Scientific Research Process

Scientific research is a systematic, objective and logical process of inquiry in which a researcher identifies a problem, formulates a hypothesis, collects and analyses relevant data, and draws valid conclusions. It is "scientific" because it follows a defined, replicable and self-correcting sequence of steps rather than relying on intuition or guesswork. The scientific research process is therefore the orderly series of stages through which a research study passes from the identification of a problem to the reporting of findings.

Steps Involved in the Scientific Research Process

1. Identification and Formulation of the Research Problem The first step is to select and clearly define the problem to be studied. The researcher identifies a gap in existing knowledge, narrows it into a specific, researchable question, and states the objectives of the study. A well-formulated problem specifies what is to be studied, where, when and among whom.

2. Review of Literature The researcher surveys books, journals, reports, theses and other sources related to the problem. This helps in understanding what has already been done, avoiding duplication, identifying gaps, refining the research question and developing a sound theoretical base.

3. Formulation of Hypothesis A hypothesis is a tentative, testable statement about the relationship between variables. It gives direction to the study and provides the basis for accepting or rejecting assumptions. For example: "Higher family income leads to higher expenditure on education."

4. Preparation of Research Design The research design is the blueprint of the study. It decides the type of research (descriptive, exploratory, causal), the population and sampling method, the sources of data (primary/secondary), the tools of data collection (questionnaire, interview, observation) and the techniques of analysis. A good design ensures economy, reliability and validity.

5. Sampling and Data Collection The researcher selects a representative sample from the population using probability or non-probability sampling. Data are then collected through surveys, interviews, questionnaires, observation or experiments. Care is taken to ensure accuracy, objectivity and freedom from bias.

6. Processing and Analysis of Data Collected data are edited, coded, classified and tabulated. Statistical tools such as averages, percentages, correlation, regression, chi-square and t-test are applied to test the hypothesis and bring out relationships and patterns.

7. Interpretation of Results The analysed results are interpreted in the light of the objectives and hypothesis. The researcher explains what the findings mean, whether the hypothesis is accepted or rejected, and how the results relate to existing knowledge.

8. Drawing Conclusions and Generalisation On the basis of interpretation, conclusions are drawn and generalisations made within the limits of the study. Limitations and scope for further research are also stated.

9. Preparation and Presentation of the Research Report Finally, the findings are compiled into a formal report containing introduction, objectives, methodology, findings, conclusions and recommendations. The report is presented in a clear, logical and standard format for academic or practical use.

Conclusion

The scientific research process is thus a continuous, cyclical sequence — problem → literature → hypothesis → design → data collection → analysis → interpretation → conclusion → report. Each step logically follows the previous one, and the whole process is guided by the principles of objectivity, systematic inquiry and verifiability, which together distinguish scientific research from ordinary inquiry.

Research Hypothesis

A research hypothesis is a tentative, testable statement or prediction about the relationship between two or more variables that the researcher intends to verify through empirical investigation. It is an educated guess based on existing theory, observation, or prior research, which is either accepted or rejected on the basis of sample data. For example: "There is a significant relationship between the level of education and the income of individuals."

In statistical terms, the hypothesis is stated in two forms:

• Null hypothesis ($H_0$): a statement of no difference or no relationship, e.g. $H_0: \mu_1 = \mu_2$.
• Alternative hypothesis ($H_1$ or $H_a$): a statement that contradicts $H_0$, e.g. $H_1: \mu_1 \neq \mu_2$.

