1. What is Research?

Before we can understand a research problem, we must be clear about what research itself means. Many students think research means “reading many books and writing a long report”. That is study, not research.

The word research comes from the French word recherche, meaning “to search again”. This tells us the spirit of research: we look again, more carefully, at something the world thinks it already understands.

1.1 Characteristics of Research

1. Systematic: It follows a definite sequence of steps — problem, objectives, method, data, analysis, conclusion. It is not random trial and error.
2. Objective: Conclusions are based on evidence, not on the researcher's personal liking, belief or opinion.
3. Logical: Reasoning connects the evidence to the conclusion. The reader must be able to see why the data supports the claim.
4. Empirical or verifiable: The claim can be tested against measurement, experiment or proof.
5. Replicable: Another person, following the same method, should get comparable results.
6. Original: It adds something new — a new result, a new method, a new comparison or a new application.
7. Cyclical: Research ends by raising new questions. One study's conclusion becomes the next study's problem.

1.2 Types of Research

Fundamental, Basic, Applied and Industrial Research

These four terms are frequently asked together as a 10-mark question (2.5 marks each), so each needs a definition, a distinguishing feature and one computing example.

1. Fundamental research (also called pure or theoretical research). Undertaken to extend the boundary of knowledge itself, with no immediate application in view. It is driven by curiosity, generalizes into theories and laws, and is usually published openly.
   CS example: proving that a newly defined problem is NP-complete; establishing a tighter lower bound on the communication complexity of a distributed protocol.
2. Basic research. In the standard classification, basic and fundamental research mean the same thing — research aimed at fundamental understanding rather than at a particular use. Where an examiner lists them separately, distinguish them by emphasis: treat fundamental as the discovery of entirely new principles, and basic as the systematic study of the properties and behaviour of known principles that underpin later application.
   CS example: studying the convergence behaviour of gradient descent on non-convex surfaces — no product is intended, but every deep-learning system depends on the answer.
3. Applied research. Undertaken to solve a specific, identified practical problem. It uses the findings of fundamental research and is judged by whether the problem is actually solved.
   CS example: designing a lightweight intrusion-detection model that runs within the memory budget of an IoT gateway.
4. Industrial research. Applied research and development carried out within a company or an R&D laboratory with a commercial objective — a marketable product, a cheaper process or a patentable technology. It is distinguished less by its methods than by its constraints: it is deadline-driven, cost-driven, frequently confidential, and its output is often a patent or a shipped product rather than a paper.
   CS example: an industrial laboratory reducing the inference latency of a speech model to meet a product specification of 100 ms on a mid-range phone.

1.3 Research vs. Ordinary Problem Solving

This distinction is frequently asked as a short question.

2. The Research Process

2.1 Steps in the Research Process

1. Formulating the research problem. Identify the broad area, narrow it to a specific difficulty, and state it in clear, answerable terms. Everything that follows is decided here.
2. Extensive literature survey. Read journals, conference proceedings, theses, surveys and standards to establish what is already known and to locate the gap. (Treated in detail in Unit III.)
3. Developing the working hypothesis. State the tentative, testable answer that the study will examine. Not every study has a formal hypothesis — exploratory studies may not — but hypothesis-testing research must.
4. Preparing the research design. Decide the overall arrangement for collecting and analysing data: experimental or non-experimental, what is controlled, what is measured, and how bias is excluded.
5. Determining the sample design. Decide which units will actually be studied and by what sampling technique (probability or non-probability), and justify the sample size.
6. Collecting the data. Execute the plan — experiment, benchmark, questionnaire, interview, simulation, observation or log capture — using validated instruments.
7. Execution of the project. Carry out the work in a controlled and systematic manner, keeping records so that the study remains replicable and any deviation from the plan is documented.
8. Analysis of data. Edit, code, classify and tabulate the data, then summarise it descriptively and examine it with the appropriate statistical or computational techniques.
9. Hypothesis testing. Apply the chosen test (t-test, ANOVA, chi-square, and so on) and decide whether the evidence supports rejecting the null hypothesis.
10. Generalisation and interpretation. Explain what the results mean, compare them with existing knowledge, and state how far they can legitimately be generalised.
11. Preparation of the report. Write up the problem, method, results, conclusions and limitations, with complete references, so that another researcher can verify and build on the work. (Treated in detail in Unit IV.)

2.2 Research as Fact-Finding, Analysis and Evaluation

A recurring statement question is: “Research is much concerned with proper fact-finding, analysis and evaluation — do you agree?” The expected answer is yes, with justification, structured around the three named activities.

1. Fact-finding. Research begins by gathering facts, not opinions. Facts are obtained by systematic observation, experiment, measurement, survey or documented record — never by assumption or intuition. In computing this means benchmark measurements, execution logs, traces, survey responses and existing datasets. The quality of every later stage is bounded by the quality of these facts: garbage in, garbage out.
2. Analysis. Raw facts are meaningless until they are edited, classified, tabulated, summarised and tested. Analysis converts a mass of observations into patterns, relationships and tested statements — means and percentiles, correlations, significance tests. Without analysis the researcher has data but no finding.
3. Evaluation. The analysed findings must then be judged: are they valid, reliable and significant? Do they answer the stated question? How do they compare with existing knowledge? What are their limitations and how far may they be generalised? Evaluation is what converts a finding into a defensible conclusion.

• Why all three are necessary. Fact-finding alone is mere data collection — a report, not research. Analysis without careful fact-finding produces precise conclusions from unreliable inputs. Facts and analysis without evaluation produce numbers whose meaning and trustworthiness nobody has judged. Only the three together yield knowledge.
• This is what distinguishes research from opinion. A conclusion in research is accepted because it survived measurement, testing and critical evaluation — not because of who stated it.

