Scientific Research Process

Meaning

The scientific research process is a systematic, logical and organised sequence of steps followed by a researcher to investigate a problem, collect and analyse data, and arrive at valid conclusions. It is "scientific" because it relies on observation, hypothesis, experimentation and verification rather than on intuition or guesswork. It is "a process" because it moves through a series of interrelated stages, each depending on the previous one. It is also cyclical — the conclusion of one study often generates new questions that start another cycle.

Steps Involved in the Scientific Research Process

1. Identification and formulation of the research problem The researcher selects a broad area of interest and narrows it into a clear, specific, researchable problem. The problem is stated as a question or objective (e.g., "What is the effect of organic manure on maize yield?"). A good problem statement defines the scope, variables and boundaries of the study.

2. Review of related literature Existing books, journals, theses and reports on the topic are studied. This helps the researcher learn what has already been done, avoid duplication, identify gaps, sharpen the objectives, and select appropriate methods and tools. It also provides the theoretical framework of the study.

3. Formulation of objectives and hypotheses Clear objectives state what the study intends to achieve. A hypothesis is a tentative, testable statement about the relationship between variables (e.g., "Organic manure significantly increases maize yield"). It gives direction to data collection and is accepted or rejected at the end.

4. Research design This is the blueprint of the study. The researcher decides the type of research (descriptive, analytical, experimental, exploratory), the population and sampling technique, the variables, the methods of data collection, and the tools of analysis. A sound design minimises bias and error.

5. Sampling The population to be studied is defined, and a representative sample is drawn using probability methods (random, stratified, cluster) or non-probability methods (purposive, convenience). Sample size is fixed on the basis of the required precision and resources.

6. Collection of data Data are gathered through primary sources (observation, interview, questionnaire, experiment, field survey) and secondary sources (records, journals, government publications). Care is taken to use valid, reliable and pre-tested tools so that the data are accurate and unbiased.

7. Processing and analysis of data Collected data are edited, coded, classified and tabulated. Statistical tools such as mean, percentage, correlation, t-test, chi-square or ANOVA are applied, often with computer software. Analysis converts raw data into meaningful findings.

8. Interpretation of results and testing of hypotheses The analysed results are interpreted in the light of the objectives and hypotheses. The researcher explains what the findings mean, whether the hypothesis is supported, and how the results relate to earlier studies.

9. Drawing conclusions and generalizations On the basis of interpretation, conclusions are drawn and, where possible, generalised to similar situations. Limitations of the study are also stated honestly.

10. Preparation and reporting of the research report Finally, the whole work is written up in a standard format — introduction, literature review, methodology, results, discussion, conclusion and references — and communicated through a thesis, paper or presentation.

Conclusion

Thus, the scientific research process is a step-by-step, self-correcting system that begins with a problem and ends with a verified conclusion and report. Each step is linked to the next, and together they ensure that research is objective, reliable and replicable.

Research Hypothesis

A research hypothesis is a tentative, testable statement or prediction about the relationship between two or more variables that the researcher proposes to investigate. It is an educated guess based on existing theory, observation, or prior research, which is either accepted or rejected on the basis of empirical evidence collected during the study. In other words, it is a declarative statement that predicts what the researcher expects to find, and it serves as a bridge between the research problem and the data collection and analysis process.

For example: "There is a significant relationship between the level of education and the income of individuals."

A hypothesis may be stated in two forms:

• Null hypothesis (H₀): A statement of no difference or no relationship between variables, e.g., H₀: μ₁ = μ₂.
• Alternative hypothesis (H₁ or Hₐ): A statement that contradicts the null hypothesis and represents the researcher's actual prediction, e.g., H₁: μ₁ ≠ μ₂.

Characteristics of a Good Research Hypothesis

A good research hypothesis should possess the following characteristics:

1. Testability (Verifiability): It must be capable of being tested empirically through observation or experimentation. Statements that cannot be measured or tested are not scientific hypotheses.
2. Clarity and Precision: It should be clear, specific, and unambiguous, using well-defined terms and variables so that it can be understood and tested consistently.
3. Simplicity (Parsimony): It should be as simple as possible while explaining the phenomenon adequately. A simpler hypothesis is preferred over a complex one.
4. Relevance to the Problem: It must be directly related to the research problem and objectives, and should be consistent with the existing body of knowledge.
5. Objectivity: It should be free from personal bias, values, and subjective judgments of the researcher.
6. Consistency with Known Facts: It should not contradict established theories, laws, or verified facts; rather, it should be grounded in them.
7. Specificity of Variables: It should clearly identify and define the variables involved and the expected relationship between them.
8. Falsifiability: It must be capable of being proven false. A hypothesis that cannot possibly be refuted is not scientifically useful.
9. Brief and Concise: It should be stated in as few words as possible without losing meaning.
10. Predictive Power: It should be able to predict future outcomes or relationships beyond the immediate data.