Characteristics of a Good Research Hypothesis

1. Testable (Verifiable): It must be capable of being tested empirically through data. Statements that cannot be measured or tested are not useful hypotheses.
2. Clear and Precise: It should be stated in unambiguous, specific terms so that variables and relationships are clearly understood.
3. Specific and Limited in Scope: A good hypothesis is narrow enough to be investigated within the available time, resources, and data.
4. Consistent with Existing Knowledge: It should be based on established theory, previous research, or logical reasoning, not on mere speculation.
5. States a Relationship between Variables: It should express a definite relationship (positive, negative, or difference) between the independent and dependent variables.
6. Simple and Concise: It should be stated as briefly and simply as possible without losing meaning.
7. Objectively Stated: It must be free from personal bias, values, or subjective opinion of the researcher.
8. Falsifiable: It must be possible to prove it wrong; a hypothesis that cannot be disproved is not scientific.
9. Operationally Defined: The variables in the hypothesis should be defined in measurable, operational terms.
10. Economical: It should be answerable with available resources and should not demand excessive cost or effort.

Procedure for Testing a Hypothesis

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

1. Formulate the hypotheses. State the null hypothesis ($H_0$) and the alternative hypothesis ($H_1$), either in directional (one-tailed) or non-directional (two-tailed) form.

2. Select the level of significance ($\alpha$). Choose the probability of rejecting a true $H_0$ (Type I error). Commonly $\alpha = 0.05$ or $0.01$.

3. Choose the appropriate test statistic. Select the test based on the nature of data and sample size — $z$-test (large sample, known $\sigma$), $t$-test (small sample, unknown $\sigma$), $\chi^2$-test (categorical data), or $F$-test (comparison of variances).

4. Determine the critical region. Find the critical value(s) from the relevant statistical table using $\alpha$ and the degrees of freedom. The critical region defines the values of the test statistic for which $H_0$ is rejected.

5. Compute the value of the test statistic. Using the sample data, calculate the value of the chosen statistic. For example, for a large-sample mean test:

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

6. Compare the computed value with the critical value. If the computed value falls in the critical region (or if $p$-value $< \alpha$), reject $H_0$; otherwise, do not reject $H_0$.

7. Draw the conclusion. Interpret the statistical decision in the context of the research problem, stating whether the sample evidence supports or fails to support the hypothesis.

Note: Rejection of $H_0$ provides evidence in favour of $H_1$; failure to reject $H_0$ does not prove that $H_0$ is true — it only means the sample data do not provide sufficient evidence against it.

Review of Literature

Review of literature is a systematic, critical and comprehensive examination of the existing body of knowledge — books, journal articles, theses, dissertations, government reports, and other published or unpublished sources — that is relevant to a particular research problem. It involves locating, reading, evaluating and summarising what earlier researchers and scholars have already written on the topic, and then organising that material so that it clarifies what is known, what is disputed, and what remains unexamined. It is not merely a list or summary of sources; it is an analytical synthesis that builds the intellectual foundation on which the present study is constructed. In a research work, the review appears as a separate chapter (usually Chapter 2) placed after the introduction and before the methodology.

Important Aspects of Review of Literature in a Research Work

1. Identification of the research gap The most important function of a literature review is to reveal what has not yet been studied. By mapping the existing studies, the researcher can see which questions have been answered and which have been neglected, and thereby locate the gap that the present study intends to fill. Without this, the research may simply repeat work already done.

2. Providing theoretical and conceptual framework The review supplies the theories, models and concepts on which the study rests. For example, a study on consumer behaviour may draw on the theory of planned behaviour; a study on capital structure may rest on Modigliani–Miller propositions. It also helps the researcher define key variables and concepts precisely, so that they are used consistently throughout the study.

3. Clarifying and refining the research problem Reading earlier works helps the researcher sharpen a vague idea into a clear, specific and researchable problem. It shows which aspects of a broad topic are worth investigating and which are too wide or already exhausted.

4. Formulation of objectives, hypotheses and research questions The findings and conclusions of previous studies suggest what results may reasonably be expected in the present study. This helps the researcher frame testable hypotheses and specific objectives. A hypothesis is rarely invented in a vacuum; it is usually derived from the direction indicated by prior research.

5. Selection of appropriate research methodology The review shows how earlier researchers designed their studies — what research design, population, sample size, sampling technique, data collection instruments and statistical tools they used. The researcher can then adopt, adapt or improve upon those methods, and can also learn from the limitations of earlier studies.