3. Understanding the Research Problem

3.1 Meaning of a Research Problem

In very simple language: a research problem is the gap between what we know and what we need to know. It is not a complaint, not a topic, and not a wish. It is a question that can be answered by collecting and analysing evidence.

3.2 Components of a Research Problem

A well-formed research problem always contains the following elements. Writing these five points is a standard answer for a 5-mark question.

1. The researcher (individual or organisation): the person or group who feels the difficulty and will conduct the study.
2. The objective: what the researcher wants to achieve or find out.
3. The environment or context: the setting in which the problem exists (e.g. a cloud data centre, a mobile network, a hospital information system). Results are valid only inside this context.
4. Alternative courses of action: at least two possible ways of proceeding must exist. If only one action is possible, there is nothing to investigate.
5. Doubt about the choice / uncertainty of outcome: the researcher does not know in advance which alternative is better. If you already know the answer, it is not research.

3.3 Significance of a Research Problem

“Significance” means: why does this problem deserve time, money and effort? A problem is significant when solving it produces value for someone other than the researcher.

1. It gives direction to the whole study. The problem decides the objectives; the objectives decide the method; the method decides the data and instruments; the data decides the analysis. A wrong problem statement makes every later step wrong.
2. It defines the boundary of work. It tells you what to include and, equally important, what to leave out.
3. It prevents waste of resources. Time, laboratory access, computing hours and funding are limited; a clear problem stops you from wandering.
4. It advances knowledge. It fills a genuine gap in the literature.
5. It has practical or social utility. It improves a system, a product, a process or people's lives.
6. It provides a basis for evaluation. Examiners, reviewers and funding agencies judge the work by asking whether the stated problem was actually solved.
7. It is the foundation of the research proposal. No agency funds a study whose problem is vague.

3.4 Related Terms Students Confuse

4. Sources of a Research Problem

Research problems are not created by sudden inspiration. They are found in identifiable places. Learn these as a numbered list.

1. Personal experience and observation. Difficulties faced in daily life, in the laboratory, in an internship or in a project. Example: noticing that a campus attendance app fails whenever more than 200 students log in together.
2. Literature — research papers, journals and theses. This is the most reliable source. Look specifically at:
   • the “Future Work” or “Limitations” section, where authors state exactly what they could not do;
   • contradictions between two papers reporting opposite results;
   • results tested only on one dataset, one language or one platform.
3. Consultation with experts, faculty and supervisors. Experienced researchers know which questions are open, which are already solved, and which are worth the effort.
4. Industry needs and practical problems. Companies face real bottlenecks (cost, latency, energy, accuracy) but often lack time to study them systematically.
5. New technology and new tools. Every new technology creates fresh unanswered questions — e.g. large language models raised new problems in evaluation, hallucination and security.
6. Existing theories — their testing and extension. A theory validated in one domain may be applied or tested in another.
7. Government policies, standards and reports. National missions, IEEE/IETF standards and regulatory requirements (e.g. data protection rules) generate research needs.
8. Conferences, seminars, workshops and technical discussions. Panel discussions and keynote talks often explicitly announce open challenges.
9. Replication and verification of earlier work. Checking whether a published result still holds on new hardware, new data or at larger scale is legitimate research.
10. Interdisciplinary intersections. Problems appear where two fields meet — computing with agriculture, healthcare, law, or education.
11. Funding agency calls and problem statements. Bodies such as DST, SERB, MeitY, AICTE and hackathon organisers publish lists of problems needing solutions.

5. Identification and Formulation of a Research Problem

5.1 Meaning

Think of it as a funnel: you pour in a wide interest at the top and a single sharp question comes out at the bottom.

5.2 Steps in Identifying a Research Problem

1. Select a broad area of interest. Choose a field you genuinely like, because you will spend months inside it. Example: Machine Learning.
2. Narrow the area to a specific topic. Divide the field into sub-areas and pick one. Example: ML → medical image classification.
3. Carry out a preliminary literature survey. Read recent survey papers and 15–20 recent research papers to learn what is already solved. This step prevents you from “re-inventing the wheel”.
4. Identify the gap. Write down, in one sentence, what is still missing, unverified, untested or unsatisfactory.
5. Discuss with experts and peers. Get the gap validated by someone who knows the field.
6. Rephrase the gap as a research question. It should begin with how, what, why, to what extent, or which.
7. Check feasibility. Do you have the data, tools, time, skills, funds and permissions?
8. Define the scope and delimitations. State clearly what is inside and outside the study.
9. State the objectives and (if applicable) hypotheses. Write them as numbered, measurable statements.
10. Write the final problem statement. One short paragraph containing: context, gap, specific question, and expected contribution.

5.3 The Funnel in Action

5.4 A Well-Written Problem Statement

A good problem statement has four parts. Remember them as C–G–Q–C:

1. Context: what is the situation and why does it matter?
2. Gap: what exactly is not known or not working?
3. Question: the specific question you will answer.
4. Contribution: what will exist after your work that does not exist now?

6. Technique of Defining a Research Problem

The previous section described how you find a problem. This section describes the classical five-step technique of defining it — the standard textbook sequence, and the parent of several frequently asked questions (experience survey, rephrasing, necessity of defining a problem).

6.1 The Five Steps

1. Statement of the problem in a general way. Write the difficulty in broad, ordinary language, as it is felt or observed. At this stage the statement is admittedly vague.
   Example: “The campus attendance app performs badly.”
2. Understanding the nature of the problem. Discuss the problem with those who originated it or who face it, and observe the situation directly, so that you understand its origin, its context and how it is actually experienced rather than how you assume it is.
   Example: talking to the office staff reveals that “badly” means failures at 9:00 a.m., not slowness in general.
3. Surveying the available literature. Study the existing theory, published research and reported solutions. This tells you what is already solved, what techniques exist, what data is available, and whether the problem is worth pursuing at all.
4. Developing the ideas through discussions. Discuss the problem with colleagues, supervisors and, above all, with people who have practical experience of it. This step is formally called an experience survey (Section 6.2).
5. Rephrasing the research problem into a working proposition. Restate the problem in precise, operational, analytical terms so that it becomes researchable (Section 6.4).

