Using Big Data to Solve Social Science Problems

Curtis Jessop is a Senior Researcher at NatCen Social Research and is the Network Lead for the NSMNSS network

On Wednesday 29^th June I attended a roundtable hosted by our network partners SAGE on using big data to solve social science problems. It was a great day, with contributions from leading researchers and lots of discussion of some of the key issues of working with big data in social science.

Jane Elliott began with an overview of the ESRC’s Big Data Network. She identified the difficulties with data access that earlier phases had faced, but also highlighted key challenges that big data social science currently faces:

1. Methodological

• Can we apply the same qualitative techniques/statistical inferences we have in the past?
• Are social scientists (falling) behind in using machine learning & algorithms? What are the implications of these methods?

2. Relevance of research

• Making sure we use big data to answer pertinent social science questions, and not just focus on methods

3. Ethics at a macro & micro level

• Working ethically with big data - data security, anonymity, informed consent & data ownership
• What are the implications of a ‘big data society’/algorithm-led decision making?

New methods, tools and techniques for big data research

Giuseppe Veltri outlined how data-driven science differs from ‘traditional’ social science research as it generates hypotheses and insights from the data, rather than theory, combining abductive, inductive & deductive approaches. Further, Phillip Brooker identified a tension in big data analysis between wanting to use qualitative research approaches with data of a scale that requires numerical treatment. As a result, social scientists need to work with ‘unfamiliar’ techniques and software.

Tools for Big Data analysis

It was generally agreed that existing software are not fit for addressing academic/social science research questions. Also, tools offered by commercial companies are often ‘black boxes’, when social scientists need to be transparent on the algorithms they use as they are part of the methodology.

Many at the roundtable have therefore developed their own tools (e.g. COSMOS, Textonics, Chorus, & Method52 from CASM) to enable them to conduct analysis in a manner they wanted to. However, it was felt there was still some way to go - many of these tools are ‘in-house’ and ongoing funding/support is needed to develop something more stable, well-supported, and ‘outward facing’.

Interdisciplinary working

One approach to addressing the challenges of big data analysis is working in interdisciplinary teams (in particular linking between social & computer science departments). Luke Sloan and Mark Carrigan identified the key challenge of this at a ‘human level’ is ensuring a common understanding of language, after which it was easy to have an open discussion and there were rarely disagreements. Mark argued that what was key was not necessarily making sure that everyone had the same definitions, but that there was an understanding that different fields may have different perspectives.

Mark Kennedy, based on his experiences at the Data Science Institute, emphasised the importance of ‘getting excited’ about the right research question, not just focusing on the technology, and then building a team based on what skills you need to fill that gap.

However, attendees felt that there were structural barriers to interdisciplinary working in academia – departmental silos, geography, navigating different funding bodies, finding journals to publish in, and demonstrating value for the REF were all recognised as problems, although it was also mentioned that funding increasingly supported this approach.

Training in the social sciences

Quite early in the discussion, the question was raised that if there is such a clear skills gap in the social sciences, why had universities not responded to it?

Although it was accepted that training needed to address big data methods, there were differing opinions on how feasible this might be. Adding new techniques into methods courses was welcomed, but to what extent was this achievable when these are already packed covering ‘traditional’ methods? Further, given the relative rarity of established social scientists with this skill-set, who would provide this teaching?

Although it was felt that new students are open to using Python or R/new statistical techniques, this scarcity of trainers with the skills to teach both programming and its application within social sciences was again identified as a problem. Giving students (and academics) access to data science training materials that are framed by social science problems, and relevant dummy data to work with, was suggested as a way to start addressing this.

Answering social science questions with Big Data

While discussing his own research, Slava Mikhaylov highlighted that a good way to make impact is, rather than starting with a research question, to aim to solve a problem. This was echoed by Carl Miller, who outlined some principles that Demos follow for making impact:

• Look beyond academic funders – if research is funded by a government department, they’re going to have to listen to it!
• Ask the right question – what is interesting to a researcher vs. a policy maker
• Answer quickly – policy interests change, and research won’t make an impact if everyone’s moved on
• Diversify outputs – can they be real-time, interactive, engaging?
• Networking – who are the champions of big data research?

 Carl emphasised that was just the approach that Demos used, and may not be appropriate for all research or audiences. He also mentioned you need to work hard in a new discipline to be responsible and transparent about what your research doesn’t do or say.

