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!

 

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.

Digital Sociology PhD/ECR Workshop @ Goldsmiths University of London February 19th 2014

Are you a PhD student or Early Career Researcher doing work in digital sociology? The BSA Digital Sociology Group has organised a PhD/ECR Workshop where a limited number of participants can get feedback on their work from peers and established academics in a supportive environment.

The event will take place between 11am to 4pm on February 19th at Goldsmiths College in South London. Confirmed academic respondents are Emma Uprichard (Warwick) and Noortje Marres (Goldsmiths) with one or two more TBC soon.

If you would like to register then please e-mail mark@markcarrigan.net with a short bio and 200 to 300 word abstract. The exact format of the day hasn’t been finalised yet but the intention will be to allow substantial time for discussion of each presentation so places will be extremely limited.

Quantitative research and social media: webinar 8th April

After the success of our webinar on qualitative methods (http://nsmnss.blogspot.co.uk/2013/02/update-from-our-webinar.html) we’ll be hosting a webinar on quantitative research and new social media on Monday the 8th of April. The webinar will follow the format of the previous webinar, a panel if researchers will outline three key issues for quantitative research and then open the discussion up to the floor.

In advance of the webinar we’d like to invite network members to let us know if there are any burning issues that they would like to see addressed during the webinar. We’d also like to invite network members who’d like to present case studies or sessions during the webinar to get in touch with us.

If there any particular issues you’d like to see us address, either leave us a comment below, tweet us or email jerome.finnegan@natcen.ac.uk. If you’re searching for inspiration, why not take a look at some of the videos from our quant knowledge exchange seminar http://nsmnss.blogspot.co.uk/2013/01/videos-from-our-last-knowledge-exchange.html.

Videos from our last Knowledge Exchange Seminar

During our last Knowledge Exchange Seminar we looked at three key issues for quantitative researchers using new social media:

Big Data

Populations and sampling

Data visualisation

We asked some of our participants to give a short presentation to open up the discussions on these issues. We filmed each of our presenters and you can find each of their presentations in the links below:  

Panos Panagiotopoulos

Carl Miller

Luke Sloan

Patty Kostkova

Scott Hale

Grant Blank

Ralph Schroeder

KES 2 - Data Visualisation

The third session at the Knowledge Exchange Seminar on quantitative methods on the 26^th of September was from Scott Hale of the OII. The topic was Data Visualisation and he gave a quick run through of some of some of the pitfalls and problems encountered using visualisation software without really thinking about the story you want to tell with your data. He also showed examples of really helpful visualisations that made it possible for large, complex data to be viewed and understood. One key theme here was that, due to the challenges of representing the temporal dimension of social media data, interactivity was often required. Again, this raised the real issue of skills and expertise needed in the world of social media research and the requirement for working in multidisciplinary teams.

The question was raised about whether the network could provide details about data visualisation platforms and blogs and as there are already some great resources out there, I agree this is worth trying to pull together. I’ve begun compiling a list below and urge members to share theirs too!

Nathan Yau’s blog, Flowing Data http://flowingdata.com/ (Nathan is also author of the book Visualize This, a practical guide to visualisation http://book.flowingdata.com/ )

Andy Kirk’s blog, Visualising Data http://www.visualisingdata.com/  (Every month, Andy pulls together a list of the best visualisations on the web)

Moritz Stefaner’s blog, Well-formed Data http://well-formed-data.net/

David McCandless’s blog, Information is Beautiful http://www.informationisbeautiful.net/

The Award Winning Data Journalism Handbook, which has excellent chapters on data visualisation http://datajournalismhandbook.org/1.0/en/  

Hans Rosling’s TED Talks http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html

KES 2 - Populations and sampling

The second session at the Knowledge Exchange Seminar on quantitative methods on the 26^th of September was from Grant Blank of the OII. The topic was Populations and Sampling and he asked the questions; What is the “population” on social media platforms? How do platforms differ in population characteristics? How can we select cases or sample on social media?

One of the key issues in terms of sampling online is that it’s difficult to develop a sampling frame; Grant pointed out that a biased sampling frame was unavoidable in much online research. However, despite the potential problems, the advantages of online data collection often outweigh the challenges, not least because it’s cheap and fast. 

Since Twitter data are so easy to collect, much of the discussion following the session was around the challenges in sampling Tweets. How can we get a random representative sample of tweets, especially if we’re interested in looking at more than just a snapshot of time? It seems to me that a potential aim for the network might be to put together some guidelines around sampling from Twitter for new researchers who are looking for guidance. Again, the question was raised about what kinds of questions Twitter data can really help us to answer, if we know that Twitter users are not representative of the whole population and that even getting a random, representative sample of tweets is problematic. Some case studies and examples of research questions where Twitter data has been used to good effect could also be helpful to network members.

                                                                                                                                                                                 

Little time was spent discussing sampling from other social media platforms, but an interesting reference was provided for Gjoka et al (2010) which promotes a Random Walk technique to obtain an unbiased sample of social network sites, see:

http://mkurant.com/publications/papers/Walking_in_Facebook_Infocom_10.pdf

KES 2 - Big Data

On the 26^th of September, we met at the OII for the second Knowledge Exchange Seminar for the Blurring the Boundaries network. The topic this time was quantitative methods.