Alongside these five steps, the following must also be settled while defining the problem:

• Technical terms and phrases must be clearly defined — “performance”, “failure”, “accuracy” each need an operational definition.
• Basic assumptions must be stated explicitly.
• The time period and the environment of the study must be fixed.
• The criteria for selecting the problem must be satisfied (feasibility, novelty, significance).
• The suitability and availability of data must be verified before the problem is finalised.

6.2 Experience Survey

Purpose

1. To gain insight into the practical dimensions of the problem that documents do not record.
2. To learn what has already been tried informally, and why it failed.
3. To identify the variables that practitioners believe actually matter.
4. To assess the feasibility of the proposed study — whether the data exists and can be obtained.
5. To narrow a broad problem to the aspects that are both important and researchable.
6. To generate hypotheses worth testing later by formal methods.

How it is conducted

• Who is included: people selected deliberately for their competence and experience, not randomly — administrators, senior engineers, domain experts, long-standing users. The sample is purposive, and it is usually small.
• How: unstructured or semi-structured interviews and discussions, conducted flexibly so that the respondent can raise issues the researcher had not anticipated.
• What is recorded: the ideas and insights, not statistics. The output is a sharpened problem statement, not a data table.

Advantages and limitations

6.3 Pilot Survey (Pilot Study)

Purposes

1. To test the instrument: are the questionnaire items clear, unambiguous and correctly understood by respondents? Does the measurement script record what it should?
2. To check the procedure: does the data-collection protocol work end to end?
3. To estimate variability, which is needed to compute the required sample size for the main study.
4. To estimate time and cost per unit of data.
5. To check feasibility — access, permissions, tooling and response rates.
6. To train the investigators and standardise their procedure.
7. To refine the research questions and the analysis plan before commitment.

Conduct

• Conducted on a small sample, conventionally of the order of 10% of the intended main sample, drawn from the same population but not reused in the main study.
• The full procedure is rehearsed exactly as planned, including the analysis.
• The results are used only to improve the design; pilot results are never reported as findings.

6.4 Rephrasing the Research Problem

This is the final and most important step of defining a problem. A problem that has not been rephrased cannot be researched, because nobody can say what evidence would answer it.

What rephrasing involves

1. Replacing vague words with operational definitions — “slow” becomes “median response time above 2 s”.
2. Naming the variables explicitly: what is being varied, what is being measured, what is held constant.
3. Fixing the population, environment and time period of the study.
4. Stating the assumptions and the boundary of the study.
5. Expressing the problem as an answerable question or a testable proposition.
6. Verifying that the data required is obtainable.

7. Criteria and Characteristics of a Good Research Problem

7.1 Essential Attributes

These are the qualities that a research problem must possess. This is one of the most frequently asked 10-mark questions in this unit.

1. Clarity and unambiguity. The problem must be stated in precise words with no vague terms. “Improve system performance” is unclear; “reduce average query response time” is clear.
2. Specificity (narrow focus). It must address one well-defined issue, not an entire field.
3. Novelty / originality. It must not already be fully solved. Novelty may lie in a new method, a new application, a new dataset, or a new comparison.
4. Feasibility. It must be solvable with the available time, funds, equipment, data, skill and access. A brilliant problem you cannot execute is worthless.
5. Researchability (empirical testability). It must be answerable by collecting and analysing evidence. Questions of pure belief or value (“Is open-source morally better?”) are not researchable as stated.
6. Measurability. There must be some metric or criterion by which success or failure can be judged — accuracy, latency, throughput, energy, error rate, user satisfaction score.
7. Significance and relevance. It must matter to the discipline, the industry or society.
8. Interest of the researcher. Sustained motivation is a practical requirement, not a luxury.
9. Ethical acceptability. It must not harm people, violate privacy, misuse data or breach institutional rules.
10. Availability of data and resources. The required data must be obtainable legally and in adequate quantity and quality.
11. Manageable size and time-bound. It must be completable within the allotted duration (semester, thesis period, project deadline).
12. Logical and theoretical grounding. It should connect to existing theory or literature and not stand isolated.
13. Potential for generalization or extension. Good problems open further work rather than closing a dead end.

7.2 Two Standard Checklists

Examiners like these acronyms; they also help you evaluate your own project idea.

The FINER criteria

• F — Feasible: adequate resources, data, skill and time.
• I — Interesting: engaging to the researcher and the community.
• N — Novel: produces new findings, confirms or refutes earlier ones.
• E — Ethical: acceptable to an ethics/review board.
• R — Relevant: advances knowledge, influences practice or guides further research.

The SMART criteria (mainly for objectives)

• S — Specific, M — Measurable, A — Achievable, R — Relevant, T — Time-bound.

7.3 Self-Evaluation Table for a Problem

8. Errors in the Selection of a Research Problem

Knowing what not to do is as important as knowing what to do. This too is a common examination question.

