Research Skills: Home

What is a Research Problem?

A research problem is the issue, gap, or unanswered question that motivates a study. It can come from real-world challenges, gaps in past research, or changing industry trends. A clear problem helps shape the focus of the research.

How to Identify a Good Research Problem:

• Read academic journals and reports to find unanswered questions
• Observe practical problems in work or daily life
• Speak to experts, users, or stakeholders
• Choose something that matches your skills and interests

Examples:

• Computing: "Cloud-based applications often have slow performance in low-connectivity areas."
• Business: "Small retail companies struggle to keep loyal customers despite using digital marketing."

A research question is the central question your study aims to answer. It gives your research purpose and direction. A good research question helps define what you will study, how you will study it, and why it matters.

What Makes a Good Research Question?

• Clear: Easy to understand and not too broad or vague.
• Focused: Targets a specific issue or topic.
• Researchable: Can be answered using real data or evidence, not just opinions.
• Relevant: Related to your field and current challenges or needs.

Types of Research Questions:

• Descriptive: Asks what is happening (e.g., "What features are most used in mobile banking apps?")
• Comparative: Asks about differences between things (e.g., "Which marketing method leads to higher sales?")
• Explanatory (Causal): Asks why or how one thing affects another (e.g., "How does slow internet speed affect cloud app performance?")

Why Research Questions Matter:

• They guide your literature review, methodology, and analysis.
• They help you avoid collecting unnecessary data.
• They make your study easier to explain to others.

Examples:

• Computing: "How does poor internet connectivity affect the user performance of cloud-based applications?"
• Business: "How do digital marketing strategies influence customer loyalty in small retail businesses?"

Tip for Students: Start by identifying a research problem, then turn that problem into a question that your research can realistically answer within your time and resource limits.

Research objectives are the specific steps or goals you want to achieve in order to answer your research question. They help break the question into smaller, manageable parts and guide the structure of your study. Objectives should tell the reader exactly what you will investigate or measure.

Why Objectives Are Important:

• They give your research a clear direction
• They help you stay organised and focused
• They allow others to understand the purpose and scope of your work

SMART Objectives: Good research objectives are:

• Specific: Clearly state what will be studied
• Measurable: Can be assessed using data
• Achievable: Realistic given time and resources
• Relevant: Connected to the research question
• Time-bound: Can be completed within a certain time

Examples:

Computing

Question: "How does poor internet connectivity affect the user performance of cloud-based applications?"

Example of SMART Objectives:

• To measure how different internet speeds affect the loading time of cloud-based applications
•  To compare user satisfaction between standard and optimised versions of a cloud application.

Business

Question: "How do digital marketing strategies influence customer loyalty in small retail businesses?"

Example of SMART Objectives:

• To assess how customer loyalty is influenced by digital marketing strategies over a 6-month period.
• Business: To identify which digital marketing channel leads to the most repeat purchases in small retail firms.

A hypothesis is a clear and testable statement that predicts the relationship between two or more variables. It is used mainly in quantitative research and helps guide the data collection and analysis process. Hypotheses are based on previous research, theory, or observation.

Types of Hypotheses:

• Null Hypothesis (H₀): States that there is no effect or relationship between the variables. It acts as a default or starting assumption.
• Alternative Hypothesis (H₁): Suggests there is a meaningful effect or relationship. This is what researchers usually hope to prove.

Why Hypotheses Are Important:

• They make research measurable and focused.
• They help in deciding which data to collect.
• They provide a basis for statistical testing.

Structure of a Hypothesis: Usually written as “If X happens, then Y will change” or “X affects Y”. X is the independent variable (the cause or input), and Y is the dependent variable (the result or outcome).

Examples:

Computing

• Question: "How does poor internet connectivity affect the user performance of cloud-based applications?"

  • H₀: Internet speed has no effect on the performance of cloud-based applications.
  • H₁: Lower internet speed significantly reduces the performance of cloud-based applications.

Business

• Question: "How do digital marketing strategies influence customer loyalty in small retail businesses?"
  • H₀: Digital marketing campaigns do not affect customer loyalty.
  • H₁: Customers exposed to digital marketing campaigns are more likely to become repeat buyers.

Tip for Students: Your hypothesis should connect clearly with your research question and objectives. Avoid vague words like "improves" or "worsens" without explaining how or why. Instead, focus on specific, measurable changes.

The literature review summarises and analyses previous research related to your topic. It shows what is already known, where there are gaps, and how your research will contribute something new. A good literature review helps to justify why your study is needed.

