#FLcuriosity: my research project

Harness your curiosity and use it to undertake your own research projects in a scholarly manner!

Quite. #FLcuriosity, aka Developing your research project, eight weeks from 27 June, University of Southampton.

Week 1: starting an academic research project

  • think about what inspires you (broad topic area)
  • consider what skills you might develop through undertaking a research project (transferable skills)
  • think very clearly about what exactly you are getting into by undertaking a research project (checklist)

A good research project will look at the work of previous scholars, will build upon that, while adding original views and interpretations, so that you get the opportunity to make an original contribution to the subject that interests you.

Week 2: drafting a research proposal

You might just end up researching and carrying on finding things that you find are really interesting, but never narrow down a research question…work out what you’re interested in…not coming up with a list of everything but rather picking something and sticking to it and creating a research question from that.

  • document your thoughts as you go along in a research log (mindmaps!)
  • home in on a research topic that meets your requirements
  • develop a draft hypothesis that is broad enough to give you scope to explore but narrow enough to be manageable
  • write a draft research proposal  (approx 200 words)
hypothesis

draft hypothesis: To what extent have tuition fee increases reduced the number of students applying to UK universities?

Either work downwards, or if you already have a topic you wish to explore, work backwards to broaden out your focus to identify what subject it is that your project actually falls under – and accompanying approach and methodology.

Week 3: undertaking research and recording your findings

How to find and select reliable sources, as well as how to record the origins of these sources to make sure you can prove where your evidence came from.

Should be ‘meat and drink’:

  • familiarise yourself with commonly used book and journal terminology
  • put a system in place for systematically checking out sources and recording your findings
  • consider why searching out primary sources rather than using secondary information can give you the ‘edge’ in your research project
  • experiment with ‘exploding’ out the terms of your draft title to get you started with your research (try post-its or a mindmap); it’s about knowing a lot about a little, not vice versa, so keep the theme of your research narrow, focused, and ideally measurable:

Screenshot

Sturgeon’s Law: 90% of everything is rubbish.

Week 4: choosing an appropriate methodology

  • find out what type of research methods are appropriate for your topic
  • consider the benefits and drawbacks for the research methods you have selected and whether your research questions and hypotheses may need re-thinking
  • update your research proposal to include your methodologies

The different types of methodology are broadly split between:

  • quantitative – produce quantifiable outcomes; you are likely to have clearly set out responses (variables) to questions you ask, eg yes/no responses, likelihood or degrees of satisfaction questions on a given scale, allowing for statistically reliable and significant analysis of and between variables, which may infer something about the sample population, and if a representative sample, the wider target population
  • qualitative – do not provide as structured responses and as such fewer inferences can be made beyond the individuals sampled, however less structure means less restricted answers, often providing very rich and contextual data; we might  want to know beyond a yes or no answer, instead trying to achieve a ‘well maybe, I’m not sure though, because of x, y and z’ type answer that tells us far more
  • consider also mixed methods

Questions:

  • which sources of information might be instrumental in answering your research question?
  • how will you obtain sources of information appropriate for your research project?
  • how may you wish to analyse them?
  • how you might wish to look at your source material and what methods of analysis will you use to investigate it more closely?
  • consider the potential biases you may encounter with the sources of information and analyses you have chosen – think about how these biases could impact upon your project and weigh up some of the advantages and disadvantages of your choice accordingly

Week 5: academic reading and note taking 

Academic reading is a very practical way of dealing with books and materials. Instead of reading through every single piece of the material, begin by going straight to the sign posts:

  • chapters – read the opening and concluding paragraphs and ask: “is this relevant?”
  • index – look for keywords
  • signal words – ‘therefore’, on the other hand’

Three main approaches:

  • scanning – locate specific information (statistics, details, particular names or keywords) by just looking at the page, in particular the key terms
  • skimming – read a longish text or parts of one (eg the first and last couple of lines of paragraphs) to get the gist (the main idea) of what it contains; the aim is not to get a detailed understanding but rather an overview that may be relevant to your enquiry
  • critical close reading
  • see Barbara Fillip on What happens when I read a non-fiction book and Different ways of reading

At the heart of much academic writing is an argument. An academic argument can vary in form according to the subject area; however, there are shared common elements (claim, data, justification). You need to be able to deconstruct and understand an academic argument when reading and create an argument in your own writing.

