Staying afloat

One of the questions I get most frequently from students upon their return from the field is “What now?”. They come back gloriously with tens of hours of interview recordings, pages after pages of ethnographic fieldnotes, and gigabytes of photos and news clippings, and they all say — understandably — that they feel overwhelmed by the challenge ahead of staying afloat and making headway in that sea of unstructured data.

RT @JessicaCalarco Doing qualitative research often feels like playing Jeopardy – you can see the answers (i.e., the patterns you find in your data), but you don’t always know the question (i.e., the problem those patterns solve). (21 December 2018)

I share with them well-established tips such as ease into it, embrace the messiness, keep an audit trail, put oneself in the reader’s [examiner’s] shoes, read what you want to write et cetera. These tips have all been highly appreciated, but then there are every now and then situations where students are still looking for something more concrete and readily usable in their research while I consciously try to be less prescriptive and more ‘Socratic’ (so to say). Those situations always feel to me like we are communicating back-scratching coordinates.

While I maintain that I shouldn’t be, and cannot be, too prescriptive, I thought I’d put together a nice ‘mixtape’ of resources for them. More will be added on.

For code-based theory building (as in GT)

For ‘Big Qual’ analysis 

For thematic analysis

For framework analysis

For discourse analysis

What we mean by a ‘case’ when we say we do case studies 

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Then and now

Just came back from a conference on “migration, mobility, and borders”, organised by and for our doctoral researchers. Interestingly, I was invited to give a ‘career talk’. My immediate suggestion was to bring in a career consultant instead, but for a combination of a couple of reasons, I ended up doing the talk. Come to think of it, I have been living and working among doctoral and early career researchers for almost 15 years, while being required to monitor the latest developments in the sector, so I told myself that I might indeed have one or two things to say about for their benefit.

Considering the theme, I prepared my talk along the lines of the increased expectation of (early career) researchers to be available/willing to be globally mobile. That is just one of the many, previously non-existent expectations imposed on the current generation of PhD candidates. I included this image (as a GIF) in my slides because every time I see it, I think of them. I honestly do.

Here are a couple more items that highlight how far things have changed in the PhD game.

# 2015 advice for your 856-year-old Ph.D. (Christian Sandvig, 5 August 2015)

Academic superheroes? A critical analysis of academic job descriptions (Pitt & Mewburn, 2016)

100 years of the PhD (Bogle, 2017, Vitae)

# The UK doctorate: history, features and challenges (Deem & Dowle, 2018 [email of 12 January 2019)

# “How I Got My First Academic Job, 1965 ed” (@profmusgrave, 20 March 2019)

# Thesis declaration, now and then (source: Got this off Twitter two months ago, but despite my best efforts, I can’t trace back to the original link. Let me know!)

+ Speaking of thesis declarations, see also Stephen Hawking’sone that broke the internet.

Politics of counting [2]

A little new collection for an upcoming module. 🤓

# Using hierarchical categories in qualitative data analysis (Richards, T. & Richards, L., in Kelle, U. (ed.), Computer-Aided Qualitative Data Analysis: Theory, Methods and Practice, 1995)

# The Social Life of Numbers: A Quechua Ontology of Numbers and Philosophy of Arithmetic (Urton, G., 1997; see also The Social Life of Things, Appadurai, A., 1988; The Inbetweenness of Things, Basu, P., 2017)

# Tricks of the Trade: How to Think about Your Research While You’re Doing It (Beck, H. S., 1998)

Where Mathematics Come From: How the Embodied Mind Brings Mathematics into Being (Lakoff, G. & Nunez, R., 2001)

# Standards and Their Stories: How Quantifying, Classifying, and Formalizing Practices Shape Everyday Life (Lampland, M. & Star, S. L., 2008)

What is SNA using qualitative methods? (Crossley, N. & Edwards, G., methods@manchester, 3 January 2012)

Data not seen: The uses and shortcomings of social media metrics (Baym, N. K., First Monday 18(1), 2013)

Oh Ordinal data, what do we do with you? (Petty, N. [Dr Nic], Creative Maths, 8 July 2013)

The Tyranny of Numbers: Why Counting Can’t Make Us Happy (Boyle, D., 2014)

Infinitesimal: How a Dangerous Mathematical Theory Shaped the Modern World (Alexander, A., 2014)

