Month: August 2019

Disseminating my research

Publication in a peer-reviewed journal is no longer sufficient – research findings need to be disseminated more broadly to ensure (and demonstrate) that they have impact. This means that once I’ve submitted an article for publication I immediately start working on the dissemination plan (if I haven’t already done it as a form of ‘productive procrastination‘!)

There is no one-size-fits-all approach. However, I do have a standard list of dissemination options and a general process that I use. Here it is, in case it is useful for you:

Step 1: Write different versions of your article (during article writing/immediately after submission)

  • Blog post – I usually start by writing a blog post, and this is an excellent article about how to turn your journal article into a blog post, but I’ve also found this one useful.
  • Press release – The University press office has been really helpful in structuring the story and using appropriate language for my press releases in the past (although they sometimes need help making sure the essential message isn’t lost).
  • Talking points – talking points are a great way to prepare for a media interview. In addition, the process of identifying and refining my talking points helps to identify and refine the message, audience and purpose for my dissemination strategy. I usually come up with about 5 talking points, for example: a short sentence and a short paragraph about the main result(s), a short sentence and a short paragraph about the implications, and a short sentence about what might come next.

Step 2: Circulate your pitch (before acceptance)

You may need to modify your pitch for each of the sources below, but you can base all of them on your press release. You need to circulate your pitch to these sources before your article is accepted, because often things move quite quickly after acceptance and you want to have time to work with these people to craft the best piece, and to coordinate the release dates with them.

  • Send a pitch to The Conversation (to do this you need to log in, and use the link on the left hand side of the dashboard)
  • Send a pitch to podcasts that might be interested. Podcasts usually have a longer lead time than the general media, so better to contact them early. There are some health-specific ones (e.g. 2SER Think:Health, the Research Roundup podcast by PC4) or more general ones, such as the University of Sydney podcast ‘Open for Discussion‘.
  • Send a pitch to any other magazine, website, etc that might be relevant. For example, in the past I’ve published summaries in Cancer Professional and have flagged oncologynews.com.au and Croakey as a possible media to approach in the future.

Step 3: Prepare for release (once accepted)

Once you know your article is accepted you should get a timeline for when it will be released. At this point you should let anyone who you’ve worked with on an article (e.g. the Conversation, etc) know the date and coordinate the release. You can also:

  • Contact relevant journalists with your press release. The press office can do this for you, and/or you can use informal approaches such as twitter (list of tweeting journalists below)
  • Contact relevant professional associations about circulating a short article about your research in their newsletter etc. I usually approach groups like the HSRAANZ, AHES, ESA.
  • Finalise your talking points for any media interviews. This includes the talking points drafted earlier, as well as notes on the different ways journalists or readers could misunderstand my research, and any sticky questions I’m nervous about. Then I draft responses to these (which I usually never need, but it makes me feel less nervous knowing I’m prepared).

Step 4: Disseminate (once published)

At last! Today is the day to…

  • Publish your blogpost on your blog
  • Publish your blogpost on LinkedIn
  • Write a post with a link to your blogpost (on your blog or LinkedIn) to Facebook
  • Tweet about your research – over the day or two after publication I usually tweet a link to the original article (with a sentence summarising the main finding), tweet a link to my blog post, tweet a link to any companion pieces (e.g. an article in The Conversation), and retweet any press coverage I get. I haven’t tried this yet, but I was recently told to tag relevant journalists in some of these tweets, and so I’ve compiled the following list of potential options:

Step 5: Tracking your dissemination

As we increasingly need to report our impact, it will become more important to be able to track how and to whom our research was disseminated. Tools like Google Alerts and Altmetrics can be very useful, but I’m also going to try and take screenshots/links/copies of any press coverage etc that I get and save them in the project folder, so that I can easily find them later.

Practical resources for analysing your first DCE

 

I’m relatively new to discrete choice experiments and have really enjoyed learning about the different analysis approaches and techniques used. It is such a rapidly evolving field and there is always something new to learn. While there is a lot happening to push the boundaries, I’ve recently been helping a couple of people with the analysis of their first DCE. While a lot of your analysis approach should be worked out before you begin the DCE,  when you get to the point of actually doing the analysis for the first time there is a whole lot of stuff around which commands to use that you might still need help with. I realised there are some references I just keep recommending and coming back to, so I’ve shared them here maybe you’ll find them helpful too. [Note: this post is updatted as I come across new resources].