Effective note taking means identifying the information which is relevant without noting everything down. Using appropriate academic reading skills can save you time. When note taking, where possible put the information in your own words and, if you don’t, make sure that you have a system that makes this clear otherwise you could end up plagiarising.

Note taking tools:

  • blogging and mind mapping
  • annotating – highlighting, underlining, writing in the margin; summarise afterwards to avoid plagiarism
  • Docear – imports and organises PDFs with notes into a mind map
  • Read Cube, Scrivner and Zotero – all show PDFs in one half and a notebook on the other half to take notes while reading
  • a notebook – half-processed writing

Week 6: referencing

By the end of this week you will be aware of the different styles of referencing and know how to set your references out to an academic standard.

Understanding academic integrity (Soton’s regs) and plagiarism. Referencing styles, including Harvard, Chicago, Modern Humanities Research Association (MRHA; Soton guide), Modern Language Association (MLA), OSCOLA…

A Harvard reference, yuk:

Lipson, C (2006) Cite Right: A Quick Guide to Citation Styles – MLA, APA, Chicago, the Sciences, Professions, and More London: The University of Chicago Press

Useful online tools include Endnote and Mendeley (tutorial).

Week 7: writing up your research

Ways of making sense of the sources and results you have gathered and how to go about structuring your essay, as an essay plan:

  • establish a time limit and/or word count
  • lay your sources out, either physically or digitally, and work out which ones fit to which parts of your essay
    • for or against style essay –  arrange them on two sides
  • introduction –  set out the context and tell the reader what they’re going to be told, what your overall position will be and exactly how you plan to guide the reader through your work
    • ie context, hypothesis, structure
  • main body – explore in more depth the importance of your research, what the background to it is, and what work has already been done in this field
    • show examples as evidence of the issues that you’ve considered in shaping your general point of view
    • for each section outline your point, provide evidence for it, then link it back to your research question, and on again to your next point
    • make a counterargument for every point to show that you’ve thoroughly considered all sides of the argument
    • literature review – document work that exists in your field already, its significance, and your take on it
    • methodology section – explain complicated methods, or forms of analysis
    • ie  overview, examples, paragraphs
  • conclusion – a very clear statement of your argument in a way that satisfies your research questions
    • what the implications of your work are, who agrees with you, and where further research might be useful
    • reveal your results, followed by a discussion which indicates what their significance is and the impact on your research questions
    • tie all the strands of evidence together into one coherent piece of work
    • ie answer, argument, implications

Write an abstract (around 200 words) after you have finished writing up your research project, summarising what your project contains:

  • what you set out to do and why (hypothesis and research questions)
  • how you did it (methodology)
  • what you found (results and conclusions)
  • recommendations (whether you have any will depend on the type of research project)

But Why is academic writing so academic? See also #acwri post, and, rather more me, Engage 2014.

Week 8: presenting your research

A bit academic, at this juncture.

Tools: PowerPoint | Sway | Prezi | overview

#smwbigsocialdata: getting social at CBS

On 27 February the boffins at Copenhagen Business School (aka the Computational Social Science Laboratory in the Department of IT Management) opened their doors for Social Media Week with Big social data analytics: modelling, visualization and prediction. This was the second time CSSL has participated in #smwcph, with their 2014 workshop (preso) looking at social media analytics. See also my post on text analysis in Denmark.

Wifi access was not offered, resulting in only 19 tweets, but as many of these were photos of the slides I’m not really complaining. Also no hands-on this year, all in all a bit of a lacklustre form of public engagement.