Procedure for Testing a Hypothesis

Hypothesis testing is a formal statistical procedure used to decide whether to accept or reject the null hypothesis. The procedure involves the following steps:

1. Formulation of the Hypothesis State the null hypothesis (H₀) and the alternative hypothesis (H₁) clearly. For example: $H_0: \mu_1 = \mu_2 \quad \text{(no difference)}$ $H_1: \mu_1 \neq \mu_2 \quad \text{(there is a difference)}$

2. Selection of the Significance Level (α) Choose the level of significance, commonly 0.05 (5%) or 0.01 (1%). This defines the probability of rejecting a true null hypothesis (Type I error).

3. Selection of the Appropriate Test Statistic Choose the test statistic based on the nature of data and sample size — e.g., Z-test (large sample), t-test (small sample), chi-square test (categorical data), or F-test (comparison of variances).

4. Determination of the Critical Region Identify the critical value(s) from the relevant statistical table (Z, t, χ², F) corresponding to the chosen α. The critical region is the set of values that leads to rejection of H₀. It may be:

• Two-tailed (H₁: ≠)
• Right-tailed (H₁: >)
• Left-tailed (H₁: <)

5. Computation of the Test Statistic Calculate the value of the test statistic using the sample data and the appropriate formula. For example, for a large-sample mean test: $Z = \frac{\bar{X} - \mu}{\sigma / \sqrt{n}}$

6. Comparison and Decision Compare the computed value with the critical value:

• If the computed value falls in the critical region, reject H₀.
• If it falls in the acceptance region, do not reject H₀.

Alternatively, using the p-value approach, reject H₀ if p ≤ α.

7. Drawing the Conclusion Interpret the statistical decision in the context of the research problem. State whether the evidence supports the research hypothesis or not, and discuss its theoretical or practical implications.

Note: Rejecting H₀ provides support for H₁; failing to reject H₀ does not prove H₀ is true — it only indicates insufficient evidence against it.

Review of Literature

Review of literature is a systematic, critical and comprehensive survey of the existing published (and sometimes unpublished) body of knowledge on a particular topic or research problem. It involves locating, reading, evaluating and summarising the relevant studies, theories, reports, books, journal articles and other sources, and then organising them into a coherent account that shows what is already known, what is not known, and how the proposed study fits into that body of knowledge. It is not merely a list or summary of sources; it is an analytical synthesis that interprets the literature in relation to the researcher's own objectives.

In a research work, the review of literature is carried out mainly at two stages: first, during the formulation of the research problem (to identify a gap and sharpen the objectives), and second, in the final report (as a separate chapter that provides the theoretical and empirical background of the study).

Important Aspects of Review of Literature in a Research Work

1. Identification of the research gap The review reveals what has already been studied and what remains unexplored. By comparing the findings, methods and populations of earlier studies, the researcher locates inconsistencies, unanswered questions or neglected areas. This gap justifies the need for the present study and establishes its originality.

2. Sharpening and refining the research problem Reading the literature helps the researcher narrow a broad idea into a precise, researchable problem. It clarifies the variables, the scope and the boundaries of the study, and prevents the researcher from repeating work that has already been done.

3. Providing theoretical and conceptual framework The review introduces the theories, models and concepts relevant to the topic. It helps the researcher define key terms operationally, choose an appropriate theoretical lens, and interpret the findings within an established framework.

4. Formulation of hypotheses and research questions Earlier findings and theoretical propositions suggest the direction of expected relationships among variables. These form the basis for stating testable hypotheses or guiding research questions.

5. Selection of appropriate methodology The review shows which research designs, sampling techniques, instruments and statistical tools have been used by previous researchers, along with their strengths and limitations. This guides the researcher in choosing a suitable design and in avoiding the methodological weaknesses of earlier work.