General guidance

It often helps to know at the start what you are aiming to achieve at the end. I think this is a nice example of describing the methods and assumptions of a DCE around parental preferences for vaccination programs really clearly and succinctly. The other general information I refer people to is the ISPOR Analysis of DCE guidelines, which include the ESTIMATE checklist of things to consider when justifying your choice of approach.

Analysis approach

When I did the DCE course run through HERU in Aberdeen it was suggested that the typical approach to considering analysis of DCEs was to be to start with a simple model and then use more complex models to address specific issues that arise with your data or relate to your research question. This commonly means starting with a conditional logit model, and then considering options such as mixed logit and latent class analysis. The ISPOR Analysis of DCE guidelines have clear descriptions of the theory and assumptions of these approaches, and I found this paper interesting in comparing mixed logit and latent class approaches.

Analysis code

I am originally a SAS user, and so when I first started analysing DCE data I assumed I would do so in SAS. However, after much investigation I’ve realised this is easier said that done and have now moved to using STATA for the DCE analysis, although I’m still much more comfortable doing the data management and preparation in SAS. Using two different packages is time consuming, clunky and the opposite of “reproducible research”, so my next step is to convert managing my DCE data AND analysis in R. I haven’t got very far, so if anyone knows any good packages then please pass them on! I promise to update this page if I find something useful.

  • SAS

It is straight forward to run a conditional logit in SAS using PROC MDC (user guide). Some resources I found helpful to implement PROC MDC is this example code for conditional logit with PROC MDC and this SAS user group paper “Discrete choice modelling with PROC MDC”. The error message I’ve had most often in doing this analysis is “CHOICE=variable contains redundant alternatives” which relates to the data looking like people have chosen more than one option in a choice set. If you get this, check the cleaning and the sorting of your data!

You can do effectively the same analysis using PROC PHREG, as described by this technote, plus there is a suite of marketing research guides that describe various ways to analysis discrete choice data.

Moving on from conditional logit to mixed logit or latent class analysis is more difficult in SAS. There is a guide in this video to running conditional logit models and mixed logit models (using PROC MDC, starts at 5:30 minutes), although I could never get their mixed logit method to work (entirely possible due to user error!). I did also contact the SAS helpdesk and they said it would be difficult, but recommended using PROC BCHOICE (Bayesian Choice) for mixed logit analysis with DCE data that has multiple choice sets per participant. There is some documentation here and a worked example here.  Again, I never really got this to work but it could be my mistake.

  • STATA

Having faffed around in SAS for long enough, I caved in and transitioned to using STATA like everyone else in my research group! I found this a really nice introductory, step by step guide to analysis in STATA, including data set up and Conditional Logit and Mixed logit options. There is also this article which is a guide to analysing DCE data and model selection, and includes STATA code (as well Nlogit and Biogene) in the supplementary material. Finally, this working paper is useful for describing the theory and code for doing more advanced models, like Mixed Logit and Latent Class analysis in STATA, although the code isn’t annotated which I found frustrating as a new STATA user. I haven’t used it yet, but there was a STATA newsletter article about using the margins option to interpret MIXL choice model results, which could be useful.

For latent class analysis is STATA I found this article in the STATA journal a useful description of the command, and this was a nice example of a paper that used mixed logit and latent class models and wrote them up clearly. Finally, these three articles (one, two, three) seemed like good examples of calculating and displaying relative importance graphs.

  • R

I’m keen to analyse my next DCE in R, so have started looking at how I might do this. I have found the following resources, but if anyone has any experience with DCEs in R then please get in touch!

  • Two papers by Aizaki and Aizaki & Nishimura on designing DCEs in R, and including analysis using conditional logit models
  • Example R code and case study of mixed logit model with multiple choices per respondent, including analysis and helpful tips, written by Kenneth Train and Yves Croissant
  • An mlogit package for analysing DCE data in R, as described in Kenneth Train (2009)
  • Thanks to Nikita Khanna for pointing me to this paper & code for doing sample size calculations for a DCE in R.
  • There is also the Apollo package in R, developed by the group at the Choice Modelling Centre at the University of Leeds, with a website & manual available.