Ravi Vatrapu kicked off the workshop with a couple of definitions:

  • What is social? – involves the other; associations rather than relations, sets rather than networks
  • What is media? – time and place shifting of meanings and actions

The CSSL conceptual model:

model

  • social graph analytics – the structure of the relationships emerging from social media use; focusing on identifying the actors involved, the activities they undertake, the actions they perform and the artefacts they create and interact with
  • social text analytics – the substantive nature of the interactions; focusing on the topics discussed and how they are discussed

It’s a different philosophy from social network analysis, using fuzzy set logic instead of graph theory, associations instead of relations and sets instead of social networks.

Abid Hussain then presented the SODATO tool, which offers keyword, sentiment and actor attribute analysis on Twitter and Facebook (public posts only, uses Facebook Graph API). Data from (for example) a company’s wall can be presented in dashboard style, eg post distribution by month.

Next, Raghava Rao Mukkamala explored social set analytics for #Marius and other social media crises. Predictions (emotions, stock market prices, box office revenues, iphone sales) can be made based on Twitter data.

Benjamin Flesch’s Social Set Visualizer (SoSeVi) is a tool for qualitative analysis. He has built a timeline of factory accidents and a corpus of Facebook walls for 11 companies, resulting in a social set analysis dashboard of 180 million+ data points around the time of the garment factory accidents in Bangladesh.

The dashboard shows an actor’s engagement before, during and after the crisis (time), which can also be analysed over space (how many walls did they post on). Tags are also listed, allowing text analysis to be undertaken.

Niels Buus Lassen and Rene Madsen then outlined some of their work with predictive modelling using Twitter. You have to buy into #some activity being a proxy for real world attention, ie Twitter as a mirror of what’s going on out in the market – a sampling issue like any other. Using a dashboard driven by SODATA they classify tweets using ensemble classifiers, such as iPhone sales from 500 million plus tweets containing the keyword “iphone” (see CBS news story | article in Science Nordic).

They also used a very cool formula I nearly understood.

Last up, Chris Zimmerman gave an overview of CSSL’s new Facebook Feelings project, a counterpart to all those Twitter happiness studies. A classification of 143 different emotions on Facebook, based on mood mining from 12 million public posts, yikes. “Feeling excited” was the most popular feeling by far. Analysis can be done and correlations made on any number of aspects of the data, with an active | passive axis in addition to the positive | negative axis used in sentiment analysis. Analysis by place runs into the usual issue – only 5% of data has locality data.

Overview slides currently available from the URL below…

Danish Twitter census 2014

(Post copied from Danegeld blog, 4 Feb 2015.)

The latest Danish Twitter census was launched on 8 May. Couldn’t attend, but here’s the gen, from the 60 tweets at #twittercensus, 129 at #tcdk. See also my report on last year’s census, on page 7 of my 2013 Social Media Week diary (PDF).

  • number of Danish users of Twitter doubled since last census at 222,5o5 (SE: 641,746, NO: 406, 250, FI: 153,746)
  • still few ‘active’ –  39,963  Danes (18%) tweet at least once a month (SE: 38%, NO: 29%, FI: 34%)
  • very active (tweet at least once a day) – 6245 (3%; SE: 13%, NO: 8%, FI: 6%); falling
  • 64% of all Danish twitter users have only posted between 1 and 9 tweets
  • an average Danish twitter user has 64 followers (208 globally) and follows 89; low figures may be due to the rapid growth
  • techsome segment isn’t growing, but teen segment is
  • individuals are driving Twitter use in Denmark, not brands
  • only 30% tweet location
  • @bavnhoej: “mon der en dag kommer  kvalitativ analyse af hvad der bliver sagt på twitter. Vildt spændende værktøj #tcdk , men fokus er igen på kvantitet” – see @tagsterdk and @anders_boje

Mapping a community: a SNA case study

(Post copied from Danegeld blog, 4 Feb 2015.)