6. Avoiding duplication of effort By knowing what has already been done, the researcher avoids wasting time, money and resources on repeating the same study. Genuine contribution is possible only when the researcher knows the existing boundary of knowledge.

7. Interpretation and comparison of results After data analysis, the findings must be discussed in relation to previous studies — whether they support, contradict or extend them. The review provides the reference point for such discussion and gives meaning to the results.

8. Identification of variables and research instruments The review helps in identifying independent, dependent, intervening and control variables, and in locating standardised tools, scales or questionnaires that can be adopted or adapted for the present study.

9. Development of critical and analytical skill Preparing a review trains the researcher to read critically, judge the quality of evidence, distinguish reliable from weak sources, and synthesise diverse findings rather than merely summarising them.

10. Justification of the study and its significance A well-documented review demonstrates to examiners, funding agencies and readers that the researcher is familiar with the field and that the study is worth undertaking. It thereby strengthens the rationale, credibility and academic value of the research.

Conclusion

Review of literature is thus the foundation on which the entire research work rests. It identifies the gap, frames the problem and hypotheses, supplies the theoretical base, guides the methodology, and provides the standard against which findings are interpreted. A research work without a proper review of literature lacks direction, originality and academic credibility.

Case Study Method

Meaning of Case Study

A case study is a research method in which a single person, group, institution, event, or community is studied in depth and in its natural setting over a period of time. The researcher collects detailed information about the "case" using several techniques — observation, interview, questionnaire, records and documents — and then analyses it to understand the case as a whole.

According to Goode and Hatt, "Case study is a way of organizing social data so as to preserve the unitary character of the social object being studied."

According to P.V. Young, "A case study is a comprehensive study of a social unit — a person, a family, a group, an institution or a community — in which the investigator studies the whole unit in all its aspects."

So a case study is intensive, qualitative and holistic rather than extensive. It studies one unit deeply instead of studying many units superficially. For example, studying one school, one patient, one village, or one business firm in detail is a case study.

Characteristics

1. It studies a single unit (individual, family, group, institution or community).
2. It is intensive and in-depth, not extensive.
3. It studies the unit as a whole, in its total setting.
4. It uses several methods of data collection together.
5. It is flexible — the researcher can change the plan as new facts appear.
6. It is more qualitative than quantitative, though quantitative data may also be used.
7. It is often longitudinal, i.e. the case is studied over a long period.

How the Case Study Method Helps in Research Work

1. Deep and complete understanding. Since only one unit is studied, the researcher can go into every aspect — social, economic, cultural, psychological — and understand the case fully. This depth is not possible in a survey covering thousands of respondents.

2. Study of rare and unique cases. Some events or conditions occur rarely (a rare disease, a unique social movement, a single exceptional student). Such cases cannot be studied by statistical methods; only the case study method can handle them.

3. Useful where the unit cannot be separated from its setting. Human behaviour has meaning only in its natural context. Case study keeps the unit in its real-life setting and therefore gives more valid data.

4. Basis for hypothesis formulation. Careful study of a few cases brings out patterns and relationships which can later be tested on a large sample. Many important hypotheses in sociology, psychology and medicine began as case studies.

5. Helps in intensive diagnosis. In clinical psychology, social work and education, the case study is used to diagnose the real problem of an individual (e.g. why a child is failing) and to suggest treatment or remedy.

6. Useful for studying change over time. Because the same case is followed for a long period, the researcher can see how behaviour, attitudes or conditions develop and change — something a one-time survey cannot show.

7. Provides illustrative and vivid material. Case study data are concrete and descriptive. They make the research report interesting and help in explaining abstract findings through real examples.

8. Useful for pilot study. Before a large survey, a few case studies help the researcher to understand the field, frame proper questions and decide the variables to be measured.

9. Helps in studying causal processes. By tracing the full history of a case, the researcher can see how one factor led to another, which is difficult to establish in a large-scale statistical study.

Limitations (for balance)

• Findings are based on one unit, so they cannot be generalized to the whole population.
• It is time-consuming and costly.
• The researcher's personal bias may enter into the interpretation.
• It depends heavily on the memory and honesty of the informants.
• Statistical analysis and verification are difficult.