6. Avoiding unnecessary duplication of work By making the researcher aware of what has already been established, the review prevents wasteful repetition of the same study and allows effort to be directed towards new and unexplored areas.

7. Interpretation and comparison of findings When the present study is completed, its results must be compared with those of earlier studies to determine whether they confirm, contradict or extend existing knowledge. The literature reviewed earlier provides the benchmark for this comparison and discussion.

8. Justification of the study's significance A well-documented review demonstrates that the research is timely, relevant and contributes something to the field. This is important for obtaining approval, funding and academic credibility.

9. Broadening the researcher's knowledge It deepens the researcher's understanding of the topic, its history, its theoretical debates and its practical applications, which improves the quality of the entire research process.

10. Identification of sources and experts The review leads the researcher to key journals, databases, research centres and scholars working in the field, which is useful for further reference and collaboration.

Conclusion

Review of literature is therefore not a formality but a core component of research. It identifies the gap, supplies the theoretical base, guides the formulation of objectives and hypotheses, informs methodological choices, prevents duplication, and provides the standard against which the new findings are interpreted. A research work without a proper review lacks direction, originality and academic standing.

Case Study Method

Meaning of Case Study

A case study is a research method in which a single person, group, institution, event, or situation is studied in depth over a period of time. The unit studied is called a "case." 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 for the purpose of viewing social reality." P.V. Young defines it as "a comprehensive study of a social unit — a person, a family, a group, an institution or a community — in which the investigator studies the unit in its totality."

Features of Case Study

1. Intensive study — it studies a few units very deeply rather than many units superficially.
2. Study of a unit as a whole — the case is studied in its total setting, not in isolated parts.
3. Use of multiple methods — observation, interview, records and documents are all used together.
4. Longitudinal nature — the case is often followed over a long period to see change and development.
5. Qualitative in nature — it gives descriptive and explanatory information rather than only numbers.
6. Flexible — the researcher can change questions and direction as new facts appear.

How Case Study Helps in Research Work

(a) Deep and complete understanding. Since one unit is studied in its totality, the researcher gets a full picture of the case — its background, behaviour, causes and effects — which a large survey cannot give.

(b) Useful for exploratory and pilot research. When little is known about a problem, a case study helps to form hypotheses and to design a larger study later. It is often the first step before a survey.

(c) Helps to study rare and unique cases. Events like a bank failure, a riot, a suicide, or the success of one cooperative cannot be studied by sampling. Case study is the only suitable method for such unique cases.

(d) Gives cause-and-effect explanation. By tracing the history of a case, the researcher can see which factors produced which results — for example, why one firm failed while another succeeded.

(e) Provides deep and reliable qualitative data. Direct observation and long contact with the case give rich, first-hand information that is not distorted by memory or by the presence of many respondents.

(f) Helps in theory building and verification. Case findings can suggest new concepts and can also be used to test whether an existing theory holds in a real situation.

(g) Useful in practical and professional fields. In social work, management, medicine, law and education, case studies give lessons from real experience that can guide policy and practice.

(h) Basis for comparison. Several case studies of the same type can be compared to find common patterns — for example, studies of several small enterprises to find why some grow and others do not.

Limitations (brief)

• Findings of one case cannot be generalised to the whole population.
• It is time-consuming and costly.
• Personal bias of the researcher may enter the study.
• It depends heavily on the skill and honesty of the investigator.

Conclusion

A case study is an intensive, total, and multi-method study of a single social unit. It helps research work by giving deep understanding, by explaining causes and effects, by handling unique and rare cases, and by supplying material for hypothesis making and theory testing. Though its results cannot be generalised, it is an indispensable method, especially in the early and explanatory stages of research.