Session 1 kicked off with a presentation from Ralph Schroeder and Eric Meyer from the OII on Big Data and raised the question, is the availability of Big Data changing the kinds of questions social researchers are asking? Is the Big Data tail wagging the research dog?

From the discussions after the presentation and throughout the day, a common theme emerged. And that was that there was still a great deal of uncertainty about what kinds of questions Big Data is useful for. If we want to ensure that we’re doing high quality social science research and not letting the Big Data tail wag the dog, then we need to think carefully about questions first and appropriate methods and data sources (big or small), second. It seems clear that the potential for companies to learn from their data and to predict consumer behaviour and increase sales, for example, is great. However prediction is not the only concern in the social sciences, and the questions we might want to keep in mind are, what do we potentially lose with a shift in attention to Big Data? How is Big Data changing the questions people are asking and the ways in which we do research? There seemed to me a consensus that it’s still relatively early days in terms of Big Data’s role in social science research and at the moment there are some examples of researchers grabbing onto the ‘low hanging fruit’ and that it’s up to social scientists to, over time, show how Big Data can be used in a way that aligns with the goals of social research.

A second common theme of the day was the idea that data fusion is a key issue for the social sciences. It may be that the potential for Big Data to be useful comes not from having lots of the same type of data, but in finding ways to integrate different types of data. What Jim Hendler calls Broad Data, in that it’s about the overlaying of many different types of data sets; structured and unstructured, big and small, public and private, open and closed, person and non-personal, anonymous and identified, aggregate and individual. It’s about finding the structure in all this data and a way to link it all together so that it becomes meaningful. He said the goal is integrating data assets. http://www.slideshare.net/jahendler/broad-data How do social scientists learn these skills?

In thinking about skills, another common theme emerged around the training of social scientists in the UK in quantitative skills. The ESRC is pushing for better quant methods training at undergraduate level for social scientists, but there is certainly a question as to whether our researchers are equipped with the statistics skills to understand what kinds of questions can be answered with Big Data. There is also the question of whether what we now need are social science researchers who are also computer scientists, rather than traditional statistics skills. Certainly a common theme that emerged from the day was that we absolutely need more multi-disciplinary teams if we’re working with this type of data, involving social scientists and computer scientists. There are issues then around research funding for collaborative research and questions about whether the REF does enough to encourage truly multi-disciplinary working when pressure to publish in discipline specific journals is substantial in many fields.

Using Quant Methods for Social Media Research - knowledge exchange event 26th Sept, Oxford.

We're delighted to announce our next round of free NSMNSS network activities.

We will be holding our second Knowledge Exchange Seminar, hosted at the Oxford Internet Institute on September 26th.

This half-day session (12.30-4.30pm) will focus exclusively on using quantitative methods in social media research, please see the programme below for more details. We have very limited spaces for this event so places will be allocated on a first-come first-served basis and preference will be given to participants willing to contribute a case study or example from their own experience to one of the sessions.

If you would like to attend the seminar please email: events@oii.ox.ac.uk  confirming your contact details (email and phone number). We would hope attendees are able to share personal experiences using quantitative methods in social media. Although the Seminar is free, space is limited; if you do not attend after reserving a place we will make an administrative charge to cover the costs of catering and administration. Please only reserve a space if you intend to attend.

Our Knowledge Exchange Seminars provide an opportunity for researchers, practitioners and policy-makers to share ideas. Each of the four sessions will be interactive, with lots of opportunity for open discussion. You can volunteer to:

Lead a themed session – by preparing a 5 minute introduction on one of the seminar themes, which poses key questions for the group to discuss. You will co-facilitate the following discussion with one of the NSMNSS team and write a summary blog post after the event.

Tell us about your experiences by providing a descriptive case study– a short 2-3 minute description of your experience using quantitative methods in social media research. Please identify which of the suggested themes in the programme you think your example would fit under and we will ask you to share this during the relevant session.

If you would like to contribute to the event on 26th September please reply to

events@oii.ox.ac.uk by 19th September giving details of what you would like to contribute.

A video of the introductions to each theme will be available shortly afterwards, if you are unable to join us please  share your thoughts and insights with tweets and comments on our  Methodspace forum. You can join in network activities there at any time: http://www.natcen.ac.uk/nsmnss/

 

We will also be hosting a virtual Blackboard session for international participants later in September. More details to follow. 

Knowledge Exchange Seminar 2 – Quantitative Methods in Social Media Research, 26th September 2012, Oxford Internet Institute

12.30pm Arrival and lunch – networking

1.00pm -  Session 1 Visualisation:

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?

1.50pm - Session 2 

Populations and Sampling: What is the “population” on social media platforms? How do platforms differ in population characteristics? How can we select cases or sample on social media?

2.40pm Break

3.00pm - Session 3 Big Data:

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?

3.50pm Session 4 Drawing together key messages for quantitative research:

Exploring existing frameworks, identifying gaps and additions.

4.30pm Close and next steps