How to Do Your Research Project: A Guide for Students in Medicine and the Health Sciences by
Getting involved in medical and biomedical research through necessity or personal choice can be a testing experience. Each step of the process brings its own challenges, from liaising with supervisors, to the lack of opportunities to promote completed research. This brand new How to provides a complete guide to the process: from the planning stages, to execution, write-up, preparation for the viva examination, and how to maximise the impact of your research. It ensures you get the most out of the experience, both in terms of personal development and academic achievement, and even provides guidance on what to do when things don't quite go to plan. An ideal companion, written by an experienced postgraduate tutor, this concise and user-friendly manual is full of practical exercises, key points, and hints and tips, which will give you the edge when dealing with the more elusive realities of medical and healthcare research.
How to Do a Research Project: a guide for undergraduate students by
Written specifically to address the needs and concerns of the undergraduate, this tightly focused volume guides students through the process of conducting and completing a research project. Friendly and accessible, this fully-updated second edition includes a number of accompanying student support materials to aid students further. Closely integrated sets of end-of-chapter tasks covering all aspects of research projects from design to completion, as well as suggested further reading, enhance each chapter. A wide range of additional helpful materials relevant to particular subject areas is also available on the accompanying website at www.wiley.com/college/robson. This textbook is an invaluable resource for students in a wide range of disciplines and fields of study, particularly those planning to use social research methods or to carry out a library-based study, for their undergraduate research project.
This diagram illustrates general steps of a research process.
To build a research question, you can start from a big topic of your interest.
Once you have the topic, you need some basic background knowledge to know the field that you are going to focus.
Where to find background knowledge:
After you have some background information, you will work on your research question and finally ask a manageable one.
Check the video below on choosing a manageable question.
After you have a preliminary question, you will need to do a literature search.
1. What databases to search (general rule):
For interdisciplinary questions: OneSearch, Scopus, Google Scholar
New EBSCO Discovery Service (EDS)- Quick Start Guide
Concept Map - Quick Start Guide "The primary purpose of Concept Map is to allow users to interactively and visually explore related concepts to construct effective EDS search queries..."
For biomedical questions: PubMed, CINAHL (For Nursing and Allied Health profession)
Please visit ABL A-Z Databases for all subjects.
2. General literature search strategy:
Step 5. Apply filters.
Note: remember to save your search strategy in a file.
Example research question: How does omega-3 fatty acid intake affect cognitive performance in adolescents?
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Key concepts |
Search terms |
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omega-3 fatty acids |
(omega-3 fatty acids) OR (alpha-linolenic acid) OR ALA OR (eicosapentaenoic acid) OR EPA OR (docosahexaenoic acid) OR DHA OR (polyunsaturated fatty acids) OR PUFAs OR (fish oil) |
|
cognitive performance
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(cognitive performance) OR (academic performance) OR (school performance) OR cognition OR Learning |
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adolescents
|
Use the age filter. |
After you gained thorough background information, the next step is to formulate a clear, testable statement that predicts a relationship between variables. It can be structured as an "if-then" statement, defining the independent and dependent variables involved. It is a possible answer or explanation for your research question which then becomes your hypothesis. In other words, it can be your prediction of your experiment results. In some studies, theories can be developed. A theory is a more comprehensive explanation supported by evidence, while a hypothesis is a specific, testable prediction based on that theory. To know more about theory, check Chapter 3 The use of Theory in Research design: Qualitative, quantitative, and mixed methods approaches.
How to craft a research hypothesis
Here are some strategies provided by Elsevier Author Services:
Example: If administered drug X, then patients will experience reduced fatigue from cancer treatment.
Example: When workers spend a significant portion of their waking hours in sedentary work, then they experience a greater frequency of digestive problems.
Example: Drug X and drug Y reduce the risk of cognitive decline through the same chemical pathways
To know more about this topic, like types of hypotheses: null hypothesis and alternative hypothesis, please visit Step-by-Step Guide: How to Craft a Strong Research Hypothesis.