1. Selecting too broad a problem. “Artificial Intelligence in healthcare” is a continent, not a problem. It can never be completed or defended.
2. Selecting too narrow or trivial a problem. A problem so small that its answer interests nobody has no significance.
3. Choosing an already-solved problem. This happens when the literature survey is shallow. It results in wasted months and rejected papers.
4. Vague or ambiguous statement. Words like “better”, “efficient”, “optimal” without a defined metric make the problem untestable.
5. Ignoring feasibility. Choosing a problem that needs hospital data, industrial hardware, proprietary datasets or supercomputing time that you cannot obtain.
6. Following fashion or trend blindly. Picking a hot topic without genuine interest or background; the trend often shifts before the work is complete.
7. Not defining variables and metrics. If success is not measurable, results cannot be evaluated.
8. Personal bias and preconceived conclusions. Selecting a problem to prove what you already believe destroys objectivity.
9. Overlooking ethical and legal issues. Collecting personal data without consent, scraping prohibited sources, or capturing network traffic without permission.
10. Poor or absent literature survey. Leads simultaneously to duplication, wrong assumptions and missed methods.
11. Copying a problem without understanding it. Taking a supervisor's or a paper's problem without grasping the background leaves the student unable to defend the work.
12. Neglecting time and cost constraints. An excellent problem that needs three years cannot be a one-semester project.
13. Confusing the problem with the solution. Writing “To develop a blockchain-based voting system” states a solution; the problem (what is wrong with current systems, and which specific weakness will be fixed) has not been stated at all.
14. Non-availability of a reliable dataset or benchmark. Without data or a baseline, no claim can be validated.

9. Defining the Scope and Objectives

9.1 Scope of Research

Why is defining the scope necessary?

1. It keeps the study focused and prevents “scope creep” (uncontrolled expansion).
2. It makes the study achievable within available time and resources.
3. It tells the reader how far the results may be generalized, preventing over-claiming.
4. It helps the examiner or reviewer judge the work fairly, against what was promised.
5. It guides the choice of data, sample, methods and instruments.

Elements that define the scope

1. Subject boundary: which concepts, techniques or variables are included.
2. Population/data boundary: whose data, which dataset, which systems.
3. Geographical boundary: which location, campus, region or network.
4. Time boundary: the period of data collection or the duration of the study.
5. Methodological boundary: which methods and tools will be used.
6. Exclusions: an explicit statement of what is deliberately left out.

Delimitations vs. limitations

9.2 Research Objectives

Types of objectives

• General (primary) objective: one broad statement of the overall aim of the study.
• Specific (secondary) objectives: 3–5 narrow, sequential objectives that together fulfil the general objective.

Rules for writing objectives

1. Begin with an action verb: to identify, to measure, to design, to develop, to compare, to evaluate, to analyse, to validate.
2. Avoid vague verbs: to understand, to study, to know, to learn — these cannot be measured.
3. Each objective should address one idea only.
4. Objectives must be logically ordered — each builds on the previous one.
5. They must be achievable within the available resources and time.
6. They must be directly linked to the problem statement; every objective must serve the problem and every part of the problem must be covered by an objective.
7. Ideally 3 to 5 objectives; too many indicate the scope is too large.

9.3 Research Questions and Hypotheses

• A research question states the problem in interrogative form. Example: Does replacing payload inspection with flow features reduce detection accuracy on encrypted traffic?
• A hypothesis is a tentative, testable statement of the expected answer, framed before the study. Example: Flow-feature based detection achieves an F1-score within 5% of payload-based detection on encrypted traffic.
• The null hypothesis (H₀) states there is no difference or no effect; the alternative hypothesis (H₁) states there is one. Statistical tests attempt to reject H₀.

10. Formulation of a Research Hypothesis

10.1 Meaning

A hypothesis is an intelligent guess, not a random one: it is derived from theory, from the literature or from observation, and it is written in a form that data can contradict. A statement that no possible observation could refute is not a hypothesis.

10.2 Characteristics of a Good (Workable) Hypothesis

1. Clear and precise. Stated in unambiguous language, so that conclusions drawn from it are reliable.
2. Capable of being tested. There must exist a procedure and data by which it could be shown false. Testability is the defining property.
3. States a relationship between variables. If it is a relational hypothesis, it must name the variables and the expected relation.
4. Limited in scope and specific. Narrow hypotheses are testable; sweeping ones are not.
5. Stated in simple, understandable terms, without unnecessary jargon.
6. Consistent with known facts. It should not contradict established, well-verified knowledge.
7. Amenable to testing within a reasonable time. However elegant, a hypothesis that cannot be tested with available resources is useless.
8. Empirically grounded. It must explain the facts that gave rise to it, and be capable of being supported by evidence.

10.3 Types of Hypothesis

10.4 Purpose and Functions of Formulating a Hypothesis

This is the standard 5-mark half of the question “describe the formulation of a research hypothesis and explain its purpose”.

1. It gives direction to the research. It tells the researcher precisely what to look for and prevents aimless data collection.
2. It delimits the scope of the study. It fixes which variables are relevant and which are not.
3. It determines the research design. An experimental hypothesis demands an experimental design with controls; a descriptive study does not.
4. It decides the data to be collected and the method of collection. You collect only what is needed to test the hypothesis.
5. It determines the statistical technique. The form of the hypothesis selects the test — t-test, ANOVA, chi-square.
6. It links theory to observation. It converts an abstract theoretical expectation into an observable, measurable prediction.
7. It provides the framework for conclusions. The conclusion is a statement about whether the hypothesis was supported.
8. It makes the research objective and falsifiable. By committing to a prediction in advance, the researcher cannot later adjust the claim to fit whatever the data happened to show. (This is why HARKing — hypothesising after results are known — is a research malpractice; see Unit III.)
9. It saves time and resources.

10.5 Steps in Formulating a Hypothesis

1. Study the problem and the literature to identify the expected relationship.
2. Identify the independent and dependent variables and define them operationally.
3. State the expected relationship in a clear declarative sentence (the research hypothesis).
4. Convert it into a statistical pair: the null hypothesis H₀ and the alternative H₁.
5. Check the hypothesis against the eight characteristics above.
6. Decide, in advance, the test and the significance level by which it will be judged.