Update, July 2015: see Hazel Hall on DREaM Again (again), investigating the long term impact of the project. Splendid! May 2016: not much on #sna lately, apart from a snippet on R4’s Digital Human: Are you more likely to find what you’ve lost using online social networks? Are we as connected as we think we are? Or does it make more sense to step out of the digital world and search with the help of physical social networks? A larger network of weaker/looser ties is more effective in finding something lost – these ties have information you don’t have. Other factors also come into play, eg how navigable is the network? The same processes go on IRL, with the Lost and Found Office now also online.

Over the last couple of years I followed the work of the DREaM project, aimed at building a community of LIS researchers in the UK. Effective event amplification provided me with an introduction to social network analysis (SNA; nearly two years ago now!) and a host of other research methods.

The DReAM project SNA’d themselves, specifically a cadre of 33 individuals who attended all the f2f events and created the network ‘core’. In the first workshop the participants provided data on (1) individuals’ awareness of the research expertise and knowledge of other participants, and (2) social/ interactional links across the network, data which was collected again at the final workshop. The hypothesis was that analysis of the two sets of data would reveal changes in levels of integration among the DREaM cadre and network density among the group as a whole over the series of workshops – ie that integration and network density would increase.

Initial findings were presented at the final DREaM event and a paper finally published in the Journal of Documentation in October – see  Hazel Hall’s post for full details and to download the manuscript. The paper offers a potential model for nurturing and assessing network and community (of practice) development, specifically a developing, or emergent, network based on spontaneously formed ties, which could also be applied to NSMNSS , the legal education community, Danish literary translators, walking types, etc. As well as a useful overview of the development of SNA from the 1930s it provides a model for moving forward from the presentation of network diagrams, discussing features of network articulation and measurement, relational ties and network roles.

Methodology and findings:

  • data were input manually into Ucinet v.6 and visualised network diagrams (sociograms) were produced using Netdraw; measures of density and degree centrality were calculated using Ucinet
  • the sociograms highlighted the centrality of position of certain participants, prompting speculation as to their identity and the reasons behind this centralisation as well as discussion on the meaning behind some of the more isolated positions occupied by some of the outliers
  • the findings from the first round of data collection demonstrated that the participant networks were not very highly connected, and heavily centralised around a small number of actors from one role
  • analysis of data collected in the course of the final workshop reveals a demonstrable increase in network density, indicating a much more closely linked and robust network; more evenly linked, with less dependence on two or three very densely networked actors, when analysed by role several categories had moved to a more central position, one category had formed a clique and one category seemed particularly adept at network building, with most members moving towards the centre of the network
  • not all the key players were those one might have expected to play such roles; a small number of relatively novice researchers proved to be particularly strong networkers and were central to the network structure (this was not explored further due to ethical concerns)
  • greater change in the density of the network with regard to expertise awareness than for interaction, suggesting that even if participants had not had one-to-one interaction with another participant they were still more likely to know of their area of research expertise – ie who knows what, typical of a work related rather than ‘social’ network
  • note of caution: in an information sharing network, for example, an actor with a high degree of betweenness centrality may be playing the role of either broker or a bottleneck – for most network patterns multiple interpretations are possible, and it is therefore appropriate to follow up such analysis with qualitative research that seeks to explore likely explanations (data from other sources included a ‘before and after’ audit of skills and feedback on face to face events)

Discussion:

  • the results suggest that network density and integration can be increased by structured and informal social and work based interaction; a model of combining workshops with social events and the use of social media reduces the isolation often experienced by the researcher, in particular the solitary, novice or practitioner researcher
  • increased network density and integration reduces the dependence of the network on a couple of actors, making the sustainability of the network more likely and increasing network capital – more likely that participants will be able to leverage potential benefits
  • potential drawbacks – a higher density of network structure and the formation of cliques may pose a barrier to incomers and increased homogenisation – homophily; it is critical to ensure that barriers to entry to the network remain low with a network of loose ties; individuals should be encouraged to play an active role in boundary spanning, ensuring innovation, opportunity and diversity of viewpoint
  • the challenge is to maintain the existing links and further develop the network so that it evolves into a self sustaining and continuously developing supportive community

Specific interventions used to increase and strengthen network ties over the course of the project included pre-event social meetups, a Twitter list, curation over the full event lifecycle, a Spruz community, participant led sessions, event reporters.

