Gaining research internship skills

As a mature student, I took an unconventional route to university. I left formal schooling at the age of 14 and later completed an access course to attain a GCSE equivalent qualification. This was followed by training to become a Montessori teacher. My hands-on experiences working with children with diverse learning needs gave me a curiosity to delve deeper into the environmental and biological influences that shape our behaviours and cognitive processes.

That directed my path toward studying psychology at university, where I developed a passion for research. I was able to gain some experience by asking my lecturers if I could support their ongoing research projects, and I started by taking on small tasks and responsibilities.

Accumulating these small bits of research experience enriched my CV and allowed me to successfully apply for the UNIQ+ research internship at the University of Oxford. It's a programme designed to provide students from under-represented and disadvantaged backgrounds the experience of a funded six-week placement.

There I worked on the 'Working with the Learning for Families through Technology (LiFT) project alongside the Department of Education and Linguistics as well as commercial partners Kinder and Gameloft. The project aimed to develop and evaluate the educational potential of app-based games for children and families. I was able to further develop a range of research skills such as literature dissemination, review and data analysis. 

I contributed to research efforts wherever I could and chose qualitative research methods in my third-year as a module option. All of this experience benefitted me when I applied for a paid research assistant position at my university.

Here, I will take you through the steps of the project that I contributed to as a Research Assistant (RA) for seven months during the final year of my BSc studies. My hope is that this will be helpful for anyone planning a research career alongside their Psychology BSc.

The project

I was supervised in the RA work by Dr Dom Conroy, a lecturer in Psychology at London Metropolitan University, on a study about stakeholder viewpoints of embedding employability in Higher Education institutions. The project was a qualitative study, using semi-structured interviews to speak to various stakeholders (students, teachers, alumni, employers and further education) on their views on employability as a definition and how it is embedded in higher education. Thematic analysis was used to systematically organise and interpret the data.

I gained experience on several different aspects of the qualitative research process during this position.

Stage 1: Data Transcription

The first stage involved transcribing a set of interviews that had already been conducted. We used an AI transcription tool (Otter.ai) for this task, but it was necessary to further process the transcripts. This involved anonymising, deidentifying, and rectifying any errors made by the AI tool. This stage allowed me to get initially acquainted with the data, the research questions and the interview schedule. Notably, the AI transcription tool had limitations in handling diverse accents, possibly attributed to bias in its training data (Manyika et al., 2022).

Although AI transcription is a great tool to speed up the time-consuming process of transcription, it can only provide an initial draft, which then requires the researcher to engage with the audio recording to capture all elements in the data and ensure correct and accurate representation of the participant's words (Bokhove, 2018). As a result, some transcripts required additional time for corrections.

Stage 2: Interviewing individuals

After conducting two interviews and reviewing the transcripts, I had the chance to seek advice and reflect on my interview technique. There were challenges involved with this stage in the work. For example, I wondered if at times I had shown too much of my own opinion, which could have been leading. Openly discussing these concerns with an experienced qualitative researcher during project supervision helped address these issues, allowing me to make improvements in subsequent work.

Transcribing my own interviews during the interview stage was a valuable learning experience. Over time, I made several reflections and adjustments. For example, I learned to embrace moments of silence, as participants often require this pause to gather their thoughts before adding to their answers. Being someone who is a bit of a people pleaser and instinctively wants to make the situation comfortable, this felt unnatural to begin with, but easier with practice. It led to richer and more detailed responses.

Employability and career planning is something that almost anyone can relate to, and many participants talked about issues I passionately agreed with. It was difficult to strike a balance between showing engagement that allows participants to express their views comfortably and ensuring the interview felt conversational, whilst remaining subjective and not showing too much of my position that would influence the data.

Stage 3: Additional recruitment for project participation

Recruitment ran in tandem with the interviewing stage. Engaging students and alumni posed no significant barriers, as I used my university networks and engaged in direct dialogues with students to communicate the essence of the study. However, navigating the recruitment of employers and higher education staff presented challenges. My approach involved outreach to more than a hundred employers and further education providers, employing a combination of telephone communication and electronic correspondence. Regrettably, this did not yield the desired outcomes: likely due to the impersonal nature of emails and the demanding schedules of educators.

Recognising the need for a more personalised approach, I turned to LinkedIn as a means to directly engage individuals within my connections network. This approach proved to be effective, as it allowed for more tailored interactions that facilitated the recruitment of the required sample.