Ethics of research using Big Data

Anne Alexander differentiated between the ethics of research using big data and the ethics of doing research in a networked world.

On the latter, Anne felt that there has not been enough reflection on the implications of the ‘datafication’ of human interaction, and that we need to de-mystify these processes and consider what the use of machine learning/algorithms means for society (e.g. their potential for discrimination).

Anne emphasised the need to take into consideration the public’s views on this when considering Big Data research, a point re-enforced by Steve Ginnis, whose work at Ipsos Mori on developing ethical guidelines for social media research drew on public ethics, existing industry guidelines and legal frameworks.

Steve’s research identified that the public both have low awareness of, and are not keen on, their social media data being used for research. This was not just due to concerns about privacy/anonymization – people were uncomfortable with being profiled and its possible implications.

That said, participants were willing to weigh up the risks and benefits, and context (who is doing the research and why) was important. Nonetheless, the ‘fundamentals’ (consent, what information, anonymization, etc.) played a much larger role in whether they felt research using social data was appropriate.

Both Anne & Steve emphasised that ethics is an ongoing process, not a one-off event at the start of a project – they need to be considered at the collection, analysis and publication stages of the research cycle.

Some concluding thoughts

Carl Miller identified that in the context of pressure for evidence-based policy, digital by default, and the open data initiative, there has never been a better time for social scientists to make impact with big data research.

Wednesday’s session demonstrated how far big data analysis in the social sciences has come over recent years and it is impressive to hear how much work has been put into developing the tools and methods to mould this rich, but novel, form of data into social insights.

However, the session also showed that there are number of areas that still need to be addressed if we are to make the most of big data:

• Access to large data sets continues to be an issue, be they proprietary, public, or administrative. We need to bargain collectively to talk to large, often global, actors and argue for academic access.
• There is a skills gap among social scientists for analysing big data, and support is needed to help develop the required methodological and programming skills.
• The interdisciplinary working required for big data analysis can be challenging, and we need to work to enable effective collaboration.
• Developing an ethical approach to big data analysis is challenging given its novelty, variety, and changing nature. Any framework needs to provide practical guidance to researchers while remaining flexible and responsive to changing contexts.
• Available tools for big data analysis can be expensive, lack transparency, or inappropriate for social science research. A maintained central library of available tools, with appropriate documentation and guidance could be extremely useful.

A student perspective on 'Qualitative Online Interviews' by Dr Janet Salmons

Ivett Ayodele is an undergraduate student at the University of Salford studying BSc (Hons) Psychology and Counselling. She tweets as @ivettayo and @salfordpcy1 and blogs here.  

I have accepted the challenge to review Qualitative Online Interviews by Dr Janet Salmons (2015) because I believe that as part of the next generation of psychologists, it is a great opportunity to familiarise myself  with the emerging methods of online interviews, which will surely become popular in the future. It is also a great chance to extend my knowledge of qualitative methods generally, while writing this post helps to develop my academic writing skills. My task is to give a student perspective on the book since a lecturer’s perspective has already been explored (see here).

Qualitative Online Interviews by Dr Janet Salmons (2015) guides researchers and students through the process of extending their research into various online settings and it gives guidance on ethical issues that can arise during online interviews. As the author puts it, “the purpose of Qualitative Online Interviews is to encourage researchers to extend the reach of their studies by using methods that defy geographic boundaries” (Salmons, 2015, p. xviii). The book is structured around the E-Interview Framework, a conceptual system which helps to understand interrelationships between the key elements of Online Interviews and aids the process of decision making throughout the research design.

As a first year undergraduate student, I have had the opportunity to learn extensively about quantitative research methods; however Qualitative Online Interviews by Dr Janet Salmons (2015) gave me the opportunity to extend my growing knowledge of qualitative methods. This learning journey ‘forced’ me to develop a complex picture of research methods and now I have a better understanding of both quantitative and qualitative methods; while  the benefits of mixed methods became crystal clear to me.

Qualitative Online Interviews (Salmons, 2015) gives a deep insight into specific ethical issues surrounding online interviews. The author took the typical ethical issues of research, such as informed consent or confidentiality, and placed them at the heart of online interviews. For example Dr Janet Salmons draws attention to the possible flaws in data protection in an online setting by pointing out that some companies who own the platform, where the data is stored, might not have adequate protection against unauthorized access.