10.6 Procedure of Hypothesis Testing

1. State H₀ and H₁.
2. Choose the level of significance α (commonly 0.05 or 0.01) — the risk of wrongly rejecting a true H₀ that you are willing to accept. Choose it before seeing the data.
3. Select the appropriate test on the basis of the data type, the number of groups and the distributional assumptions.
4. Compute the test statistic from the sample.
5. Determine the critical value for the chosen α and the degrees of freedom (or compute the p-value).
6. Compare and decide: if the computed value exceeds the critical value (equivalently, if p < α), reject H₀; otherwise do not reject it.
7. Interpret in the language of the problem, not in the language of statistics alone.

Type I and Type II errors

• Reducing α increases β for a fixed sample size; the way to reduce both is to increase the sample size.
• One-tailed tests are used for directional hypotheses, two-tailed for non-directional ones. A two-tailed test is more conservative.

10.7 The Chi-Square (χ²) Test

The chi-square test is applied to categorical (frequency) data arranged in a contingency table. It is the test most often set as a numerical question, so learn the procedure completely.

Uses

• Test of independence: are two attributes associated, or independent? (e.g. is survival associated with inoculation?)
• Test of goodness of fit: does an observed distribution match an expected theoretical one?
• Test of homogeneity: do two or more populations have the same distribution over categories?

Formula and procedure

with degrees of freedom ν = (r−1)(c−1) for an r × c contingency table.

1. State H₀: the two attributes are independent (no association); H₁: they are associated.
2. Compute the expected frequency E_ij for every cell.
3. Compute (O−E)²/E for every cell and sum them to get χ².
4. Find ν = (r−1)(c−1) and read the table value at the chosen α.
5. If computed χ² > table value, reject H₀ — the attributes are associated.
6. Interpret in the language of the problem.

Worked example: effectiveness of a vaccine

11. Research Design

11.1 Meaning and Need

The design stands between the hypothesis and the data: the hypothesis says what is to be tested, and the design says how the evidence will be produced so that the test is fair.

Why a research design is necessary

1. It provides a plan of action, so the study proceeds systematically rather than by improvisation.
2. It ensures that the data collected is actually relevant to the research question.
3. It minimises bias and maximises the reliability of the evidence.
4. It controls extraneous variables so that observed effects can be attributed to the intended cause.
5. It economises effort, time and money, and makes them estimable in advance.
6. It makes the study replicable — another researcher can repeat it.
7. It identifies the analysis before the data arrives, which prevents the results from dictating the question.

Features of a good research design

Relevance to the objective; flexibility (in exploratory work); appropriateness to the question; efficiency and economy; the smallest experimental error; minimum bias; maximum reliability; and information that permits consideration of many aspects of the problem.

11.2 Basic Concepts

11.3 Types of Research Design

1. Exploratory (formulative) design. Used when little is known. The objective is to discover ideas and sharpen the problem, not to test anything. Its defining feature is flexibility. Methods: literature survey, experience survey, analysis of insight-stimulating examples.
2. Descriptive and diagnostic design. Used to describe the characteristics of a phenomenon accurately, or to determine the frequency with which something occurs and its association with something else. Its defining feature is rigidity — the procedure must be fixed to be accurate. Methods: surveys, observation, fact-finding studies.
3. Hypothesis-testing (experimental) design. Used to test a causal relationship. Its defining feature is control. This is the category the examination usually asks about.

11.4 Research Designs in Hypothesis-Testing Research

The three basic principles of experimental design

1. Replication. Repeat the basic experiment on several units, so that the experimental error can be estimated and the result is not an accident of one trial. In computing: run each benchmark 30 times rather than once.
2. Randomisation. Assign treatments to units at random, so that unknown extraneous factors are distributed evenly across groups and cannot systematically favour one treatment. In computing: randomise the order in which the two schedulers are benchmarked, so that machine warm-up does not always benefit the same one.
3. Local control (blocking). Group the units into homogeneous blocks and apply all treatments within each block, so that a known source of variation is removed from the experimental error. In computing: if three different machines must be used, treat each machine as a block and run every algorithm on every machine.

Informal experimental designs

These use a simple, sometimes non-statistical, notion of control.

1. Before-and-after without control design. A single group is measured before the treatment and again after it; the effect is the difference.
   Effect = (level after treatment) − (level before treatment).
   Weakness: any change over time — other events, learning, system drift — is wrongly credited to the treatment.
   CS example: measuring page-load time before and after enabling a cache, on the same server.
2. After-only with control design. Two groups are used — experimental and control — and both are measured only after the treatment is applied to the experimental group.
   Effect = (level in experimental area) − (level in control area).
   Weakness: the two groups are assumed comparable to begin with, which may be false.
   CS example: an A/B test in which one set of users receives the new recommender and another the old one, with only post-deployment click-through measured.
3. Before-and-after with control design. Two groups are measured both before and after; the change in the control group is subtracted from the change in the experimental group.
   Effect = (change in experimental area) − (change in control area).
   Strength: this is the best of the three, because it removes both the pre-existing difference between groups and the effect of time.
   CS example: measuring defect density in two teams for three months, then introducing pair programming in one team only, and measuring both for three more months.

Formal experimental designs

These use statistical procedure and the three principles above.

1. Completely Randomised Design (CRD). Treatments are assigned to experimental units entirely at random. It uses the principles of replication and randomisation, but not local control. It is the simplest design and permits unequal group sizes, but it is efficient only when the units are homogeneous. Two common forms:
   • Two-group simple randomised design — subjects randomly assigned to an experimental and a control group.
   • Random replications design — each treatment is replicated several times over randomly selected units, which averages out extraneous variation.
   Analysis: one-way ANOVA. CS example: 60 randomly chosen input files assigned at random to three compression algorithms.
2. Randomised Block Design (RBD). The units are first grouped into blocks that are internally homogeneous with respect to one known source of variation; within each block, treatments are assigned at random, and every treatment appears in every block. It uses all three principles, and removes the block-to-block variation from the error term, giving greater precision than CRD.
   Analysis: two-way ANOVA. CS example: four sorting algorithms tested on five different input-size classes, each size class forming a block; every algorithm is run within every block.
3. Latin Square Design (LSD). Used when two extraneous sources of variation must be controlled simultaneously. The treatments are arranged in a k × k square such that each treatment appears exactly once in every row and exactly once in every column — rows representing one nuisance factor and columns the other.
   Requirement: the number of treatments, rows and columns must be equal.
   Advantage: controls two sources of variation with far fewer runs than a full design.
   Limitation: needs equal numbers, and assumes no interaction between the factors.
   CS example: testing 4 caching policies across 4 machines (rows) and 4 workloads (columns), each policy appearing once per machine and once per workload — 16 runs instead of 64.

