The middle of the academic year is always the time where students find themselves in a frenzy over just one thing: DATA. How many participants? Where and how to find them? And then what? It’s overwhelming. Now is the time to take stock of the overall picture– where you are at and if you’re progressing on schedule. Take double stock of your data– this is the backbone of your thesis.
Data handling has many moving parts, and many students do not make the transition from student to researcher with ease. It’s a giant leap and requires meticulous record-keeping and strategy. If you’re lucky, your research will yield plenty of studies to use as your scaffold, but you will have to conduct your own research, compare it to previous findings, and come to some (hopefully) new and fresh conclusions.
If you’re in the throws of your thesis year, you’ll know I’m seriously oversimplifying here.
For Ph.D. candidates, those attempting to break new ground in an area of study, previous research papers, and scholastic work may be impossible to source. This means they have to rely heavily on conducting their own research and analysing those findings in a cohesive manner in order to answer their research question.
Where do I start?
1. Understand the difference between quantitative and qualitative research.
This you would have done at the Research Proposal phase, so please refer to this as a reminder. You can choose which of these is best suited for your work, or you could use a mixed approach.
Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions. This type of research can be used to establish generalizable facts about a topic. Use quantitative research if you want to confirm or test something (a theory or hypothesis).
Common quantitative methods:
- observations recorded as numbers
- surveys with closed-ended questions
Qualitative research is expressed in words. It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood. Use qualitative research if you want to understand something (concepts, thoughts, experiences)
Common qualitative methods
- interviews with open-ended questions
- observations described in words
- literature reviews that explore concepts and theories
2. My participants
The number of participants required will depend entirely on whether your design is quantitative or qualitative. Once you’ve established the number, the question is how do you find them? Again, this will depend on your target demographic, the population size, and availability.
It is important to reach out to your supervisor, peers, and anyone else who can help you think through this specific task. Some ideas to consider for finding participants:
- Connecting with a professional body or association that meets your target demographic.
- Using social media to request participation.
- Reaching out to your own business and personal network.
- Asking the faculty at your education institution.
N.B. Do not forget that ethical approval is required before your start collecting your data.
3. Collecting my data
Once again, this will depend entirely on your design and your focus on the process. You have your participants lined up, it is time to think through each step:
- Sending out the letter of consent (how will this be done and what is the time limit).
- Tweak your data collection method (interview, survey, etc.) to ensure the questions get to the heart of your research.
- If you’re recording, use the right technology. Do not end up with interviews that were not recorded, this will set you back.
- Safeguard wherever this data is landing. Back up. Do not lose it.
- Follow your own ethical principles.
- Check-in with your supervisor.
4. Analyse my findings
Analysing quantitative data
Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.
Applications such as Excel, SPSS, or R can be used to calculate things like:
- Average scores
- The number of times a particular answer was given
- The correlation or causation between two or more variables
- The reliability and validity of the results
Analysing qualitative data
Qualitative data is more difficult to analyse than quantitative data. It consists of transcribed audio recordings, text, images, or videos instead of numbers.
Some common approaches to analyzing qualitative data include:
- Qualitative content analysis: tracking the occurrence, position, and meaning of words or phrases.
- Thematic analysis: closely examining the data to identify the main themes and patterns.
- Discourse analysis: studying how communication works in social contexts.
Whether you are analysing quantitative or qualitative data please research the most relevant, appropriate, and accessible digital platform to assist you. Reach out to your supervisor and peers to compare notes and understand what is available. Do this as a matter of urgency because whatever you choose, you will probably need time to learn it and money to buy it.
Plus, if you are delegating or outsourcing parts of your analysis e.g. statistician or transcriber, ensure you contract with them as soon as possible. Find out where to find these specialists, give them your timeline, and confirm their availability.
5. Resources required
What else might you need?
- People: statistician, transcriber, editor, coach, interviewer, other?
- Services: graphic design, photocopies, survey software, analysis software, word processing?
- Equipment: voice recorder, round light for zoom interviews, backup device?
- Travel: are you required to go anywhere?
- Accommodation: stayover anywhere?
- Finances: what will any or all of this cost me?
6. Beware of pitfalls and dirty data
Your research and data collection can be tainted if your tools are defective. Here are a few pitfalls to watch out for and remedies if you find yourself in a position where your data has been compromised.
- Avoid closed questions or open-ended ones that offer no guidance.
- No vague questions.
- No leading questions.
- Make sure the order of the questions is logical.
TIP: Building triangulation into interviews and questionnaires will produce a pure result. This means using more than one source of data and comparing the results. This could be as simple as rephrasing a question to see if it produces the same answer.
- Prepare and adhere to protocols that ensure that the researcher doesn’t miss critical information or only tune into aspects that are interesting to him/her.
- Record interviews and transcribe them as to not taint the research.
- Record weaknesses in the data e.g. it’s incomplete.
- Rephrase questions that are met with memory or recall bias.
- Violating rights to privacy.
- Performing concealed observation.
- Allowing personal information to become public.
- Failing to observe/respect cultural values/differences.
- Failing to obtain consent.
- Failing to protect personal cases of sensitive information.
This is an enormous amount to process, I understand that. But, in the same breath, it’s illuminating, exciting, and this is where your research comes together.
The next step is to write this all down by committing your findings to paper. You have enough to do, for now, so let’s leave that till next time.