The cover and the design of the book reminded me of my old school books; however I found that the simple design helped me to focus more on the text, rather than on the pictures and tables. I found this useful, especially as I was learning new concepts. For example, taking a position as an insider (EMIC) or outsider (ETIC) researcher was a new concept which helped me to appreciate the possible design flaws of a qualitative study, as well as the richness of it, compared to a quantitative study.

The detailed content page and the organization of the book helps the reader to find exactly what they are looking for; yet I found that this book works for me best if I read it first from cover to cover.

 I found the Researcher’s Notebook section and Discussions and Assignments at end of each chapter very helpful. The Researcher’s Notebook section encouraged me to think about each concept as a practical issue and therefore made it easier to understand and relate concepts to research methods. For example in Chapter 3 -Choosing Online Data Collection Method and Taking a Position as a Researcher- Salmons (2015) explains the main ideas of the chapter through her previous studies, which made these concepts to come “alive”.

The Discussion and Assignment section facilitate further learning by raising some questions in regards to each concept. For example in Chapter 9 (Preparing for an Online Interview), Salmons (2015) talks about the importance of Epoche –“ to approach each interview with clear and fresh perspective”- subsequently the Discussion and Assignments part encourages students/researchers to talk/think through the Epoche concept and raises the question, what could be done to clear our mind before an online interview?

The accompanying website is not as user friendly as I would like, however once I found my way around it, I felt that it is a great way to extend the learning experience for students. The website contains of a general resources and a student resources part.

The general resources section offers materials such as course outline with suggested assignments, learning activities, worksheets and media pieces. They are great for academics for planning a course or seminar on qualitative online interviews and they are also useful for students who want to build on their knowledge outside the classroom. The student resource part is broken down into the chapters of the book. In each chapter students can find the definitions of new terms on e-Flashcards, which is a great learning tool. Students can choose whether they would like to see the term or the definition of the term and learn new terminology while they are having fun!

Qualitative Online Interviews by Dr Janet Salmons has not only extended my knowledge about qualitative methods and online interviews but it also deepened my knowledge about ethical issues during online and off-line research. I would recommend this book to any undergraduate student and if someone chooses to conduct online research for their dissertation, I believe that this book is a must have!

 

Book of blogs - contributors update

Wow, we've been blown away by the response to our call for contributors, please see our previous post for the background to this crowdsourced project. If you've already read that and are keen to get going then here are your next steps:

• Step 1 - email us at: nsmnss@natcen.ac.uk we will then send you a link to access the contributors spreadsheet.
• Step 2 - complete the Google Doc spreadsheet with your basic information and any ideas or suggestions you have for the book, we can then send you a log in for the platform we'll be using to publish the book.
• Step 3 - get writing!
• Step 3a. - (optional) send it to us if you would like the editorial support group to provide some constructive feedback.
• Step 4 -  upload it - you're done! 

Are we uploading content ourselves then? Ideally yes, it would be really helpful if, once you have written your blog, you could upload it yourself. We are a small team & the network is currently unfunded so we have chosen a process which reduces the administrative burden and encourages everyone to play a part in the publishing process.

We're using PressBook which is akin to a communal WordPress tool this allows people to upload their own content and then enables us to publish the content as an eBook for publication on ecommerce sites like Amazon and devices like Kindle etc. If you're used to using a blogging tool like WordPress or Blogger then you should find the process familiar.

If this is your first ever blog then congratulations, we think you'll find the WSIWYG format relatively simple, you can easily cut and paste text from any word-processing package. If you hit any problems then contact us and we'll provide support.

When you log into the PressBook site you will see the following screens, choose Text from the left-hand menu and then select Add new chapter - 

 

What content can I include in my blog? 

See our previous post for the general guidance on themes and topics. 

• As an ebook you can include images, videos and web links. Please don’t link to any material that we don’t have rights for.  Videos will not routinely work in all ebook formats so consider this when selecting your content.  If you are putting video and pictures into the book please make sure you aren’t breaking the law when you are doing so! Please reference quotes and use appropriate web links or citations to credit the original creators.
• You can be as provocative as you like, but anything offensive won’t make the cut.  
• Please don’t attempt to sell or push a product or service – that is the one thing that won’t be acceptable. 
• The book needs to be accessible so remember to write for a wide audience with varying levels of technical expertise and practical experience, if you are writing about complex methodologies or philosophy include links for less experienced readers to explore other resources on the subject
• Please include a short bio (no longer than a paragraph) at the end of your chapter. You can include links to your blog, or website as you wish.