Reliability and Validity

Reliability is the extent to which a measurement instrument gives consistent, stable and dependable results when the same test is repeated under similar conditions. It is concerned with consistency, reproducibility and precision of measurement. For example, if a student scores 80 in a test today, a reliable test should give a similar score if administered again without any real change in ability.

Validity is the extent to which an instrument measures exactly what it is intended to measure. It is concerned with accuracy and truthfulness of measurement. For example, a mathematics test is valid only if it measures mathematical ability, not reading ability or memory.

Importance in Measurement of Data

1. Reliability ensures that data are consistent and free from random errors. It makes results dependable and comparable over time and across groups.
2. Validity ensures that the data actually represent the concept or variable being studied. It prevents wrong conclusions and misleading interpretations.
3. A test may be reliable but not valid; however, a valid test must be reliable. Therefore, both are necessary for good measurement.
4. They increase the credibility, accuracy and generalizability of research findings and help in making correct decisions in education, business and policy.

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Numerical: Reliability Coefficient

Given:

• Number of items, (K = 20)
• Mean score, (M = 12.6)
• Standard deviation, (S = 2.3)
• Variance, (S^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 S^2}\right] ]

Substituting the values:

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

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

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

[ r_{tt} = \frac{20}{19}(1 - 0.8813) ]

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

[ r_{tt} = 0.1249 \approx 0.125 ]

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

Interpretation: A reliability coefficient of 0.125 is very low. Generally, a reliability coefficient above 0.70 is considered acceptable. Therefore, this test has poor internal consistency and is not reliable for measuring student achievement. It needs improvement by revising items, increasing the number of good items and removing ambiguous or defective items.

Sampling

Sampling is the statistical process of selecting a subset (called a sample) of individuals, items, or observations from a larger group (called the population or universe) for the purpose of making inferences about the whole population. Instead of studying every unit in the population (a census), we study a representative part of it. A good sample must be representative, adequate in size, unbiased, and economical in terms of time, cost, and effort.

Types of Sampling Techniques

Sampling techniques are broadly divided into two categories:

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A. Probability (Random) Sampling

In these methods, every unit of the population has a known, non-zero chance of being selected. Selection is based on chance, so the results can be generalized to the population with measurable reliability.

1. Simple Random Sampling Every unit has an equal probability of selection. Units are chosen by lottery method or by using a random number table. Example: To select 50 students from a college of 500, assign each student a number from 001 to 500 and draw 50 numbers using a random number table. The students holding those numbers form the sample.

2. Systematic (Quasi-Random) Sampling The first unit is selected at random, and the remaining units are selected at a fixed interval, ( k = \dfrac{N}{n} ). Example: From a population of 1,000 households, to select 100 households, take ( k = 10 ). If the first household chosen at random is number 7, the sample consists of households 7, 17, 27, 37, … and so on.

3. Stratified Random Sampling The population is divided into homogeneous subgroups called strata (e.g., by sex, income, region), and a random sample is drawn from each stratum. This reduces sampling error and ensures representation of all groups. Example: A researcher studying income levels divides a city into three strata — low, middle and high income — and randomly selects 40 families from each stratum.

4. Cluster Sampling The population is divided into groups called clusters, usually based on geography. A few clusters are selected at random, and all units within the selected clusters are studied. It is cheap and convenient but less precise. Example: To study the health status of schoolchildren in Nepal, a researcher randomly selects 10 schools (clusters) from a district and surveys every child in those schools.

5. Multistage Sampling Sampling is carried out in two or more stages, using a combination of the above methods at each stage. Example: For a national survey, first select districts at random (stage 1), then select villages within those districts (stage 2), and finally select households within those villages (stage 3).

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B. Non-Probability (Non-Random) Sampling

Here the selection of units depends on the judgement, convenience or purpose of the investigator, not on chance. The probability of selection is unknown, so results cannot be strictly generalized.

1. Purposive (Judgement) Sampling The investigator deliberately selects units believed to be most representative or suitable for the study. Example: A researcher studying the problems of street vendors selects only street vendors of a busy market area, as they best represent the population of interest.