Conclusion

The case study method is an intensive, holistic and flexible approach that studies a single social unit in its natural setting. It gives deep insight, helps in forming hypotheses, is useful in diagnosis and in studying rare cases and processes of change. Though its results cannot be generalized, it is an important and widely used method in social, psychological, educational and medical research.

Reliability and Validity

Reliability refers to the consistency, stability, and dependability of a test or measuring instrument. A test is reliable if it gives approximately the same results when administered repeatedly to the same group under similar conditions, or when equivalent forms of the test are used. It is concerned with the question: “Does the test measure consistently?”

Validity refers to the extent to which a test measures what it is intended or claims to measure. A test is valid if it accurately measures the intended trait, skill, or attribute. It is concerned with the question: “Does the test measure the right thing?”

Importance in Measurement of Data

Importance of reliability:

1. It ensures consistency and reproducibility of scores.
2. It reduces measurement error and increases dependability of data.
3. It is necessary for making fair comparisons between individuals or groups.
4. It is a prerequisite for validity; a test cannot be valid unless it is reliable.
5. It helps in selecting, evaluating, and improving tests and research tools.

Importance of validity:

1. It ensures that the test measures the intended objective or construct.
2. It makes the interpretation and use of scores meaningful and accurate.
3. It prevents wrong decisions in education, selection, and research.
4. It increases the credibility of research findings and evaluation results.
5. It helps in proper curriculum planning, diagnosis, and guidance.

Thus, reliability and validity are essential psychometric qualities of any measurement tool. Reliability is necessary but not sufficient for validity.

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Numerical: Reliability Coefficient

Given:

• Number of items, (k = 20)
• Mean of the test, (M = 12.6)
• Standard deviation, (\sigma = 2.3)
• Variance, (\sigma^2 = (2.3)^2 = 5.29)

Since the items are scored as 1 or 0, the Kuder–Richardson Formula 21 (KR-21) is used:

[ r_{tt} = \frac{k}{k-1} \left[ 1 - \frac{M(k-M)}{k \sigma^2} \right] ]

Substituting the values:

[ r_{tt} = \frac{20}{19} \left[ 1 - \frac{12.6(20-12.6)}{20 \times 5.29} \right] ]

[ = \frac{20}{19} \left[ 1 - \frac{12.6 \times 7.4}{105.8} \right] ]

[ = \frac{20}{19} \left[ 1 - \frac{93.24}{105.8} \right] ]

[ = \frac{20}{19} [1 - 0.8813] ]

[ = \frac{20}{19} \times 0.1187 ]

[ = 0.125 \text{ (approximately)} ]

Result:
The reliability coefficient of the test is approximately 0.125 or 0.13.

Since the reliability coefficient is very low, the test has low internal consistency reliability.

Sampling is the statistical process of selecting a subset (called a sample) of individuals, items, or observations from a larger population, in order to study the characteristics of the whole population. Since studying every member of a population (a census) is often costly, time-consuming, or impossible, we study a representative sample and use inferential statistics to draw conclusions about the population.

Key terms:

• Population (N): The entire group under study.
• Sample (n): The selected subset.
• Sampling frame: The list of all units from which the sample is drawn.
• Sampling unit: A single member of the population.
• Sampling error: The difference between a sample statistic and the true population parameter.

A good sample must be representative, unbiased, and of adequate size.

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Types of Sampling Techniques

Sampling techniques are broadly classified into two groups:

A. Probability (Random) Sampling

Every unit of the population has a known, non-zero probability of being selected. Selection is by chance, so bias is minimized.

1. Simple Random Sampling (SRS) Every unit has an equal chance of selection. Units are chosen by lottery method or random number tables.

Example: From 500 students of a college, 50 are selected by drawing chits or using a random number generator.

2. Systematic Sampling The population is arranged in order; a random start is made and then every k-th unit is selected, where k = N/n.

Example: To select 100 houses from 1,000 houses in a locality, take k = 1000/100 = 10. Start at a random house, say the 7th, then select 7, 17, 27, 37, …

3. Stratified Sampling The population is divided into homogeneous groups called strata (by sex, caste, income, etc.), and a random sample is drawn from each stratum. It may be proportional or equal allocation.

Example: A college has 600 boys and 400 girls. For a sample of 100, select 60 boys and 40 girls randomly (proportional allocation).

4. Cluster Sampling The population is divided into groups called clusters (usually geographical), and whole clusters are randomly selected and studied.