Stage 4: Coding the textual data

Coding involves the systematic organisation of qualitative data, which in this case was textual data in the form of interview transcripts. Through this process, raw data is broken down into segments, or 'codes', each representing a meaningful unit of content. These codes serve as the building blocks for identifying patterns, themes, and insights within the dataset (Braun & Clarke, 2006).

I found the experience of working on a formal research project very different from the work I had done in class. In my previous work, I found the coding process straightforward.  Operating independently, I wasn't required to adhere to any particular format or way of writing the codes. However, the project required a standard approach to ensure consistency between different coders and the codes needed to be clear and easily understood for subsequent stages of the thematic analysis.

This process helped me understand the importance of clear lines of communication and frequent opportunities for discussion as part of working within a wider research team. Having clear, understandable steps taken to produce one's work, an organised process and mindful consideration for those who will be building upon the work for subsequent research phases is something I will take forward when approaching future research endeavours.

Stage 5: Validating theme development

The final stage I worked on was reanalysis of codes from four stakeholder groups that had already developed themes. This was to ensure there was parity in the way the codes had been interpreted and to add additional validity to the findings.

I particularly enjoyed this phase, as my previous work on different parts of the project gave me a solid understanding of the data's topics. It was interesting to observe that certain themes were echoed across the various stakeholder groups.

Once re-analysis was complete, I met with Dom to compare identified themes and discuss similarities. We had identified very similar thematic structures and talked through the wording of the themes to reach agreement on the final theme title. Keeping a careful audit trail of notes on how I had approached the reanalysis of the data was important and aligned with broader quality concerns (e.g., transparency) often discussed in quality guidelines for qualitative research (Yardley, 2000).

Supervision

The supervision process was key to success in the RA role. Dom and I met regularly to discuss project progress and explore how the data was being approached. Taking the project work in clearly defined, scaffolded stages, was important. Through supervision, there was some valuable feedback on where codes could be adjusted and improved for clarity.

Reflection during the supervision process was also key to developing my research skills. For example, Dom provided suggestions during meetings to calibrate the coding approach. I was encouraged to ask questions around the data like 'Whose lens is this?', 'What environment are they referring to at this point?' and 'Is the code capturing the participant's position?' Given that we were interviewing different stakeholders, it was important to capture this, and I found these to be very useful questions to ask myself when coding the transcripts.

I reflected on my positionality (Holmes, 2020) and how my view of the world, beliefs and assumptions determine my own positioning in the research, which could have influenced the way I conducted this research or how I interpreted the data. As an example, my views on education and emotional responses have been shaped through my own experiences. I kept my positionality in my mind throughout this process, working reflexively to ensure I was capturing the participants' meaning and stance rather than my own.

Reflections and next steps 

Learner cohorts in higher education have never been more diverse, and the profile of employability continues to build as a priority for universities and other stakeholders to address. I have found it meaningful to contribute to a project seeking to further understand how employability can be embedded effectively, but also inclusively and ethically, to support and promote equal employment opportunities for all learners.

It was my pleasure to present this research alongside Dom at the British Education Research Association (BERA) conference at Aston University. I'm grateful for the support that has enabled me to seize these opportunities.

Of course, it's not always easy or feasible to pursue voluntary work while studying. Financial obligations or caregiving responsibilities can make it challenging to allocate time to develop the experience necessary for relevant applications alongside the demands of a degree.

This issue emerged as a notable finding in the research. It underscores the very real and significant barriers individuals face when aiming to acquire experiential footholds in their chosen fields. While recognising the diverse responsibilities and pressures students face that may pose obstacles to developing practical experience, even gaining a few hours here and there can significantly contribute to preparing for your career ahead.

Come October, I will begin postgraduate studies in AI Ethics and Society at the University of Cambridge. Being accepted into a Russell Group University, especially coming from a working-class background, feels quite surreal. Looking back, I believe the accumulation of those seemingly small research experiences during my undergraduate degree played a significant role in helping my application stand out. 

References

Bokhove, C., & Downey, C. (2018). Automated generation of 'good enough' transcripts as a first step to transcription of audio-recorded data. Methodological Innovations, 11(2).

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.

Manyika , J., Silberg , J.,  Presten, B. (2022, November 17). What do we do about the biases in AI?. Harvard Business Review.

Yardley, L. (2000). Dilemmas in qualitative health research. Psychology and health, 15(2), 215-228.