2. Convenience Sampling Units are selected simply because they are easily available and accessible. Example: A student conducting a survey on mobile phone usage collects data from friends and classmates in his own college.

3. Quota Sampling The population is divided into groups and a fixed number (quota) is assigned to each group; within each quota, units are chosen by convenience or judgement. Example: An interviewer is told to collect opinions from 25 males and 25 females in a shopping mall; he chooses whoever is available until each quota is filled.

4. Snowball Sampling Existing respondents refer further respondents, so the sample grows like a rolling snowball. Useful for hidden or hard-to-reach populations. Example: A study on drug users begins with one known user who refers other users, who in turn refer more.

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Conclusion

Probability sampling is preferred when representativeness and generalization are required, while non-probability sampling is useful when the population is unknown, hard to reach, or when time and cost are limited. The choice of technique depends on the nature of the population, the objective of the study, and available resources.

Questionnaire vs. Schedule

Kinds of Schedule

1. Structured schedule — All questions are pre-determined, with fixed wording and fixed alternatives. Used in descriptive and census-type surveys.
2. Unstructured schedule — Only broad topics or points are listed; the interviewer frames questions freely. Used in exploratory and case studies.
3. Non-structured schedule — No fixed questions at all; the investigator holds a free conversation and records relevant information.
4. Rating schedule — Respondents are asked to rate objects, persons or institutions on a scale (e.g. excellent, good, average, poor).
5. Documentary schedule — Information is collected from records, documents, registers and files rather than from living persons.
6. Observation schedule — The investigator records behaviour or events as observed, without asking questions.
7. Census schedule — A single, uniform schedule applied to every unit of the universe (as in the national census).
8. Sample schedule — Applied only to a selected representative portion of the population.

Contents of a Schedule

1. Heading / identification particulars — Name of the survey, place, date, schedule number, and identification details of the respondent (name, address, age, sex, occupation).
2. Explanatory introduction — A short note on the purpose of the study and assurance of confidentiality, to win the respondent's confidence.
3. Instructions to enumerators — 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: simple to complex, general to specific, and personal or sensitive questions at the end.
5. Cross-check / verification questions — A few overlapping questions to test the reliability of the answers given.
6. Space for answers — Adequate blank space or boxes, with coding columns for easy tabulation.
7. Classification and coding section — Columns for post-survey classification by age, sex, income, etc., and code numbers for computer processing.
8. Footing / ending — Signature of the enumerator, date of interview, time taken, and remarks of the supervisor.

Z-test

A Z-test is a statistical test of hypothesis in which the test statistic follows the standard normal distribution (Z-distribution) under the null hypothesis. It is used when the sample size is large ($n \ge 30$) or when the population standard deviation $\sigma$ is known, so that the sampling distribution of the mean is (approximately) normal.

Conditions for use:

• The population standard deviation $\sigma$ is known, or the sample size is large.
• The sample is random and independent.
• The population is normal, or $n$ is large enough for the Central Limit Theorem to apply.

Purposes:

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

The test statistic is

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

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Numerical

Given:

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

Hypotheses:

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

Test statistic:

$Z = \frac{\bar{x} - \mu_0}{\sigma/\sqrt{n}} = \frac{113.5 - 110}{10/\sqrt{50}}$

$= \frac{3.5}{10/7.0711} = \frac{3.5}{1.4142} = 2.475$

Critical value: For a one-tailed test at $\alpha = 0.05$, the critical value is

$Z_{0.05} = 1.645$

Decision: Since the calculated value $Z = 2.475 > 1.645$, we reject $H_0$ at the 5% level of significance.

Conclusion: There is sufficient evidence to conclude that the population mean IQ is greater than 110.