Example: To survey school children in Kathmandu, randomly select 10 schools (clusters) and interview all students of those schools.

5. Multistage Sampling Sampling is done in successive stages — first large units, then smaller units within them.

Example: Select districts → then wards → then households → then one member per household.

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B. Non-Probability (Non-Random) Sampling

The chance of selection of a unit is not known; selection depends on the investigator's judgement or convenience. It is cheaper and quicker but may be biased.

1. Convenience Sampling Units are selected because they are easily available.

Example: A researcher interviews shoppers in a nearby supermarket.

2. Purposive (Judgement) Sampling The investigator deliberately selects units believed to be representative.

Example: Selecting 20 expert economists to forecast inflation.

3. Quota Sampling The population is divided into groups and a fixed number (quota) is filled from each group by the interviewer's choice.

Example: An interviewer fills a quota of 50 males and 50 females in a market survey.

4. Snowball Sampling Existing respondents refer further respondents; useful for hidden populations.

Example: Studying drug users by asking one user to refer others.

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Comparison

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Conclusion

Sampling saves time, money, and effort while giving reliable estimates of population parameters. Among the techniques, simple random sampling is the simplest and most unbiased, while stratified sampling gives greater precision when the population is heterogeneous. The choice of technique depends on the nature of the population, the objective of the study, and available resources.

Questionnaire vs. Schedule

A questionnaire is a set of written questions sent to respondents (by post, e-mail, or handed over) which they fill in themselves. A schedule is a set of questions filled in by the enumerator (investigator) himself in the presence of the respondent, recording the replies as they are given.

Kinds of Schedule

1. Structured schedule — All questions and the possible answers are pre-determined and fixed. The enumerator only ticks or records the reply. Used in large-scale surveys where uniformity is needed.
2. Unstructured schedule — Only broad topics or guiding questions are given; the enumerator is free to probe and frame questions as the interview proceeds. Used in exploratory or intensive studies.
3. Non-structured (open) schedule — Questions are open-ended and the respondent answers in his own words; no fixed alternatives are supplied.
4. Rating schedule — Used to record qualitative judgements such as attitudes, opinions, or preferences, usually on a scale (e.g., strongly agree to strongly disagree).
5. Documentary schedule — Used to collect information from records, registers, or documents rather than from persons directly.
6. Observation schedule — Used when the enumerator records what he observes rather than what the respondent states.
7. Village or community schedule — A schedule administered to a group, village, or institution to collect group-level information.

Contents of a Schedule

1. Identifying particulars — Name, address, age, sex, caste/ethnicity, occupation, and other identification details of the respondent.
2. Introduction and purpose — A brief statement of who is conducting the study and why, to win the respondent's confidence.
3. Instructions to the enumerator — Clear directions on how to ask questions, record answers, skip questions, and handle difficult situations.
4. Main body of questions — The actual questions arranged in a logical sequence — from general to specific, simple to complex, and personal questions placed at the end.
5. Response categories — Pre-coded alternatives or space for open replies, with codes for easy tabulation.
6. Cross-check or control questions — Questions to verify the consistency and truthfulness of earlier replies.
7. Classification and coding section — Space for the enumerator or editor to classify, code, and edit the schedule after collection.
8. Space for remarks — For the enumerator's own observations about the interview situation.
9. Signature and date — Signature of the enumerator and the date of the interview for record and verification.

Z-test

A Z-test is a statistical test of hypothesis in which the test statistic follows the standard normal distribution (Z-distribution) when the null hypothesis is true. It is used to decide whether to accept or reject a null hypothesis by comparing the computed Z-value with the critical value of Z at a chosen level of significance.

$Z = \frac{\text{Statistic} - \text{Hypothesized parameter}}{\text{Standard error of the statistic}}$

When and why it is used

The Z-test is used when:

1. The sample size is large ($n \ge 30$), so the sampling distribution is approximately normal by the Central Limit Theorem.
2. The population standard deviation $\sigma$ is known.
3. The population is normally distributed (or approximately so).

Purposes:

• To test a hypothesis about a population mean (one sample or two samples).
• To test a hypothesis about a population proportion.
• To test the difference between two means or two proportions.
• To construct confidence intervals for large samples.