What about editing and quality control?

Anyone can contribute – there's no formal quality control – this is not a peer reviewed journal, if someone has taken the trouble to write it then we will take the time to publish it.  We want this to be packed full of different voices, some will be experienced, others not, that's fine.

We hope (and expect) that we won’t have a cut to make as we do want to publish everything that is submitted. We're assuming the average blog will be about 1000 words long – a bit longer or shorter is fine, but we won’t publish a paragraph or a long treatise – unless they are really good ;-). If we find do have to make a cut we'll talk to you all about a fair way of doing that, if we think your blog is too long or too short we will contact you about that individually.

We aren't planning on a protracted editing process but we are offering to review draft blogs and make suggestions, give constructive feedback, particularly for novice bloggers. We're looking for volunteers to form an editorial support group to provide informal feedback, so let us know if you'd be willing to play a part in that when you sign up on the spreadsheet.

What's the timescale, milestones and deadline?

• By June 30th - everyone signed up on the spreadsheet and an editorial support group formed
• By July 31st - we're aiming to have 60% of your content submitted
• August 29th is the final deadline for contributors
• End September is our provisional publication date *nervous laugh*

How can I help?

• By being as self-sufficient as possible, try uploading yourself first before asking us to do it.
• By sticking to the deadlines, if one person misses these everything gets held up.
• By being an informal editorial mentor for others, this will involve reading early drafts and offering constructive feedback
• By promoting the book of blogs to your network, we need to get the idea out to as many people as possible to both encourage contributions and to ensure wide readership come publication
• Let us know if you have ideas about where and how we should publicise and promote the book
• Please do suggest and pass on details to other potential authors and let us know possible people to write the forward to the book, it would be great to get the endorsement of someone well respected and well known - think big and outside of the box, who should we ask to introduce our work to the rest of the world?

Now is when it starts to get really interesting!

We're looking forward to hearing from you really soon.

Kandy & the #NSMNSS team

Why I love online interview research

Janet Salmons is a network member and a contributing expert to the online team. 

There are nearly as many kinds of research approaches as there are questions to study. While it is the task of the researcher to match approach to question, it is clear that some researchers simply prefer one approach over another, and select types of questions that permit them to carry out the inquiry in the preferred way. I admit it, I am guilty. While I see the value in all sorts of studies, I would not be content to crunch Big Data, even if it meant I could generate impressive maps and diagrams. I want to learn about their experiences first hand. I want to know why. Why did you make that choice and not another, why was it important to you, why do you plan to do things differently in the future? I cannot ask an extant mountain of Big Data to tell me more.

Online interviews allow researchers to pose questions in a variety of ways with participants anywhere, at any time. In synchronous interviews we can capture the immediacy and emotion of the moment, and when we click on the webcam to video conference we can have a dynamic exchange that is close to being there in person. We can traipse around a virtual world together or share a desktop or application, and discuss what we are experiencing. Or we can use a whiteboard to diagram and draw perceptions of the phenomena. With flexible asynchronous emails we can create an extended narrative correspondence or text message on the go, and ask participants to share their observations on site-- perhaps even relaying pictures or maps back to us.  It can be a rich exchange!

And yet, as always in scholarly research there are more issues and requirements to be considered including rigour, ethics, and methodological alignment. There is also the all-important acceptance by dissertation chairs and ethics boards, editors and peer reviewers. To add to the challenge, many of those who would hold the study’s fate in their hands are not familiar or comfortable with online methods.

Resources and upcoming events

To help researchers navigate these matters and create well-designed, coherent studies that merit approval, I have been focusing my attention on the development of design approaches for online interview methods. I created the “E-Interview Research Framework,” a holistic, systems-thinking set of guiding questions and models (Salmons, 2012, 2015). My new book from Sage Publications, Qualitative Online Interviews, is organized using this Framework.

To celebrate the May book release I am organizing an interactive, global online multi-platform extravaganza of free webinars, discussions and tweetchats about using e-interviews in research and teaching online methods. In addition to NSMNSS, SCoPE, e/merge Africa and IT4All are offering opportunities to learn together and exchange ideas. You are invited!

SCoPE, an online community interested in technology, educational research and practice, will host events from May 1 to 16, and additional events will be offered throughout the summer. Visit SCoPE at http://bit.ly/1i7W73j for free registration, webinar log-in information and related resources. Follow @einterview for updates.

