Social Cohesion, inclusion and diversity

Greetings from the ANU Grand Challenges pitch night in Canberra.  Four teams are presenting their ideas. One will receive funding. The third is "Social Cohesion, inclusion and diversity". This proposes five phases: synthesis, stock-take, solve, scale-up and share. The approach appears similar to that of
Burstow, Newbigging, Tew, and Costello (2018). One aspect the team might like to investigate is online aspects of community building in the face of so-called social media.

Reference 

Burstow, P., Newbigging, K., Tew, J., & Costello, B. (2018). Investing in a resilient generation: Keys to a mentally prosperous nation. URL http://repository.tavistockandportman.ac.uk/1792/1/Burstow%20-%20Investing.pdf

Online Help for Fair Trade

Greetings from the famous room N101 at the Australian National University in Canberra, where Jennyfer Lawrence Taylor is speaking on "ICT Pathways for Consumer-Producer Feedback Loops within the Fair Trade System". She is asking if social media-like systems help connect first world consumers to third world producers, where there is a large cultural gap.

With schemes such as Fairtrade it is assumed that the developing nation producer deals directly with the first world distributor. However, normal trading arrangements would assume a local intermediary and I suggest perhaps they could deal with consumer questions. As an example, when teaching  web design to museum staff of the pacific, I was asked about web sites for museum shops. My students pointed out to me that they have essentially the same items on sale in the shop as on display in their museum. The shop then acts as an intermediation to educate the consumer about the items.

Research Questions

• How is ICT currently used in fair trade supply chains to facilitate feedback loops? How might ICT be applied in future to improve these information flows?

– What types of feedback do consumers currently provide to producers, and what are their motivations for giving feedback?

– What consumer feedback would producers like to receive, and how would they use this feedback to influence supply chain operations?
– How is feedback modified, filtered and translated back through the supply chain to producers by intermediaries such as Australian importers?

Experiments with users and sample size

Greetings from the Australian National University in Canberra, where Diane Kelly, University of North Carolina, is speaking on "Statistical power analysis for sample size estimation and understanding risks in experiments with users". Having struggled through a course in research methods, I was relieved to hear that there is no perfect sample size. Diane looked at some of the constraints on sample size, such as budget and time.

ABSTRACT:
One critical decision that researchers must make when designing experiments with users is how many participants to study. In our field, the determination of sample size is often based on heuristics and limited by practical constraints such as time and finances. As a result, many studies are underpowered and it is common to see researchers make statements like "With more participants significance might have been detected," but what does this mean? What does it mean for a study to be underpowered? How does this effect what we are able to discover about information search behavior, how we interpret study results and how we make choices about what to study next? How does one determine an appropriate sample size? What does it even mean for a sample size to be appropriate? In this talk, I will discuss the use of statistical power analysis for sample size estimation in experiments. Statistical power analysis does not necessarily give researchers a magic number, but rather allows researchers to understand the risks of Type I and Type II errors given an expected effect size. In discussing this topic, the issues of effect size, Type I and Type II errors and experimental design, including choice of statistical procedures, will also be addressed. I hope this talk will function as a conversation starter about issues related to sample size in experimental interactive information retrieval.

Sample size risks in experiments with users

Diane Kelly, University of North Carolina, will speak on "Statistical power analysis for sample size estimation and understanding risks in experiments with users" at CSIRO IR & Friends at the Australian National University in Canberra, 4pm, 1 December 2014.

ABSTRACT:
One critical decision that researchers must make when designing experiments with users is how many participants to study. In our field, the determination of sample size is often based on heuristics and limited by practical constraints such as time and finances. As a result, many studies are underpowered and it is common to see researchers make statements like "With more participants significance might have been detected," but what does this mean? What does it mean for a study to be underpowered? How does this effect what we are able to discover about information search behavior, how we interpret study results and how we make choices about what to study next? How does one determine an appropriate sample size? What does it even mean for a sample size to be appropriate? In this talk, I will discuss the use of statistical power analysis for sample size estimation in experiments. Statistical power analysis does not necessarily give researchers a magic number, but rather allows researchers to understand the risks of Type I and Type II errors given an expected effect size. In discussing this topic, the issues of effect size, Type I and Type II errors and experimental design, including choice of statistical procedures, will also be addressed. I hope this talk will function as a conversation starter about issues related to sample size in experimental interactive information retrieval.