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Numerical

Given:

• Population mean under $H_0$: $\mu_0 = 110$
• Population standard deviation: $\sigma = 10$
• Sample size: $n = 50$
• Sample mean: $\bar{x} = 113.5$
• Level of significance: $\alpha = 0.05$ (one-tailed, right)

Hypotheses:

$H_0 : \mu = 110 \quad \text{against} \quad H_1 : \mu > 110$

Test statistic:

$Z = \frac{\bar{x} - \mu_0}{\sigma/\sqrt{n}}$

Substitution:

$Z = \frac{113.5 - 110}{10/\sqrt{50}} = \frac{3.5}{10/7.0711} = \frac{3.5}{1.4142}$

$Z = 2.475$

Critical value:

For a one-tailed test at $\alpha = 0.05$,

$Z_{0.05} = 1.645$

Decision:

Since the calculated value

$Z = 2.475 > Z_{0.05} = 1.645$

the computed value falls in the critical (rejection) region.

Conclusion:

We reject $H_0$ at the 5% level of significance. There is sufficient evidence to conclude that the population mean IQ score is greater than 110.

Preparing and Presenting a Research Proposal

A research proposal is a written document that describes a planned study and requests approval or funding for it. It tells the reader what you will study, why it matters, how you will do it, and what you expect to find. A good proposal is clear, specific, feasible, and logically organized.

Part A: How a Research Proposal Should Be Prepared

1. Preliminary Steps (before writing)

• Select a topic that is specific, researchable, and within your capacity and resources.
• Review the literature (books, journals, theses, reports) to find what is already known and where the gap lies.
• Define the problem precisely — narrow it from a broad area to a focused question.
• Consult the supervisor and finalize the title and objectives.

2. Structure and Content of the Proposal

2.1 Key Points in Methodology

• Research design: descriptive, exploratory, experimental, case study, etc.
• Population and sample: universe, sample size (with justification), and sampling method (random, stratified, purposive).
• Tools: questionnaire, interview, observation, focus group discussion, document analysis.
• Analysis: descriptive statistics (mean, percentage, tables, graphs) and inferential statistics (t-test, chi-square, correlation) for quantitative data; thematic analysis for qualitative data.

3. Qualities of a Good Proposal

• Clear and specific title and objectives
• Logical flow from problem to method to expected outcome
• Feasible in terms of time, money, and skill
• Free from bias; ethically sound
• Properly referenced and neatly formatted

Part B: How a Research Proposal Should Be Presented

1. Written Presentation

• Type the proposal on A4 paper with 1.5 line spacing, standard margins, and a consistent font (e.g., Times New Roman 12 pt).
• Include a title page, table of contents, and page numbers.
• Use headings and subheadings to make the structure visible.
• Present data collection tools and the work plan in tables and figures.
• Follow the referencing style prescribed by the institution.

2. Oral Presentation (Proposal Defense / Seminar)

• Prepare slides (10–15) covering title, problem, objectives, methodology, expected outcomes, and timeline.
• Keep each slide brief — bullet points, not paragraphs.
• Rehearse the presentation and stay within the allotted time.
• Speak clearly, maintain eye contact, and use simple language.
• Be ready to answer questions from the supervisor and evaluation committee.
• Accept suggestions positively and revise the proposal accordingly.

3. Common Mistakes to Avoid

• Topic too broad or vague
• Objectives not measurable
• Methodology inconsistent with objectives
• No literature review or poor referencing
• Unrealistic budget or timeline
• Overcrowded slides and reading from slides during defense

Conclusion

A research proposal should be prepared systematically — from topic selection and literature review through a well-defined problem, clear objectives, and a sound methodology — and presented both in a neat written format and as a confident oral defense. A well-prepared and well-presented proposal not only secures approval but also serves as a reliable roadmap for the entire research work.

Research Report

Meaning

A research report is a formal, written document that presents the findings of a research study in an organised and systematic manner. It communicates the objectives, methodology, data, analysis, conclusions and recommendations of the research to the readers. It is the final output of the research process and serves as a permanent record of the study.

According to Kerlinger, "A research report is a written document that presents the methods, findings and conclusions of a research study."

Procedure of Writing a Research Report

The writing of a research report generally follows these steps:

1. Planning the Report Before writing, the researcher decides the type of report (technical or popular), the target readers, the scope, and the overall structure. A clear outline is prepared to give direction to the writing.