Save the dates

Asynchronous Discussion Forum May 2-16:

·         Teaching Digital Qualitative Interview Methods

·         Designing an (Approvable) Study with Digital Qualitative Interview Methods

·         Open Discussion and Q & A

Webinars, May 5 and 12, and June 7:

·         Monday, May 5, 2014 at 17:00:00 Fair and Good? Ethics and Quality in Online Interview Research

·         Monday, May 12, 2014 at 17:00:00  See, Share, Create: Visual E-Interviews

·         Saturday, June 7, 2014 at 15:00 Online Interviews for Active Online Learning

NSMNSS Tweetchat, May 8:

·         Thursday, May 8, 2014 at 14:00:00  5 Tips for Teaching E-Interview Methods. Follow @NSMNSS and include #NSMNSS in all your tweets.

e/merge Africa Events,
July 21-25: Details TBA

Keeping up with technology: What is “scientific lag” and can we proactively reduce it?

In 2011 then Census Director Robert Groves wrote on the Census Director’s Blog about the burgeoning volume of “organic data”—data that, as opposed to “designed data,” have no meaning until they are used (surveys are a primary example of the latter). He noted that finding ways to combine these two types of data to increase the “information-to-data ratio” was a challenge, but also represented the future of surveys. Using terms identified as “big data descriptors” in Groves’ piece, as well as a few other terms I think qualify, I put together the graph below to show the number of AAPOR presentation titles between 2010 and 2013 that contain a big data descriptor.1,2

One take away is the increased interest researchers have shown in big data over the past few years. An equally important lesson is that almost all of the attention big data has received from AAPOR members—at least measured by the number of presentations they’ve done—has been on social networking sites (SNS). I found only one presentation in the past four AAPOR conference programs that contained a big data descriptor for a non-social media topic—a demonstration in 2012 by Ben Waber on the use of wearable sensors for measuring behavior.

To some extent this is explained by scientific lag. Just like there is cultural lag—the time between the emergence of a new technology and when culture catches up—there is a lag time between when consumers adopt technologies and when our research methodologies catch up (i.e., scientific lag = cultural lag + time until research methodologies using those technologies are implemented). And, technologies often don’t remain static, but rather evolve making it a continuous game of catchup (development) for research methodologists. I’ll go into more detail about this in a presentation I’m giving at the AAPOR conference this year, but one quick example from the annals of survey research history is the development of computer-assisted telephone interviewing (CATI). While telephone exchanges had existed for almost a century and programmable computers emerged in the 1940s, it took until 1971 for CATI systems to be developed by market researchers, another five years for academic researchers to begin using it, and the federal government another seven years to implement its use. Certainly, cultural lag played a role. It took years for enough households to have telephones for probability based telephone sampling to make sense. In addition, it took time for the programmable computer to develop into a device usable for this purpose. But, it also took researchers time to figure out such a system was possible and the value it presented.

Now, let’s fast forward a bit. In 1997, one of the first SNSs, sixdegrees.com, was created.  It lasted until 2001. A host of other networking sites, the ones most of us are familiar with, sprang up in the early 2000s—Myspace (2003), Facebook (2004), and Twitter (2006). There are, I suppose, two ways of looking at the cultural and scientific lags and SNSs. On the one hand, it took a few years SNSs to grow to significant numbers. For example, it took Facebook four years (2004 – 2008) to grow to 100 million users. Within four years of that development there were multiple presentations at AAPOR on the subject. That’s certainly much faster than the development of CATI technology/adoption. On the other hand, social researchers took nearly a decade from the birth of widely popular SNSs to begin formally recognizing their research utility.

Now, we may be at the cusp of another such tsunami of consumer technology adoption. Groups disagree on the exact timing (e.g., Forbes says 2014 and MIT Technology Review says 2013), but the evidence points to the start of rapid growth in the use of internet connected sensors and devices for a multitude of purposes. I’ve recently written about how and why I think the devices and the IoT will affect social science data collection.

My question is whether the research community can be more proactive, and therefore decrease the scientific lag between adoption and research implementation. My hope is we will and that it will have a positive effect on survey data collection.

I’ll be presenting more thoughts on this topic at AAPOR and look forward to the discussion we have about big data in the session. Between now and then I’d welcome the thoughts others have on or experiences others have had with using wearable tech, sensors, or the IoT for research.