To prove a hypothesis, you will need to design your research, collect and analyze data. There are two types of hypotheses: null hypothesis and the alternative hypothesis.
Research design is the strategy to collect, study, and evaluate data of your research. Based on the type of data collected and analyzed, research design can be broadly classified into qualitative research, quantitative research and mixed methods research.
Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. It is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc. Qualitative research methods include interviews, documents, focus groups, case study research, and ethnography.
Quantitative research collects and analyzes numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations. It is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc. Quantitative research methods include surveys and experiments. Experiments typically yield quantitative data. Other research methods, such as controlled observations and questionnaires, can produce both quantitative and qualitative information.
Mixed methods research involves combining or integrating qualitative and quantitative research and data in a research study.
Note: the table is from Table 1.3 Quantitative, Mixed and Qualitative Methods in Chapter 1 (Page 11) in Research design: Qualitative, quantitative, and mixed methods approaches. (Chapter 1 is available as preview in Google Books. The print book is in QU library collection.)
The above research methods in this research guide only gives you a brief overview of research design. To know more about it, please check the following resources:
Qualitative vs Quantitative Research: What’s the Difference? (2023, December 18). https://www.simplypsychology.org/qualitative-quantitative.html
Carson, H. (n.d.). Subject and Course Guides: Quantitative and Qualitative Research: What is Quantitative Research? Retrieved January 6, 2025, from https://libguides.uta.edu/quantitative_and_qualitative_research/quant
Every research collected data, either from the literature or the subject being studied. Data can be broadly categorized into two types: primary data and secondary data.
Primary data is collected firsthand through methods such as: experiments, questionnaires, observations and interviews in primary studies.
Secondary data is derived from existing sources, such as literature and reports.
Data analysis in the biomedical field follows a similar process to other fields. First, it’s essential to grasp the fundamentals of statistics to understand data distributions, hypothesis testing, and key analysis methods. Next, learn programming languages like Python or R, or software tools for data analysis. Then you will focus on biomedical-specific tools and datasets, such as those related to genomics, clinical data, and epidemiology. Finally, you should practice using real-world biomedical data through projects or case studies, to enhance problem-solving abilities and understanding of the field.
Applied Mathematics for the Analysis of Biomedical Data by
"Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB®" offers a practical approach to analyzing biological data, focusing on the application of mathematical models and scientific computing to understand biological systems. Drawing on the author's experience in academia, industry, and government research, the book provides MATLAB-based programs for epidemiology, machine learning, and biostatistics, using real-world data examples like disease spread and diagnostic methods. It includes a set of MATLAB functions for quantitative biology and biostatistics, with case studies that offer insights into biological systems. Designed for readers with varied backgrounds, it provides clear, accessible explanations and a toolbox to facilitate data analysis. The book is an excellent resource for students in mathematics, biostatistics, and life sciences, as well as professionals in biomedical research and product development.
Statistical Learning for Biomedical Data by
This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests(tm), neural nets, support vector machines, nearest neighbors and boosting.
Coursera courses:
Programming for Everybody (Getting Started with Python)
Note: Coursera offers student plan which includes one free course per year. You may also apply financial aid for some courses.
A research paper or manuscript represents the culmination of your study. Planning for your manuscript should begin on the first day of your research. Here are some tips to help streamline the writing process:
Start using a citation management tool, such as Zotero, to organize references from the beginning. Zotero helps keep all your citations in one place, and it can insert in-text citations and generate a reference list. For guidance on using Zotero, visit the Library Zotero Guide.
While reviewing the literature, identify key points or concepts you may want to include in your research. Write down your ideas and use them to draft an initial outline for your research paper. Continuously revise and refine this outline as your research progresses to ensure it aligns with your findings and objectives.
Be prepared to perform additional literature searches as your research unfolds. New topics or gaps may emerge, requiring you to find relevant studies and incorporate them into your work.
Starting early with a clear plan can make writing your biomedical research paper more efficient and less stressful.