2. Collection and Organisation of Material All the materials collected during the study — notes, questionnaires, interview schedules, statistical tables, and references — are gathered and arranged in a logical order according to the objectives of the study.

3. Preparation of the Outline (Outline of the Report) A detailed outline is prepared showing the main sections and sub-sections. This ensures that no important point is left out and that the report has a proper flow.

4. Writing the First Draft The researcher writes the first draft covering all the sections — introduction, literature review, methodology, results, discussion and conclusion. At this stage, emphasis is given to content rather than style.

5. Revision and Editing The first draft is carefully revised. Errors of grammar, spelling, punctuation and style are corrected. Ambiguous statements are removed, and the language is made simple, clear and objective.

6. Preparation of Tables and Figures Data are presented in well-labelled tables, graphs and charts so that the findings can be understood easily. Each table and figure is given a number and a proper title.

7. Writing the Final Draft After revision, the final draft is prepared incorporating all corrections and suggestions. The report is then typed or printed neatly in the prescribed format.

8. Preparation of Bibliography and Appendices References used in the study are listed alphabetically in the bibliography. Questionnaires, schedules and other supporting materials are placed in the appendices.

9. Final Typing, Binding and Submission The report is finally typed, proofread, bound and submitted to the concerned authority or institution within the stipulated time.

Structure of a Research Report

A standard research report contains the following parts:

• Preliminary section: Title page, certificate, acknowledgement, table of contents, list of tables and figures.
• Main body: Introduction, objectives, literature review, research methodology, data presentation and analysis, findings, conclusion and recommendations.
• Reference section: Bibliography and appendices.

Conclusion

Thus, a research report is the systematic and formal presentation of research findings. Following the above procedure step by step ensures that the report is accurate, clear, complete and useful for future researchers and decision-makers.

Score:
A score is a numerical value assigned to an individual’s performance on a test, examination, or measurement. It may be a raw score (original marks or number of correct responses) or a derived score (percentage, percentile rank, standard score, etc.).

Transformation of a score to a percentage scale:
If an individual obtains raw score (X) out of maximum total marks (M), then

[ \text{Percentage score}=\frac{X}{M}\times 100 ]

Example: If a student scores 45 out of 60,

[ \text{Percentage}=\frac{45}{60}\times 100=75% ]

So a raw score is transformed to the 0–100 percentage scale by dividing the obtained score by the maximum possible score and multiplying by 100.

Percentile rank:
The percentile rank of a score is the percentage of individuals in the group who score below that score, plus half of those who score equal to it. The formula is

[ PR=\frac{B+0.5E}{N}\times 100 ]

where
(B) = number of individuals scoring below the given score,
(E) = number of individuals scoring equal to the given score,
(N) = total number of individuals.

For ranked data, if (R) is the rank from the top,

[ PR=\frac{N-R+0.5}{N}\times 100 ]

Given:

[ N=6 ]

Ranks:

[ A=6,\ B=4,\ C=2,\ D=1,\ E=3,\ F=5 ]

Here rank 1 is the highest score. So the order from highest to lowest is:

[ D,\ C,\ E,\ B,\ F,\ A ]

Percentile rank of B:
Rank of B (=4).
Number of individuals below B (=2) (F and A).
Number equal to B (=1).

[ PR_B=\frac{2+0.5}{6}\times 100 ]

[ PR_B=\frac{2.5}{6}\times 100=41.67 ]

Percentile rank of E:
Rank of E (=3).
Number of individuals below E (=3) (B, F and A).
Number equal to E (=1).

[ PR_E=\frac{3+0.5}{6}\times 100 ]

[ PR_E=\frac{3.5}{6}\times 100=58.33 ]

Answer:

[ \boxed{PR_B=41.67,\qquad PR_E=58.33} ]

So B is above about (41.67%) of the group, and E is above about (58.33%) of the group.

Reference Citing

Reference citing is the practice of formally acknowledging, within the text and at the end of a piece of academic writing, the sources of the ideas, data, quotations or methods that the writer has borrowed from other authors. The in‑text mention is called a citation, and the complete list of sources given at the end is called a reference list (or, when it also includes works consulted but not cited, a bibliography).

Its purposes are:

1. To give credit to the original author and thereby avoid plagiarism.
2. To allow verification — a reader can trace the source and check the facts.
3. To show the depth and quality of the reading behind the work.
4. To help the reader locate further literature on the topic.
5. To maintain academic honesty and the ethics of research.