Scientific method: Statistical errors (Nuzzo, R., Nature, 2014; see also related articles published in 2015, 2016, 2017)

# Most psychology papers can’t be reproduced (IFLScience, 28 August 2015; crossposted 29 August 2015)

Measurement: A Very Short Introduction (Hand, D. J., 2016)

# The Quantified Self (Lupton, D., 2016; see also Seeing Ourselves Through Technology, Rettberg, J. W., 2014; Self-Tracking, Neff, G., 2015; The Qualified Self, Humphreys, L., 2018)

Surveying immigrants without sampling frames — evaluating the success of alternative field methods (Reichel, D. & Morales, L., Comparative Migration Studies 5(1), 2017)

Computer says so. ([yawningtree], 7 February 2017)

# List Cultures: Knowledge and Poetics from Mesopotamia to BuzzFeed (Liam Young, 2017, crossposted 1 September 2017)

Addressing the challenges related to transforming qualitative Into quantitative data in qualitative comparative analysis (de Block, D. & Vis, B., Journal of Mixed Methods Research, 2018)

How to sample networks using social media APIs (Coscia, M., 11 December 2018)

# Missing Numbers: a blog on the data the government should collect, but doesn’t [a blog about the gaps in government data] (Powell-Smith, A. (c) 2019)

The four dimensions of feedback [2]

Have been having a challenging couple of months. A bit of a work-life balance crisis, if you like – because there has been no life!!! Exaggeration aside, all in all, it feels like September 2004 has been toppled.

While barely bumping through this autumn term, I must admit there have been a few high points too. One definite one was an anonymous comment in a student survey report circulated in-house recently. It said: “[…] She is also extremely professional when expressing concerns or having to remark a downside of a paper. […]”

I have had one-to-one meetings with hundreds of students since I joined the School, so I would never know who this respondent was. There were also other equally nice comments in the report. Nevertheless, I think this particular sentence just struck me because it was about something that I happen to care about and want to do well.

I once saw a tweet that summarised my stance on this topic in a way I couldn’t better, so let me simply pin that one here.

RT @seankross Strive to create a world where peer review feedback sounds like you’re trying to help your peer improve their work and less like you’re writing a product review for a blender. (21 December 2017)

“Fake news”, academia style

I believe the title says all. 

This is something I always mention when I do a session on literature review, but now my collection has grown too big to fit within one single slide, so here we are.

Fostering interview researchers in an interview society

Challenging, no doubt, but one good thing is that there is never a shortage of resources for classroom discussions.

Fox News interview with Reza Aslan (July 2013)


Fairytale prisoner by choice: The photographic eye of Melania Trump
(Kate Imbach, Medium, 16 April 2017)

Trump gave an ‘impromptu’ interview to the NY Times. Did it grill him hard enough? (Keith Wagstaff, Mashable, 29 December 2017) — in reaction to Excerpts from Trump’s interview with The Times (The New York Times, 28 December 2017)

Uma deserves better (Anne Helen Petersen, 4 February 2018) — in reaction to This is why Uma Thurman is angry (Maureen Dowd, The New York Times, 3 February 2018)

My precious

I am someone who just has to have a knick-knack box. I have always had one since, well, as far as I remember. Much of that compulsion has now gone digital. I see my Tumblr page in particular as my virtual knick-knack box and treasure it more than any other spaces I have carved out in this vast digital world.

My only complaint, however, is about its search function. It sucks. So this post is to move one of my collections from there to here for easier navigation. I have collected quite a few ‘pedagogical gems‘ over the past couple of years. <puffed with pride>

Learning Theory (crossposted 7 Feb 2016)

Course Workload Estimator (Center for Teaching Excellence, Rice University)

Learning Designer (UCL)

Rubistar: Create rubrics for your project-based learning activities (via ALT, 23 May 2019)

Cognitive bias cheat sheet (crossposted 4 December 2016)

Media Theorised (crossposted 25 March 2017)

An Illustrated Book of Bad Argument (crossposted 27 December 2013)

Tea Consent (Blue Seat Studios, as part of a campaign by Thames Valley Police, 12 May 2015)

A timeline of earth’s average temperature (xkcd, 2016)

Timeline Tools (Florian Kräutli, 8 April 2016)