Preparing and Presenting a Research Proposal

A research proposal is a written document that describes what the researcher intends to study, why it is worth studying, and how the study will be carried out. It serves as a blueprint of the research and is submitted to a department, supervisor, or funding agency for approval. A good proposal must be clear, logical, feasible, and academically justified.

A. Steps in Preparing a Research Proposal

1. Selecting and defining the research problem The researcher identifies a broad area of interest, reviews existing literature, and narrows it down to a specific, manageable problem. The problem should be researchable, relevant, and original. It is then stated clearly in one or two sentences.

2. Formulating objectives and research questions The objectives state what the study intends to achieve (general and specific objectives). Research questions or hypotheses are derived from these objectives. They give direction to the whole study.

3. Reviewing the literature Relevant books, journal articles, theses, reports, and government publications are reviewed to find out what has already been done, to identify the research gap, and to develop a theoretical framework. This also helps in selecting appropriate variables and methods.

4. Developing the theoretical/conceptual framework The researcher shows the relationship among the variables of the study, often with the help of a diagram. This framework links the problem to existing theory and guides the analysis.

5. Determining the research methodology This is the most important technical part. It includes:

• Research design — descriptive, exploratory, causal-comparative, or case study.
• Population and sample — definition of population, sample size, and sampling technique (probability or non-probability).
• Sources of data — primary and secondary.
• Instruments of data collection — questionnaire, interview, observation, or secondary records.
• Data analysis techniques — frequency tables, percentages, mean, correlation, regression, chi-square, etc.
• Reliability and validity of the instruments.

6. Preparing the work schedule (time plan) A Gantt chart or table showing the activities (literature review, fieldwork, data analysis, report writing) and the time allotted to each.

7. Preparing the budget An estimate of the cost of the study — stationery, travel, enumerators, printing, and miscellaneous expenses.

8. Writing the proposal in the required format The usual format is:

• Title of the study
• Introduction / background
• Statement of the problem
• Objectives of the study
• Research questions / hypotheses
• Significance of the study
• Review of literature
• Research methodology
• Limitations of the study
• Work schedule and budget
• References / bibliography
• Appendices (questionnaire, etc.)

9. Getting it approved The draft is submitted to the supervisor or research committee, who may suggest corrections. After revision, the final proposal is approved and the research work begins.

B. Presenting a Research Proposal

Presentation means communicating the proposal clearly to the supervisor, department, or funding body. It is usually done in two ways:

1. Written presentation The proposal is typed neatly on standard paper with a proper title page, table of contents, headings and sub-headings, page numbers, and a consistent referencing style (APA, MLA, etc.). Language should be simple, precise, and free from grammatical errors. Tables, charts, and diagrams should be used where they make the content clearer.

2. Oral presentation (seminar / defence) The researcher presents the proposal before a committee using slides or transparencies. The presentation should cover:

• Title and objectives of the study
• Statement of the problem and its significance
• Brief literature review and research gap
• Methodology to be used
• Expected outcome, time schedule, and budget

The presenter should keep within the allotted time, speak clearly, maintain eye contact, use simple visual aids, and be prepared to answer questions from the committee. Suggestions given during the presentation should be incorporated into the final proposal.

Conclusion

Thus, a research proposal is prepared systematically by selecting a problem, reviewing literature, framing objectives and hypotheses, deciding the methodology, and planning the time and budget. It is presented both in written form and orally before the concerned authority. A well-prepared and well-presented proposal not only gets approval but also ensures that the research is carried out in an organised, scientific, and time-bound manner.

Research Report

Meaning

A research report is a formal, written document that presents the findings of a systematic investigation in an organised and objective manner. It communicates the problem studied, the methods used, the data collected and analysed, and the conclusions or recommendations drawn from the study. It is the final output of the research process and serves as a permanent record of the work done, enabling others to understand, evaluate, and use the findings.

In short, a research report is a structured communication of research objectives, methodology, findings, and conclusions, written for a specific audience such as academicians, management, or funding agencies.