A complete reference normally answers four questions: Who wrote it (author), When (year), What (title), and Where it can be found (journal, publisher, volume, pages, DOI/URL).

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Approaches Used in Citing Journals, Books and Articles

Two broad families of citation systems exist:

The three common source types are cited as follows.

1. Journal Papers

A journal reference must give author(s), year, title of the paper, name of the journal, volume, issue, page range and DOI.

APA (author–date):

Sharma, R., & Thapa, K. (2021). Effect of nitrogen on rice yield in Chitwan. Nepalese Journal of Agriculture, 12(3), 45–53. https://doi.org/10.3126/nja.v12i3.4567

In text: (Sharma & Thapa, 2021) or Sharma and Thapa (2021) reported …

MLA (author–page):

Sharma, Rajesh, and Kamal Thapa. "Effect of Nitrogen on Rice Yield in Chitwan." Nepalese Journal of Agriculture, vol. 12, no. 3, 2021, pp. 45–53.

In text: (Sharma and Thapa 47).

Chicago (notes–bibliography):

¹Rajesh Sharma and Kamal Thapa, "Effect of Nitrogen on Rice Yield in Chitwan," Nepalese Journal of Agriculture 12, no. 3 (2021): 47.

IEEE (numeric):

[1] R. Sharma and K. Thapa, "Effect of nitrogen on rice yield in Chitwan," Nepalese J. Agric., vol. 12, no. 3, pp. 45–53, 2021.

2. Books

A book reference needs author(s), year, title (italicised), edition, publisher and place of publication.

APA:

Kandel, B. P. (2019). Research methodology in science (2nd ed.). Ekta Books.

In text: (Kandel, 2019, p. 88).

MLA:

Kandel, Bishnu Prasad. Research Methodology in Science. 2nd ed., Ekta Books, 2019.

Chicago:

Kandel, Bishnu Prasad. Research Methodology in Science. 2nd ed. Kathmandu: Ekta Books, 2019.

IEEE:

[2] B. P. Kandel, Research Methodology in Science, 2nd ed. Kathmandu, Nepal: Ekta Books, 2019.

3. Articles (chapters in edited books, conference papers, newspaper and web articles)

Here the title of the article is placed in quotation marks and the title of the containing work is italicised, together with editor, page range and publisher.

APA (chapter in an edited book):

Adhikari, S. (2020). Climate change adaptation in hill farming. In P. R. Joshi & M. Lama (Eds.), Sustainable agriculture in Nepal (pp. 112–130). Himal Books.

MLA:

Adhikari, Suman. "Climate Change Adaptation in Hill Farming." Sustainable Agriculture in Nepal, edited by Prakash R. Joshi and Maya Lama, Himal Books, 2020, pp. 112–30.

Chicago:

Adhikari, Suman. "Climate Change Adaptation in Hill Farming." In Sustainable Agriculture in Nepal, edited by Prakash R. Joshi and Maya Lama, 112–30. Kathmandu: Himal Books, 2020.

Conference paper (IEEE):

[3] S. Adhikari, "Climate change adaptation in hill farming," in Proc. Int. Conf. Sustainable Agriculture, Kathmandu, Nepal, 2020, pp. 112–130.

Newspaper / web article:

Bhattarai, R. (2022, June 5). Monsoon arrives early in Nepal. The Kathmandu Post. https://kathmandupost.com/...

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Points to Remember

• Consistency is essential — one style must be followed throughout the document; mixing APA and IEEE is a serious fault.
• In‑text citation and reference list must match one‑to‑one; every citation must appear in the list and vice versa.
• Italicise journal names, book titles and volume numbers; do not italicise article titles.
• Use et al. for three or more authors (APA 7th) or as the chosen style prescribes.
• Give DOI for journal papers where available, and access date for web sources.
• Reference‑management software such as Zotero, Mendeley or EndNote can automate the formatting.

In short, reference citing is the systematic acknowledgement of sources, and the different approaches — author–date (APA, Harvard, MLA, Chicago author–date) and numeric/note (IEEE, Vancouver, Chicago notes) — differ mainly in how the in‑text marker is written and how the entries are ordered, while the bibliographic details required for journals, books and articles remain essentially the same.