4. Factorial designs. Used when the effect of two or more independent variables (factors) is to be studied simultaneously, together with their interaction.
   • Simple factorial design (2×2): two factors, each at two levels. It reveals the main effect of each factor and the interaction effect between them.
   • Complex factorial design: more than two factors, or more than two levels (2×3, 3×3, 2×2×2, and so on).
   Advantage: far more economical than varying one factor at a time, and it is the only way to detect interaction.
   CS example: a 2×2 study of cache size (small/large) and replacement policy (LRU/LFU) on hit ratio — which may show that LFU helps only when the cache is small, an interaction that neither factor alone would reveal.

12. Investigative Approaches for Solutions

The approach must match the question. A question about “what actually happens” needs measurement; a question about “what must always be true” needs proof.

12.1 Main Investigative Approaches in Computing Science

1. Theoretical / Analytical approach. The problem is abstracted into a formal model (graph, automaton, logic, complexity class) and conclusions are derived by reasoning and proof. Strength: conclusions are certain and general. Limitation: abstraction may omit important real-world detail. Example: proving the time complexity of a scheduling algorithm.
2. Experimental / Empirical approach. The researcher builds or uses a real system, applies controlled inputs, and measures the output. Strength: evidence about real behaviour. Limitation: results hold only for the tested conditions. Example: benchmarking two database engines under identical workloads.
3. Simulation and modelling approach. A software model of the system is executed with varying parameters. Strength: cheap, safe, repeatable, allows study of very large or unavailable systems. Limitation: results are only as good as the model's assumptions. Example: simulating 10,000 IoT nodes in NS-3.
4. Constructive / Design-and-build (engineering) approach. A new artifact — algorithm, tool, protocol, framework — is designed, implemented and then evaluated. This is the most common approach in B.Tech projects. Example: developing a compiler optimisation pass and measuring the speedup it produces.
5. Survey approach. Structured questionnaires or interviews collect data from a sample of people. Example: surveying 300 developers about their code-review practices.
6. Case study approach. One system, organisation or project is studied in depth over time. Example: studying how one company migrated a monolith to microservices.
7. Observational / Measurement study. The researcher measures an existing system without altering it. Example: measuring real traffic patterns on a campus network.
8. Comparative approach. Two or more existing methods are evaluated on common data with common metrics to determine relative merit.
9. Mixed-methods approach. Two or more of the above are combined — for instance, prove correctness mathematically, then demonstrate the implementation, then measure its performance. Strong research usually combines approaches.

12.2 Choosing the Right Approach

12.3 Factors Influencing the Choice of Approach

1. Nature and wording of the research question.
2. Type of evidence acceptable in the discipline.
3. Availability of data, hardware, software and subjects.
4. Time and cost constraints.
5. Skill of the researcher.
6. Ethical constraints (some experiments cannot be performed on live systems or people).
7. Required level of accuracy and generality of the result.

13. Data Collection

13.1 Types of Data

13.2 Methods of Data Collection

1. Experimentation and benchmarking: running controlled tests on a system and recording performance counters.
2. Observation and logging: instrumenting software to record events, traces and metrics automatically.
3. Questionnaires and surveys: structured forms with closed or open questions, distributed online or in person.
4. Interviews: structured, semi-structured or unstructured discussions with users or experts.
5. Simulation output: data generated by executing a validated model.
6. Existing datasets and repositories: public benchmarks, Kaggle/UCI datasets, software repositories, system logs.
7. Document and literature analysis: extracting data from published papers, reports and standards (used in systematic reviews and meta-analysis).
8. Sensors and hardware measurement: power meters, network taps, embedded sensors.

13.3 Sampling

When the whole population cannot be measured, we study a sample.

• Probability sampling: random, systematic, stratified, cluster. Every unit has a known chance of selection; results can be generalized statistically.
• Non-probability sampling: convenience, purposive, quota, snowball. Easier but the results may be biased.
• Sample size must be large enough for the intended statistical test; too small a sample gives unreliable conclusions.

13.4 Requirements of Good Data

1. Relevance: it must actually answer the research question.
2. Accuracy: free from measurement error.
3. Reliability: repeated measurement gives consistent values.
4. Validity: the instrument measures what it claims to measure.
5. Adequacy: sufficient quantity for the analysis planned.
6. Representativeness: the sample reflects the population.
7. Timeliness: data is current enough to support the conclusion.
8. Ethical acquisition: collected with consent, permission and respect for privacy.

14. Data Analysis

14.1 Steps in Data Analysis

1. Editing/cleaning: removing errors, duplicates, outliers and missing values.
2. Coding: assigning symbols or numbers to categories so the data can be processed.
3. Classification and tabulation: arranging data into groups and tables.
4. Descriptive analysis: summarising the data.
5. Inferential analysis: drawing conclusions about the population from the sample.
6. Visualization: graphs, plots and charts that reveal patterns.

14.2 Descriptive Analysis

• Measures of central tendency: mean, median, mode. For performance data the median and percentiles (e.g. 95th percentile latency) are often more honest than the mean, because the mean is distorted by outliers.
• Measures of dispersion: range, variance, standard deviation, inter-quartile range. These tell you how consistent the results are.
• Frequency distributions and cross-tabulation.