Procedure of Writing a Research Report

The procedure followed in writing a research report involves the following steps:

1. Planning the report Before writing, the researcher decides the purpose, scope, and audience of the report. The type of report (thesis, project report, article), its length, format, and style are determined. A clear outline or framework is prepared to give direction to the writing work.

2. Collecting and organising the material All the materials gathered during the study — research design, questionnaires, interview schedules, raw data, statistical tables, notes, and literature reviewed — are collected and arranged in a logical order. Irrelevant material is discarded and the relevant facts are classified under appropriate headings.

3. Analysing and interpreting the data The collected data are edited, coded, tabulated, and analysed using appropriate statistical tools (percentages, averages, correlation, chi-square, etc.). The results are then interpreted in the light of the objectives and hypotheses of the study. This forms the core of the report.

4. Preparing the outline (skeleton) A detailed outline of the report is prepared, showing the main sections, sub-sections, and the sequence in which they will appear. This ensures completeness, coherence, and balanced treatment of all aspects of the study.

5. Writing the first draft Using the outline, the researcher writes the first draft covering all parts — introduction, literature review, methodology, results, discussion, and conclusion. At this stage, emphasis is on content and flow rather than on polishing language.

6. Revising and editing the draft The first draft is carefully reviewed for gaps, repetition, inconsistency, and errors in logic. Necessary additions, deletions, and rearrangements are made. The language is refined for clarity, conciseness, and objectivity. Tables, graphs, and figures are checked and properly numbered and titled.

7. Preparing the final report The edited draft is rewritten or typed in the prescribed format, with a title page, table of contents, list of tables and figures, acknowledgements, references/bibliography, and appendices. Care is taken regarding typing, spacing, margins, and citation style.

8. Presentation and submission The final report is submitted to the concerned authority or presented before the concerned body (supervisor, committee, or seminar). In oral presentation, the researcher explains the objectives, methods, findings, and recommendations, and answers queries.

Conclusion

Thus, writing a research report is a systematic process that moves from planning and collection of material, through analysis and drafting, to revision, finalisation, and presentation. A good report is clear, concise, objective, well-organised, and free from personal bias, so that it serves as a reliable source of information for decision-making and further research.

1. Meaning of a Score

A score is a numerical value that shows the performance or achievement of an individual in a test, examination, or measurement. It may be:

• Raw score: the original score obtained, e.g. 45 marks out of 60.
• Transformed score: a raw score converted into another scale for comparison or interpretation, e.g. percentage, percentile, z-score, T-score.

2. Transformation of a Score to a Percentage Scale

If a person obtains raw score (X) out of maximum possible score (M), then the percentage score is:

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

For example, if a student scores 45 out of 60:

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

Thus, a raw score is transformed to a percentage scale by dividing the obtained score by the total full marks and multiplying by 100.

3. Percentile Rank

The percentile rank of a score is the percentage of individuals in a group who score at or below that score. For ranked data, it is calculated as:

[ PR = 100 - \frac{100R - 50}{N} ]

or equivalently,

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

where:

• (R) = rank of the individual from the top, with rank 1 as the highest score
• (N) = total number of individuals

4. Calculation

Given six individuals and their ranks:

Here, (N = 6).

Percentile rank of B

B has rank (R = 4).

[ PR_B = \frac{100(6 - 4 + 0.5)}{6} ]

[ PR_B = \frac{100 \times 2.5}{6} = \frac{250}{6} = 41.67 ]

So, B lies at approximately the 42nd percentile.

Percentile rank of E

E has rank (R = 3).

[ PR_E = \frac{100(6 - 3 + 0.5)}{6} ]

[ PR_E = \frac{100 \times 3.5}{6} = \frac{350}{6} = 58.33 ]

So, E lies at approximately the 58th percentile.

Final Answer

• Percentile rank of B (= 41.67 \approx 42)
• Percentile rank of E (= 58.33 \approx 58)

This means B is above about 42% of the group, while E is above about 58% of the group.