Selling Social Science Impact

Greetings from the Australian National University where Professor Peter Davis, University of Auckland, is speaking on “Valuing the social sciences: An agenda for hard times". He argues that social sciences needs to makes its case as a useful field better and also apply more quantitative measures. He gave the example of driver education in schools, where the common suggests this is useful, but research shows it is not.
Professor Davis made the point that social scientists made a bigger impact that hard science, but outside scholarly publications.

This all seems very sensible and makes me wonder what social scientists normally spend their time doing. Peter commented that many social science students do not undertake any research methods course, which I found difficult to believe. The reason was that staff worry the statistic could drive students away from social science completely (which I can believe).  One quarter of the Master of Education I am doing is research methods, with courses on general, quantitative and qualitative methods (another chunk of the program is devoted to applying and communicating the results). Despite having a mathematics and computing background, I found statistics a very difficult subject and likely would have given up studies all together if that was what I first encountered. As it was, the statistics was something I knew I had to do.

I asked Professor Davis if social scientists could work more with the hard sciences, giving the example of climate change, where there is overwhelming evidence that global warming is real, but little action has been taken. He replied that the "nudge" theory, used in public health, could be applied. The idea is that people could be helped to make small changes to their behavior, rather than large, difficult,  lifestyle changes.

One recently example of where social science can help is the evaluation of social welfare policy. The Australian government introduced "income management", initially targeting aboriginal communities. The idea was that government payments would be made in a way which prevented the recipient from buying alcohol. Also requirements for recipients sending their children to school were made. However, a review of the program shows that it does not reduce alcohol consumption or increase school attendance. Unfortunately, it seems likely the government will ignore the evidence and expand the program anyway, for reasons of political ideology.

Web Social Science Applied to e-Learning

I found Robert Ackland's "Web Social Science: Concepts, Data and Tools for Social Scientists in the Digital Age" (SAGE Publications, 2013), on the new books stand at the ANU Library. This is very relevant to the pedagogy of e-learning, as the techniques for carrying out social research can be applied to research into the effectiveness of on-line learning. The introductory chapter includes a potted history of the Internet and the web, but more importantly discusses what virtual communities and on-line social networks are. This could be useful for illuminating a discussion of what on-line education is.

What is on-line education?

Ackland discusses how a "community" develops  "common beliefs, norms and shared understandings. Ackland sees an on-line group of being less cohesive than a community being "... a group of people who conduct personal computer-mediated interactions, where the interaction is focused on a topic that reflects the community of interests of the group ...". Therefore I suggest the students in an on-line course could be considered an on-line group, with the aim of a vocational educators being to have the students become part of community. Social science research techniques could therefore be used to evaluate the effectiveness of education, by seeing how cohesive the group is and how well those students become part of the community.

Social Science Research Using Twitter

Rob Ackland will speak on "Some approaches for social scientific research using Twitter" in the CSIRO seminar room at the Australian National University in Canberra, 4pm 2 September 2013.

Some approaches for social scientific research using Twitter

Rob Ackland (ANU)

4-5pm Monday 2 September 2013
CSIRO room S201, CSIT Building, ANU, North Road, Acton, Canberra

In this presentation I provide preliminary findings from two Twitter research projects. The first involves the use of social movement theory and logistic regression to investigate the factors that predict whether a Twitter user will contribute to the emergence of a new hashtag, using a collection of Occupy Wall Street Twitter data as an example dataset. The second involves the use of index number theory (from economic decision theory) to develop new measures of attention and information consumption in social media. CSIRO room S201 (next to the seminar room, note change of venue), 4-5