DH101: A highly opinionated resource guide by Miriam Posner (crossposted 30 June 2017; see also a series of technical tutorials that she has written)

How we helped our reporters learn to love spreadsheets (Lindsey Rogers Cook, The New York Times, 12 June 2019)

Final list of keywords for digital pedagogy in the humanities: Concepts, models, and experiments (edited by Davis et al., 2017, via @miriamkp)

How to choose a research method (Eva Nedbalova, NCRM, 2017)

Which stats test (crossposted 15 June 2017)

Discovering Statistics (crossposted 7 February 2014)

Decoded: The “how” behind the numbers, facts and trends shaping your world (Pew Research Center)

Spurious Correlations (Tyler Vigen)

Seeing Theory (crossposted 1 March 2017)

Data Viz Project (Ferdio, 2017)

One Dataset, Visualized 25 Ways (crossposted 6 February 2017)

Fundamentals of Data Visualization (Claus O. Wilke, free e-copy of a forthcoming O’Reilly Media book)

Introduction to Social Network Methods (Robert A. Hanneman & Mark Riddle, 2005)

The Philosopher’s Web (via Open Culture, 20 October 2017; see also 14 July 2016 and 25 July 2013)

How to get that pdf (via @elotroalex [Super useful list of #openaccess strategies to help you find that PDF, including sci-hub (with the legal caveat, of course)], 2 March 2018)

Dissertation Calculator (University of Minnesota Libraries)

How to write an article in no time (Anthony C. Ocampo)

***

And to the makers of these — I heart you.

Like a magpie

Kind of a (growing) YouTube playlist with my methodology students in mind. To borrow Ed Yong’s words, I scour the internet so that you don’t have to. 😉

Common errors made when conducting a literature review (Michael Quinn Patton, 2015)

Methods 101: Random sampling (Pew Research Center, 12 May 2017)

Increasing validity in qualitative research (Denise Clark Pope, 2017)

[Linked]

Why you can never argue with conspiracy theorists (Wired, 17 June 2017)

Relativity & the equivalence of reference frames (Hillary Diane Andales, 1 October 2017)

Surrounded by nebulae

Last week I attended a pan-London meeting of researcher developers. My opposite numbers, so to say. It is actually one of my favourite meetings. There I heard a colleague saying that researcher development is not a profession for life. “We have come from different places and we are on our ways to different places”, she concluded. This remark was meant to be a positive one, and I did understand the point she was making. Nevertheless, my heart literally ached a little when I heard that, and I have been pondering since about where that pain came from.

I took on my role at my current institution five years ago. Besides how the role has evolved, the way I see it is that I wear two hats. When I wear the researcher developer hat, I help students grow into researchers themselves. When I wear the researcher hat, I contribute to the scholarship of researcher development while also carrying on with my usual research in digital sociology. This three-way split of identity has been posing challenges, and I have been asked by different colleagues if I am going to ditch the researcher developer hat anytime soon.

Perhaps that is a strategic thing to do, as a recent anonymous article in the Guardian seems to suggest, and last week’s meeting got me wondering whether I am being unwise. Then I recalled this following quote that a PhD student shared with me last year. The words were from her son.

The process of forming a new idea — be it a dissertation, a book, a work of art — is similar to the process by which a star is formed. In the beginning, it is just a cloud of particles — a nebula — floating in space. Slowly, over time, these particles are drawn by gravity toward the centre, coalescing into a more solid form — the first semblance of the star, of something new. As these particles continue to amass, the energy of the centre of the nebula builds and builds until finally, after crossing a critical point — after absorbing a critical amount of information — the cloud ignites and the new star — the new idea — is born.

I am eyewitnessing the births of stars everyday. In a courtside seat, no less. I guess that’s the privilege I can’t quite give up.

Researchers’ complicated relationship with data

RT @rasmus_kleis No, your findings did not “emerge” from your data. Frogs emerge from ponds. Findings are arrived at through analysis of data. In the first case, the frog does the work, in the second, you do the work. (8 November 2017)

Immediately hearted it, but then there is also this one below, reminding me that we cannot try too hard either.

“If you torture the data long enough, it will confess to anything you’d like.” (Ronald Coase, n.d.)

I guess we are all flirting somewhere in between.