This was first posted on Survey Post  on 24/02/14

Brian Head is a research methodologist at RTI International with 5 years of experience in the government and not-for-profit research sectors.  Training in sociology and research methods and statistics led him to a career in research where his work has included questionnaire design and evaluation,  managing data collection efforts, and qualitative and quantitative data analysis.

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Using “Small Data” to Improve the Use of “Big Data”

This post was first published on Survey Post on Feb. 3rd, 2014.

Recently, I attended two statistical events in the Washington, DC, area: one was the 23rd Morris Hansen Lecture  on “Envisioning the 2030 U.S. Census”; the other was the SAMSI workshop on “Computational Methods for Censuses and Surveys.” “Big data” was a popular keyword at both events and stirred up discussions on how to utilize it (such as from administrative records and online data sources) for current government statistics, especially when combining big data with  traditional survey data.

Statisticians are exploring new ways in which big data can be used. The US Census has initiated investigations on using administrative records in the 2020 Census. The National Center for Health Statistics (NCHS) has identified some research opportunities combining multiple data sources. University-based researchers  have launched studies on the use of Google trends and other online data in small area estimation.

When big data dominated the mainstream discussion at these events, I started thinking more about “small data.” Can small data help us make better use of big data? Here are some of my thoughts.

1. Applying a conventional sampling-based approach to big data: more and more administrative records are collected electronically. Statisticians are excited about using these records that may contain information from the entire population for analytic purposes. Literature in the past two decades has extensively discussed the advantages of administrative records. Processing administrative records data, however, can be quite time consuming. In addition, it can be cumbersome to run analyses on these large datasets because of the large data volume. Especially, when analysts use conventional statistical software, such as SAS, Stata and R, it becomes increasingly complex to handle, store and analyze these data. The question is: is there a way to reduce the data volume and increase computational speed? Applying conventional sampling-based approach (e.g. optimal sampling, calibration weighting) may make a big data smaller and more manageable while allowing researchers to maintain decent data quality.
2. Combining non-probability sample data with probability sample data: many big data, such as data collected by Google/Twitter/Facebook, are not census (population) data. We may treat them as non-probability sample data.  Elements are chosen arbitrarily in these datasets and there is no way to estimate the probability that each element in the population will be included. Also, it is not guaranteed that each element has a chance of being included, making it impossible either to assess the validity (always measured in terms of “bias”) and reality (always measured in terms of “variance”) of the data. One solution to make the data more representative of the entire population is to combine them with probability sample data (e.g. survey data), which can be relatively smaller. This method can also assist us estimating sample variability and identifying potential bias in big data.
3. Using high-quality small data for measuring and adjusting errors in big data: big data is not only non-representative of the target population, but also carry loads of measurement errors because the construct behind a particular measure in these data can differ from the construct that analysts require. To evaluate errors in the big data and improve precision, small survey data can be collected for validation. Take the National Health Interview Survey (NHIS) as an example. This is a household interview survey with only self-reported data. To improve on analyses of the NHIS self-reported data, an imputation-based strategy for using clinical information from an examination-based health survey (i.e. National Health Nutrition Examination Survey, NHANES) was implemented that predicts clinical values from self-reported values and covariates. Estimates of health measures based on the multiply imputed clinical values are different from those based on the NHIS self-reported data alone and have smaller estimated standard errors than those based solely on the NHANES clinical data. Similarly, we may assess potential errors in big data through a more sophisticated and accurate small survey.

While big data provides us massive and timely information from various sources (e.g. social media, administrative records, small data is simple, easy to collect and process, and can be more accurate and representative.  Can small data help you when dealing with your big data problems?

Dan Liao is a research statistician at RTI International. She currently works on multiple aspects of data processing and  analysis for large, multistage surveys of health care in the United States, including sampling design, calibration weighting, data editing and imputation, statistical disclosure control, and the analysis of survey data. Her survey research interests include multiphase survey designs, combining survey and administrative data, domain estimation, calibration weighting, and regression diagnostics for complex survey data. Dan has a PhD in Survey Methodology from the Joint Program in Survey Methodology at University of Maryland and has published research focusing on regression diagnostics, calibration weighting and predictive modeling.

SAGE Research Methods Cases

Over the last 40 years SAGE has become world famous for publishing the highest-quality and most cutting-edge books on research methods.  Yet research methods – the “how” of doing research – is still commonly considered a dry and abstract subject.  SAGE needs your help to change that!  