Reference Citing

Reference citing is the practice of formally acknowledging the sources of information, ideas, data or quotations used in a piece of academic writing. It means giving complete bibliographic details of every source — author, title, year, publisher, place, page numbers, URL/DOI — both within the text (in‑text citation) and at the end of the work (reference list or bibliography). A citation tells the reader whose idea it is and where it can be found.

Importance

1. Avoids plagiarism — credit is given to the original author.
2. Verifies and supports the writer's arguments with evidence.
3. Helps readers locate the original source for further study.
4. Shows the depth and breadth of the research done.
5. Maintains academic honesty and the credibility of the researcher.

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Approaches Used in Citing Sources

A. By placement in the text

B. By citation style

1. APA (American Psychological Association) — author–date, used in management, social sciences, education. Most common in BBS/BSc research reports.
2. MLA (Modern Language Association) — author–page, used in language and literature.
3. Chicago / Turabian — two forms: Notes–Bibliography (footnotes) and Author–Date.
4. Harvard — author–date, similar to APA but with place of publication.
5. Vancouver — numbered style, used in medicine and science.
6. IEEE / ACS — numbered styles used in engineering and chemistry.

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Format of Citing Journals, Books and Articles

1. Books

APA: Author, A. A. (Year). Title of book (Edition). Publisher.

Sharma, R. (2019). Business research methods (3rd ed.). Kathmandu: Ekta Books.

MLA: Last name, First name. Title of Book. Publisher, Year.

Sharma, Rajan. Business Research Methods. Ekta Books, 2019.

Harvard: Author, Year, Title, Publisher, Place.

Sharma, R., 2019, Business Research Methods, Ekta Books, Kathmandu.

Chicago (footnote): ¹Rajan Sharma, Business Research Methods, 3rd ed. (Kathmandu: Ekta Books, 2019), 45.

2. Journal Articles

A journal citation needs author, year, article title, journal name, volume, issue and page range.

APA: Author, A. A. (Year). Title of article. Journal Name, Volume(Issue), pages. https://doi.org/…

Thapa, B., & Karki, S. (2021). Impact of capital structure on profitability of Nepalese commercial banks. Journal of Business Studies, 12(2), 45–60. https://doi.org/10.3126/jbs.v12i2.34567

MLA: Last name, First name. "Title of Article." Journal Name, vol. 12, no. 2, 2021, pp. 45–60.

Thapa, Bikash, and Sita Karki. "Impact of Capital Structure on Profitability." Journal of Business Studies, vol. 12, no. 2, 2021, pp. 45–60.

Harvard: Author, Year, 'Title of article', Journal Name, vol. 12, no. 2, pp. 45–60.

3. Articles (newspaper, magazine, chapter in an edited book, web article)

Newspaper article (APA): Author, A. A. (Year, Month Day). Title of article. Newspaper Name, page.

Poudel, K. (2022, March 15). Inflation hits rural households hardest. The Kathmandu Post, p. 4.

Chapter in an edited book (APA): Author, A. A. (Year). Chapter title. In B. B. Editor (Ed.), Book title (pp. xx–xx). Publisher.

Adhikari, M. (2020). Sampling techniques in social research. In P. R. Joshi (Ed.), Readings in research methodology (pp. 88–105). Kathmandu: New Hira Books.

Online article (APA): Author, A. A. (Year, Month Day). Title of article. Site Name. URL

Nepal Rastra Bank. (2023, July 25). Monetary policy for 2023/24. https://www.nrb.org.np

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Conclusion

Reference citing is thus the systematic acknowledgement of sources, done either as in‑text citations with a reference list or as footnotes/endnotes. The choice of approach depends on the discipline and the style prescribed — APA and Harvard for management and social sciences, MLA for literature, Chicago for history, and Vancouver/IEEE for science and engineering. Whatever the style, the citation must be complete, consistent and accurate for books, journals and articles alike.