Purpose of the Literature Review:

• To understand what others have discovered about your topic
• To identify gaps, contradictions, or questions that remain
• To provide a theoretical background and define key concepts
• To show how your research connects to or builds on past studies

Tips for Writing a Good Literature Review:

• Use reliable sources such as peer-reviewed journals, books, and academic reports
• Organise by themes, methods, or findings — not just a list of articles
• Summarise the key points, compare ideas, and point out areas of disagreement or uncertainty
• Show clearly how the existing research leads to your research question

Examples:

• Computing: Reviewing studies that measure how internet speed affects cloud computing, identifying a lack of research on user experience in low-bandwidth environments.
• Business: Analysing literature on digital marketing effectiveness, and identifying that few studies focus on small retail businesses and repeat customer behaviour.

Tip for Students: Keep notes as you read, and always link each source to your research question or objectives. Avoid just describing what each article says — aim to evaluate and compare.

Research design is the overall plan for your study. It explains how you will collect, measure, and analyse data to answer your research question. Your choice of design depends on what kind of question you are asking.

Main Types of Research Designs:

• Exploratory: Used when little is known about a topic. It helps you learn more and define problems more clearly (e.g., interviews with users to explore problems with a cloud app).
• Descriptive: Describes things as they are (e.g., a survey to find out how often customers return to a small shop).
• Correlational: Looks at the relationship between variables, but does not prove cause and effect (e.g., checking if better internet speeds are linked with happier cloud app users).
• Experimental: Tests cause and effect by controlling variables (e.g., giving different groups different internet speeds and measuring app performance).

Tip for Students: Choose a design that matches your question and your available time and resources.

Variables are the things you study and measure in your research. They can change and vary between people, places, or times. Measuring variables correctly is important for accurate results.

Types of Variables:

• Independent Variable: The one you change or test (e.g., internet speed).
• Dependent Variable: The one you observe or measure (e.g., how fast a cloud app loads).
• Control Variables: Things you keep the same to make your test fair (e.g., the same app, same device).

How to Measure Variables:

• Choose clear, simple ways to measure (e.g., app loading time in seconds, customer return rate as a percentage).
• Use tools like surveys, timers, or observation logs.
• Be consistent in how and when you collect data.

Tip for Students: Make sure your variables and measurements match your objectives and are easy to explain.

Sampling is the process of choosing who or what to include in your study. You usually cannot study everyone, so you choose a smaller group that represents the larger population. The following sampling types are the most comonly used in research.

Main Types of Sampling:

• Random Sampling: Everyone in the population has the same chance of being chosen. It reduces bias but can be hard to do without a full list of people.
• Stratified Sampling: The population is split into clear groups (like age, gender, or job role), and then people are randomly picked from each group. This helps make sure all groups are included fairly.
• Systematic Sampling: You choose every nth person from a list (e.g., every 5th customer). It’s easy and quick, but can be biased if the list has patterns.
• Cluster Sampling: Instead of picking individuals, you pick whole groups (e.g., one department in a company). It's useful when the population is spread out or large.
• Convenience Sampling: You choose people who are easy to reach (e.g., classmates or friends). This is fast and common in student research but can give unbalanced results.
• Purposive Sampling: You choose people on purpose because they have special knowledge or experience (e.g., IT staff when researching software use). This is useful for expert opinions.
• Snowball Sampling: You ask participants to suggest others to take part. This works well for hard-to-reach groups (e.g., freelance developers), but it may limit variety.

Tip for Students: Be clear about who you are studying, how you chose them, and why they are suitable for answering your question.

Sometimes, instead of collecting your own data, you can use existing datasets collected by others (e.g., company records, government data, open databases).

Benefits:

• Saves time and resources
• Can include large and high-quality data
• Allows comparisons across time or groups

Things to Consider:

• Is the data reliable and up to date?
• Does it match your research question?
• Do you need to clean or re-organise it?
• Do you have permission to use it?

Tip for Students: Always explain where the dataset came from, what it includes, and how you used or changed it for your study.

Ethics in research means protecting the rights, safety, and well-being of everyone involved in your study. You must follow ethical rules whether you’re doing interviews, surveys, or using online data.

Key Principles:

• Informed Consent: People must agree to take part and understand what the study involves.
• Confidentiality: Keep personal data safe and private.
• Voluntary Participation: People can choose to join or leave at any time.
• No Harm: Avoid causing stress, embarrassment, or risk to participants.
• Transparency: Be honest about your research aims and how the data will be used.

Tip for Students: Before collecting data, get approval from your tutor or ethics panel. Respect participants and follow your institution’s ethics guidelines.