Launching in May 2014, SAGE Research Methods Cases is an innovative and exciting new collection of hundreds of real-life research projects distilled into accessible, peer-reviewedcase studies.  Our goal is to bring methodological concepts and problems to life in a fully-comprehensive resource. The collection with be published online on the award winning SAGE Research Methods platform.

Get Involved!

We plan to commission cases for SAGE Research Methods Cases on an ongoing basis, so we are still looking for case authors.   Have you undertaken a research project? Can you discuss your methodological choices and challenges in an accessible and engaging way? We are looking for short original cases between 2000 – 5000 words in length that put difficult and abstract methodological concepts into a real research context. 

Do you experiment with new social media research? Would you like to discuss the implications of new up and coming methodologies? Our ambition is to represent the breadth and depth of social science research and encompass the full range of possible methodological approaches!  We welcome authors from across the methodological and disciplinary divides.

Want to know more?

Contact Bronia Flett: bronia.flett@sagepub.co.uk for more information on how to submit a case of your own to the collection.

Visit http://srmo.sagepub.com/ to sign up for a free trial of SAGE Research Methods Cases.

New Social Media, New Social Science… and New Ethical Issues!

We held a small event on Friday 21st February 2014, our goal was to have a series of focused discussions around the ethical dimensions of social media research and to come up with a series of action points for the network to take forward. The day also included two presentations of related research by network members. You can read a a storify of the day - sfy.co/rPSZ 

We will post more about the outcomes and discussions shortly but we want to kick off by sharing links to the two reports and presentations. A team from NatCen has been researching the views of social media users on how their posted data should be handled by researchers. The findings are illuminating and will help to inform how we consider we should work with social media data in the future. You can read a post by the research team here, and then the findings here.

Now Janet Salmons introduces the research she has undertaken on what researchers need to help support their ethical practice when conducting social research online:

Our NSMNSS network has convened researchers from the UK and around the world in thought-provoking dialogue on topics related to scholarly use the Internet and social media. Recurrent matters related to research ethics demonstrate that there are many questions and concerns about how to adapt conventional guidelines to kinds of online research. In the atmosphere of collaboration and exchange NSMNSS encourages, resources are often suggested.  My curiosity led to two questions: to what extent do the resources suggested by the NSMNSS network address the concerns and questions raised by the NSMNSS network? What are the gaps and how can or should they be addressed? The report, “New Social Media, New Social Science… and New Ethical Issues!” is the result of this exploration. 

Numerous concerns and queries emerged from network discussions, classified here as seven interrelated themes:

• Participants: Issues related to online sampling and recruiting to find, screen, and select appropriate and verifiable research participants.

• Identity: Issues related to the identity, anonymity and/or privacy of the participant andthe researcher.

• Research site: Issues related to the setting for the study or source of data.

• Informed consent: Issues related to the determination of when consent is needed and what type of consent is adequate.

• Data: Issues related to user-generated content and ownership and protection of data.

• Research guidance: Issues related to the academic institutions and committees that prepare the next generation of researchers and must approve researchers’ proposals or decide whether their research is adequate for tenure or promotion.

• Methods and methodologies: Issues related to implications of social media research for the ways we think about research methods and methodologies.

The first five themes relate specifically to the designing, conducting and reporting on research. The final two themes raise larger issues for the field—a field NSMNSS members characterize as multidisciplinary.

Many of the recommended guidelines and materials offered little or no advice about online research ethics. However, a few professional societies and organizations have made the effort to either create a set of guidelines specific to research on the Internet, or have created supplementary materials that focus on how to apply that profession’s ethical standards when conducting studies online—and reporting on the findings. Examples of the latter were chosen for this review including materials from the Association of Internet Researchers(AOIR), British Educational Research Association (BERA), British Psychological Society (BPS), CASRO, European Society for Opinion and Marketing Research (ESOMAR) and Market Research Society (MRS), and Association (MRA).

As you can see, in some cases the profiled guidelines make recommendations aligned with NMSNSS network needs and in other cases they instead identify other risks and concerns.

You can read the full report here or watch the presentation below.

Please use the comment box to add your thoughts, relevant experiences, or to suggest other resources. We are also planning a Tweetchat discussion of the report on Tuesday 11th March at 7pm GMT (8am NZDT/6am AEDT/8pm CET/9pm SAST/3pm EDT/1pm MDT), so please join us ! You can read more about taking part in an NSMNSS twitter chat here.