14.3 Inferential Analysis

• Hypothesis testing: t-test (comparing two means), ANOVA (comparing more than two groups), chi-square test (categorical data). The procedure, the two types of error and a fully worked chi-square example are given in Section 10.
• Confidence intervals: express the range within which the true value probably lies.
• p-value: the probability of observing the result if the null hypothesis were true; a small p-value (< 0.05) suggests the difference is unlikely to be due to chance alone.
• Correlation and regression: measure the strength of relationship between variables and build predictive models.

14.4 Qualitative Analysis

For interviews and open-ended responses: transcription → coding → grouping codes into themes → thematic interpretation. Content analysis and grounded theory are standard techniques.

14.5 Common Tools

Python (Pandas, NumPy, SciPy, scikit-learn, Matplotlib), R, MATLAB, SPSS, Excel, Origin, Tableau; and for simulation-generated data, the analysis modules of NS-3, OMNeT++, CloudSim or GEM5.

15. Interpretation of Results

Analysis tells you what the numbers are. Interpretation tells you what they imply. This is where research actually happens, and it is the step students most often skip.

15.1 Steps in Interpretation

1. Restate each finding in plain language.
2. Relate the finding to the corresponding objective and hypothesis — was it supported or rejected?
3. Compare with results reported in the literature; explain agreements and disagreements.
4. Explain the underlying reason or mechanism for the observed behaviour.
5. State the practical or theoretical implications.
6. State the limitations and threats to validity honestly.
7. Suggest directions for future work.

15.2 Precautions in Interpretation

1. Correlation is not causation. Two variables rising together does not prove one causes the other; a third factor may cause both.
2. Do not over-generalize. Results are valid only within the stated scope — the tested dataset, workload, hardware and time period.
3. Do not ignore contradictory data. Reporting only favourable results is scientific misconduct.
4. Distinguish statistical significance from practical significance. A 0.3% improvement may be statistically significant yet practically useless.
5. Beware of confounding variables that were not controlled.
6. Do not confuse the artefact with the phenomenon — an unusual result may come from a bug in the measurement script rather than from the system.
7. Report uncertainty using confidence intervals or standard deviations.

15.3 Threats to Validity

• Internal validity: is the observed effect really caused by the factor you changed?
• External validity: do the results generalize beyond your setting?
• Construct validity: does your metric really capture the concept you claim to measure?
• Conclusion validity: is the statistical reasoning sound?

16. Necessary Instrumentation

16.1 Types of Instrumentation in Computer Science Research

1. Hardware instruments: servers, testbeds, embedded boards, oscilloscopes, power meters, network taps, GPUs.
2. Software measurement tools: profilers (gprof, perf, VTune), benchmarking tools (iperf, SPEC, TPC-C, JMeter), monitoring systems (Prometheus, Grafana, Wireshark).
3. Simulators and emulators: NS-3, OMNeT++, CloudSim, GEM5, Mininet.
4. Data-collection instruments for human subjects: questionnaires, interview schedules, observation checklists, rating scales (e.g. Likert scale).
5. Analysis and statistical tools: Python/R libraries, SPSS, MATLAB.
6. Development and versioning environment: IDEs, Git, Docker containers for reproducible environments.

16.2 Criteria for Selecting an Instrument

1. Validity: it measures exactly the quantity of interest.
2. Reliability: repeated measurements are consistent.
3. Accuracy and precision: error is small and resolution is sufficient.
4. Low overhead/intrusiveness: a profiler that itself slows the program distorts the result (this is called the probe effect).
5. Suitability to the scale of the experiment.
6. Availability, cost and licensing.
7. Acceptance in the research community — standard benchmarks make your results comparable.
8. Ease of use and adequate documentation.

16.3 Calibration, Validation and Standardization

• Calibration: checking an instrument against a known reference before use, to ensure it reads correctly.
• Pilot testing: conducting a small trial run to detect problems in the instrument or procedure before the full study.
• Standardization: using well-known benchmarks and standard procedures so that others can compare and reproduce your results.
• Documentation: recording the exact tool versions and settings; this is essential for replication.

17. A Complete Worked Example

The following example ties every concept of Unit I together. Reproducing a table like this in the exam demonstrates full understanding.

18. Chapter Summary

1. Research is a systematic, objective, verifiable and original investigation, concerned throughout with proper fact-finding, analysis and evaluation.
2. Research may be classified by purpose into fundamental (basic), applied and industrial research; by data into quantitative and qualitative; by approach into theoretical and empirical; and by objective into descriptive, analytical and exploratory.
3. The research process has eleven steps, from formulating the problem to preparing the report, and it is iterative rather than linear.
4. A research problem is a clearly stated gap between existing knowledge and required knowledge; it has five components: researcher, objective, environment, alternatives and uncertainty.
5. Problems come from experience, literature (especially “future work” sections), experts, industry, new technology, theory, standards, conferences, replication, interdisciplinary areas and funding calls.
6. Identification is a funnel: area → topic → literature survey → gap → question → feasibility check → scope → objectives → problem statement.
7. Defining a problem follows five steps: general statement, understanding its nature, literature survey, developing ideas through discussion (experience survey), and rephrasing it into a working proposition stated in operational terms.
8. An experience survey consults people with practical experience in order to sharpen the problem; a pilot survey rehearses the whole procedure on a small sample to correct the design before the main study. Neither is reported as a finding.
9. A good problem is clear, specific, novel, feasible, researchable, measurable, significant, ethical and time-bound (remember FINER and SMART).
10. Common errors: too broad, already solved, vague, infeasible, trend-driven, biased, unmeasurable, unethical, or stating a solution instead of a problem.
11. Scope sets the boundary; objectives break the problem into measurable tasks written with action verbs.
12. A hypothesis is a tentative, testable statement of a relationship between variables. It must be clear, testable, specific, simple, consistent with known facts and testable in reasonable time. Its purpose is to give direction, delimit scope, decide the design, the data and the statistical test, and to make the study falsifiable.
13. Hypothesis testing proceeds: state H₀ and H₁ → choose α → select the test → compute → compare with the critical value → decide → interpret. A Type I error rejects a true H₀; a Type II error fails to reject a false one.
14. The chi-square test compares observed and expected frequencies, χ² = ∑ (O−E)²/E with ν = (r−1)(c−1); for a 2×2 table the 5% critical value is 3.841.
15. A research design is the blueprint for collecting and analysing data with relevance and economy. Experimental designs rest on replication, randomisation and local control, and comprise the three informal designs (before-and-after without control; after-only with control; before-and-after with control) and the formal designs CRD, RBD, Latin square and factorial.
16. The investigative approach must match the question: theoretical, empirical, simulation, constructive, survey, case study, observational, comparative or mixed.
17. Data must be relevant, accurate, reliable, valid, adequate, representative and ethically obtained.
18. Analysis summarises and tests; interpretation explains meaning, compares with literature, and states limitations. Correlation is not causation.
19. Instrumentation must be valid, reliable, accurate, low-overhead, standard and properly calibrated and documented.

