#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)

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:


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


  • 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

#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.


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

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

Quantitative methods for social media research

Update: see coverage of the CCC Symposium in Copenhagen on 16 October for lots more on the perils of big data and quant methods.

The second round of #nsmnss activities took the form of a tweetchat (24 September) followed by a knowledge exchange event (26 September). Topics covered: data visualisation, populations and sampling, and big data. Notes below mainly paraphrased from Twitter. Here’s some videos too (posted 25 Jan 2013), and there’s a webinar on 8 April which I am sitting out.

Are we doing more than data mining when we analyse social media data? Which research questions is it best able to answer? What are the biggest methodological challenges when working with quantitative social media data?

  • Compared to surveys, social media data are conversations rather than responses to standardised questions.
  • Overall quality of data – social media is an expressive medium.

Social media data can often be analysed using visual methods. How can we visualise data collected by social media? How does visualisation relate to statistical analysis? What are the payoffs from using visualisations?

  • We can see patterns not visible in a table, for example can compare categories of data by visualising the size of each block of data- see Information is Beautiful.
  • Tools can explore data interactively – see Interactive VisualizationsGapminder and Hans Rosling’s TED Talk – and track how users explore data.
  • Visualization for storytelling and illustration, or visualization for prediction? Visualization to answer research questions.
  • “Often beautiful, sometimes helpful.”  Dataviz may be adding impact and getting statistics to a wider audience, but is it adding anything methodologically? Visualisations can be misleading – remember the real purpose of the data.

What is the ‘population’ on social media platforms? How do platforms differ in population characteristics? How can we select cases or samples on social media? Is it possible to get a statistically representative sample using from social media platforms? Does it matter?

  • Sampling goal: to make statements about a certain group of people or objects (webpages, tweets etc)
  • Sampling frame: list of every object in population. Internet: special issues, hard to find list of all blogs, pages etc, meaning that bias is common.
  • Huge problem with sampling – lack of demographic info on most people’s profiles makes this even harder.
  • Advantages of sampling with the Internet – cheap, fast turnaround, no interviewer effects.
  • Disadvantages – many have no or limited Internet access, so cannot generalise findings. Population characteristics unknown, meaning of behaviour may be unknown.
  • Social media users are only representative of social media users, not of any larger group – see Sampling and social media and Tortoise or the hare: social media sampling.

Social media research can involve very large datasets. What do we gain and lose with big data? How is big data changing the way we do research?

  • Are we analysing big data because it’s available rather than because it is suitable to answer our research questions? Bigger does not necessarily means more useful.
  • Availability of big data precedes availability of suitable quant methods for analysing complex data structures – we need to understand the structure before we can analyse it.
  • cases: Waller’s study of Australian Google users | text analysis using Google books, studies by Michel et al and Heuser and Le-Khac
  •  Twitter as social network or news/broadcast medium? Example: Kwak’s study of 1.47 billion social relations.
  • Is retweeting a social relationship? Claims based on big data network graphs, eg w influences x,y & z  because w retweets them. Does an RT mean you’ve influenced someone?