort of systems.  This way we can see patterns or data that is not visible through ‘static’ numbers.

It is extremely important to understand how statistics work.  This is due to the necessity to analyse the information inside them.  If we can not produce a proper statistical model, perhaps we won’t be able to make a good decision about our project. Also, if we can not understand statistics, there is no way we can see errors or disprove a theory or result.


Once we have developed the statistical models we also have the option of visualizing this data. Or perhaps analyzing more in depth the information provided.

Mean, Error, Percent Error, and Percent Deviation

All these arithmetical/statistical tools can help us to understand our data.  For example the Percent Deviation will allow us to understand or to see the whole extent of the data, not only the mean number.

σ =


Percent Deviation

All statistical models are methods of obtaining the probability of success of our experiments which will help making a decision about our hypothesis or group analysis

Reporting Experiments

Through the report is where the explanation about the study. Peter Harris (2008) points 5 elemental items for the report.

  • What you did
  • Why you did it
  • How you did it
  • What were your findings
  • What do you think it shows

This can then be translated to a formal document presentation like this:

  • Title
  • Abstract
  • Introduction
  • Method
  • Results
  • Discussion
  • References
  • Appendices

So, through this report we are intended to provide the information and the appropriate material. For this we also have to consider our reader.  Who is intended to see our information. This is important because perhaps we will have to give an introduction to our area of study. If we are presenting the document to Computer Scientists, perhaps we need to give and induction to Heritage or Visual Communication.

Within museums

The statistics and the report provided is also an intrinsic part of the analysis. Before even starting to provide model experimentation, it is important to provide a hypothesis.  Something like:

  • What are the main reasons why small museums don’t have access to big collections?
  • How many visitors does each museum have per year/per day/per month?
  • How many times does an expensive collection travel through different museums?

It is important to start analyzing this type of information in order to visualize the real requirements not only of the project but also of the museum. problem or situation.


Brookshear, J. G. (2010). Computer science an overview. (11th ed.). Addison-Wesley,.

Harris, P. (Peter R. ). (2008). Designing and reporting experiments in psychology (3rd ed., p. 284). Maidenhead :: Open University Press,

McKillup, S. (2006). Statistics explained an introductory guide for life sciences (p. 267). Cambridge :: Cambridge University Press