19. Glossary of Key Terms

20. Examination Practice Questions

20.1 Very Short Answer (2 marks each)

1. Define a research problem.
2. Differentiate between a research topic and a research problem.
3. State any four sources of a research problem.
4. What is meant by the scope of research?
5. Distinguish between delimitations and limitations.
6. What is a hypothesis? Give one example from computer science.
7. Differentiate between primary and secondary data.
8. Define reliability and validity of an instrument.
9. What is meant by the probe effect?
10. Expand and explain the FINER criteria.
11. Why is “correlation is not causation” important in interpretation?
12. Differentiate between data analysis and interpretation.
13. State the steps involved in the research process.
14. Differentiate between fundamental and applied research with one example each.
15. What is industrial research? How does it differ from applied research?
16. What is an experience survey?
17. What is a pilot survey? State any two of its purposes.
18. What is meant by rephrasing the research problem?
19. State any four characteristics of a good hypothesis.
20. Differentiate between the null and the alternative hypothesis.
21. Differentiate between Type I and Type II errors.
22. Define research design and state any three features of a good design.
23. State the three basic principles of experimental design.
24. What is a Latin square design? When is it used?
25. What is an interaction effect in a factorial design?
26. State the formula of the chi-square test and its degrees of freedom for an r×c table.

20.2 Short Answer (5 marks each)

1. Explain the components of a research problem with a suitable example.
2. Discuss the significance of properly defining a research problem.
3. Explain, with examples, any five sources of research problems in computing science.
4. How are research objectives written? State the rules and give an example of weak and strong objectives.
5. Explain any five errors commonly made while selecting a research problem.
6. Describe the different methods of data collection used in computer science research.
7. What is instrumentation? State the criteria for selecting a research instrument.
8. Explain the precautions to be observed while interpreting research results.
9. Differentiate between quantitative and qualitative research with examples.
10. Explain the four types of validity threats in an empirical study.
11. Briefly describe the different steps involved in a research process.
12. “Research is much concerned with proper fact-finding, analysis and evaluation.” Do you agree? Justify your answer.
13. Explain the terms fundamental research, basic research, applied research and industrial research with suitable examples.
14. Explain the technique of defining a research problem.
15. Write a short note on the experience survey.
16. Write a short note on the pilot survey, and distinguish it from the experience survey.
17. What is meant by rephrasing the research problem? Illustrate with an example.
18. Describe the formulation of a research hypothesis and explain the purpose it serves.
19. Explain the procedure of hypothesis testing, including the two types of error.
20. What is a research design? Explain the need for a research design and the features of a good one.
21. Explain the three basic principles of experimental design with computing examples.
22. Explain the three informal experimental designs, stating how the effect is computed in each.
23. Differentiate between a completely randomised design and a randomised block design.

20.3 Long Answer (10 marks each)

1. What is a research problem? Explain in detail the criteria and essential characteristics of a good research problem.
2. Describe the complete process of identifying and formulating a research problem, illustrating each step with an example from computer science.
3. Discuss in detail the errors commonly committed in the selection of a research problem and explain how each can be avoided.
4. Explain the various investigative approaches available for solving research problems in computing science. State the situations in which each is appropriate.
5. Explain the stages of data collection, analysis and interpretation in a research study. Why is interpretation considered more difficult than analysis?
6. Explain the meaning of scope and objectives of research. How does a poorly defined scope affect a research project?
7. Take any research problem of your choice in computer science and develop it completely — state the problem, its significance, scope, objectives, approach, instrumentation, data collection plan and analysis plan.
8. Describe some of the important research designs used in experimental hypothesis-testing research studies.
9. What is a research design? Explain its need and features, and describe the informal and formal experimental designs with suitable computing examples.
10. Explain in detail the technique of defining a research problem. Discuss the role of the experience survey and of rephrasing the problem into a working proposition.
11. What is a hypothesis? Explain the characteristics of a good hypothesis, its types, the purpose of formulating it, and the complete procedure of hypothesis testing.
12. Explain the different steps involved in a research process, with a diagram, and explain why the process is described as iterative rather than linear.
13. In an experiment on the immunisation of goats from anthrax, the following results were obtained. Derive your inference on the effectiveness of the vaccine. (Inoculated: 15 survived, 2 died, total 17. Not inoculated: 4 survived, 10 died, total 14. Totals: 19 survived, 12 died, grand total 31.)
14. Write short notes on: (a) Experience survey, (b) Pilot survey, (c) Components of a research problem, (d) Rephrasing the research problem.