The role of event amplification in particular is interesting, an issue which keeps popping up and perhaps has potential in proving its ROI. Effective event coverage can in fact change the nature of an event, ensuring that participants can make the most of f2f interaction and are better able to reflect after the event. Alan Cann touches on this issue too in his recent post on the way forward for #solo13 – the conference as aggregator, building an online community of mutual support. The same goes for MOOCs, but the role of aggregation and curation is often overlooked.

Some #sna bits n bobs picked up from the paper:

Commonly measured network features:

  • size – at the actor level: the number of linkages an actor has; at network level: the total number of linkages in the network
  • reachability – the accessibility of points of the network based on a notion of path, ie the connected sequence of linkages by which it is possible to move from one point to another in the network; a point is reachable when there is a path between points
  • density – the degree to which actors are linked to one another; parts of a path are dense if each of its points is reachable from every other
  • centrality – the degree to an individual actor is near others in the network and the extent to which the person lies on the shortest path between others and thus has potential for control over their communication

Examples of relational ties:

  • evaluation of one person by another – friendship, liking, respect
  • transfer of material resources – business transaction, lending, borrowing
  • association/affiliation – jointly attending the same social event, belonging to the same club
  • behavioural interaction – talking together, sending messages
  • movement between places or statuses – migration, social or physical mobility
  • physical connection – co-location at work
  • formal relations – authority
  • biological relations – kinship, descent
  • communication relations – sharing of publications, discussion of ideas

Example of network diagrams from Martin Hawksey:

network diagrams from Martin Hawksey:

#SRAconf: social media in social research

The Social Research Association‘s conference on 24 June explored the value of socme to social researchers. The SRA is a membership body, have to admit to being a bit vague about what a social researcher is, but never mind. Twitter: @TheSRAOrg.

Sessions:

Storify from social network reporter @commutiny (and reportto follow, plus one from Eoghan O’Neill bringing up some useful points:

  • a ‘perception of privacy’ – platform specific? are users on Twitter more aware of their content being public than Facebook users? to what extent do people change their content and tone from platform to platform?
  • researching ‘issues’ – which issues are people  bothered enough about to talk about online; things that are controversial, fun, funny, cool, sexy, rapidly progressing, modern, topical or just generally interesting
  • difference between online and offline personas
  • even ‘elite’ users of twitter only use hashtags 60% of the time; using hashtags for research may miss crucial info
  • types of user – apprehensive passives, confident cavaliers, controlling cautionaries, savvy opinionators…

A report from a research consultancy has also popped up.

@Flygirltwo tweeted a Bluenod SNA of #SRAconf tweets. I’d forgotten about Bluenod. Quite fun, but not sure it tells you that much really, particularly as it only looks at the last (?) 300 tweets. Comparing #SRAconf with hot topic #letr, the latter is much more dispersed, as you might perhaps expect from a topic as opposed to an event:

#letr visuaised by Bluenod

#nsmnss: the story of a network

Updates: Dec 2013: tweetchat on defining #some: Storify | Huma Bird analysis…August 2013: see paper (26 pages, PDF) on developing the network; the section on the community of practice looks particularly interesting

On 23 April the NSMNSS network held a digital debate, the last I think of a series of events before funding runs out in May. I’ve written four posts about #nsmnss, and following the blog and Twitter stream has played a key role in my learning about research methods in relation to social media over the last year – thanks to the team!

The ‘one year on’ presentation gives some insights into the success of the network and its activities. In terms of statistics, there are now 451 fully signed up members (35% non-UK) with 77 in the Methodspace group, and @nsmnss has 1000+ followers (with 900+ tweets).

I particularly liked the way the network played around with the full spectrum of f2f and virtual events (two conferences, four knowledge exchange seminars with around 25 participants each, three online seminars, seven Twitter chats), for example holding tweetchats prior to f2f events. Plus the videos shown at the digital debate were from the previous week’s conference. This hybrid/flipped events model could work well in other fora.

It is hoped to sustain the network after funding runs out – this presumably has the biggest impact on f2f events, but in the era of social media it should be feasible to carry on some activities. A poll is calling for volunteers to get involved in projects, take responsibility for organising Twitter chats, develop resources or deliver training. A test of the strength of the network!

A range of platforms was used – perhaps too many (home page vs blog vs Methodspace anyone?). One way of streamlining activities would be to slim these down and perhaps change the ratio of curation to content – another task which could be done by v0lunteers, assuming the Twitter account is to carry on.

Finally, the dog food question: is any social network analysis or other research planned as part of the network evaluation?

Qualitative methods for social media research

Webinar, 5 February: I tried to watch the webinar twice, but no luck- I’ve used Elluminate several times before, so maybe it’s a Java issue – I’ve had no end of problems since I upgraded last week. There’s a discussion page on Methodspace, but you can call me qualitatively out for now!

Event, 28 January: suspect there was no wifi at the venue, as no tweets were made during the session. A report is now available, dated 13 Feb.

Following the #nsmnss chat and event on quantitative methods came a chat on qualitative methods on 20 November, with an event scheduled for 28 January – see Deep data: digging into social media and the Tunisian Revolution case study for an intro to the issues.

Below are my notes paraphrased from the chat.

What are the biggest methodological challenges when using qualitative methods in social media research?

  • issues relating to public and private space; ethics;  both for observation and use of posts
  • ethics panels need to be educated about the real risks – difficult to just apply F2F ethics to online; there are unique and untested ethical considerations in socmed research
  • generalising with small (but rich) sample data; the volume of data
  • following people across multiple platforms; does this create potential ethical issues? depends on consent
  • what research questions are best served by social media; theoretical frameworks
  • negotiating your online identity as a researcher; how you become seen as a professional in online spaces
  • social media lets us study circulation, but ideas circulate in other places, too; ie life online is part of life offline – > no easy distinction between on and off line research? can we limit just to online? (depends on the research qu)
  • the digital divide – need to acknowledge when sampling OR can get to hard to reach groups, different access to populations
  • qualitative research lends itself very well to socmed research

Are social media affecting the way we do qual research, and how?

  • can study globally with diverse participants and little money!
  • allows geotagging of data
  • unprecedented size and depth of datasets updated in real time
  • re-interpretation of qual methodologies and methods – chance to be a pioneer!
  • options for using visual, verbal, textual data to create rich stories
  • research tasks are more fragmented, requiring more organisation and integration skills
  •  type of data influences design + analysis; advantage: rich + interesting, disadvantage: more steps to analysis

Does qualitative research using online social platforms change the relationship we have as researchers with participants? How?

  • depends on design –
  • analysing content rather than observing real time interaction has fewer proximity risks
  • if interviewing online lack of social cues is key (but with video etc can grasp many cues and non-verbals)
  • blurs boundarie s- to be recognised in a community u need to be part of it
  • similarities to participant observation issues in offline research
  • difficult to research a community you are part of, but presence not so obvious
  • ethics – how we manage dynamics, harm, disclosure etc when not in the same physical space
  • the more you open up online the greater the return – really interesting connections are made that wouldn’t happen with more distance
  • the ‘participant observer’ – like anthropologists, risk of going native?
  • can online create new cues? ways of typing, acronyms, etc?  new/replacement cues needed, but not the same amount of info and there can be cultural issues; need common meanings
  • for people with disabilities, sometimes online means MORE cues and better communication
  • email/text are great for individuals who are non-verbal or need more time to formulate responses

What tools do you use for analysis online qual data? Do existing tools work or is there a need for new tools?

  • QSR’s NCapture, works with NVivo 10 to capture and analyse tweets, activity on Facebook pages and in LinkedIn groups
  • for the statistically inclined – TwitteR