KSLU Solved Question Papers
Elements of Legal Research
Previous Year Question Paper with Solution
B.B.A. LL.B. (Five Years) · Sem 1
2021 Mar Examination
munotes.in
Mumbai
B.B.A. LL.B. (Five Years) Sem 1 Elements of Legal Research 2021 Mar Question Paper with Solutions
First published on munotes.in on 2 November 2026.
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Contents
The questions below are the paper as Karnataka State Law University set it at the 2021 Mar examination, in the order it was set.
MarksPage
The Paper as Set
The questions in this volume are the questions asked at the 2021 Mar examination, reproduced as Karnataka State Law University set them, in the order it set them. Long reading passages are shortened to their first and last lines and our summary; the original paper prints them in full. Only the answers are ours. See the original question paper.
Duration 2½ hours · Total marks 70 · 12 questions answered
How to use this volume
Solve the paper first, under exam conditions and against the clock. Then read the answers here and mark your own. Reading a solution before attempting the question feels productive and teaches very little, because recognising an answer is not the same as being able to write one.
Q1Explain the significance of sampling method in legal research.[10]
Answer
For full marks, cover: what sampling is and its vocabulary (population, sampling frame, sampling unit, sample size, sampling error); why researchers sample and when they should not; probability methods (simple random, systematic, stratified, cluster, multi-stage) with legal examples; non-probability methods (convenience, purposive, quota, snowball) with their limits; choosing a sample size; the rule of honest reporting; a table; a conclusion.
Meaning
Sampling is the study of a part of a group in order to draw conclusions about the whole. The part studied is the sample. Researchers sample because studying everyone is usually too costly or slow. But a sample tells us about the whole only if it was chosen properly.
The vocabulary
- Population or universe: every unit the conclusion is about, such as every accused produced for remand in one court over six weeks.
- Sampling frame: the list from which the sample is drawn, such as a court register. The frame is rarely the same as the population, and that gap causes much error.
- Sampling unit: what is selected, such as a person, a household or a case file.
- Sample size: how many units are selected.
- Sampling error: the difference between the sample's result and the true figure for the whole population.
Why sample, and when not to
Sampling saves cost and time, and makes studies of large or hard-to-reach groups possible. A careful study of a good sample can even be more accurate than a hurried study of everyone. But where the population is small, such as the eleven law colleges in a district, the researcher should study all of them. This is called a census, and it removes sampling error altogether.
Probability sampling
In probability sampling every unit has a known chance of selection, so the results can be generalised and the error can be estimated.
- Simple random sampling: every unit has an equal chance, chosen by lot or random numbers. Example: forty files drawn at random from a numbered clinic register.
- Systematic sampling: every tenth unit, for example, from a random start. Example: every tenth file in a court register. It fails if the list has a pattern matching the interval, such as taking the same weekday every time.
- Stratified sampling: the population is divided into groups, and a sample is drawn from each. Example: legal aid clinic users divided by sex, so that women are properly represented.
- Cluster sampling: some clusters, such as villages, are chosen at random, and everyone in them is studied. It is cheaper, but less precise.
- Multi-stage sampling: districts, then courts within them, then cases within those courts.
Non-probability sampling
Here the chance of selection is unknown, so results describe only those studied.
- Convenience sampling: whoever is available, such as litigants at the court gate. It is quick but biased.
- Purposive sampling: units chosen deliberately for what they are, such as the three colleges known to run real clinics.
- Quota sampling: fixed numbers from each category, filled by whoever is available. It looks like stratified sampling but lacks random selection.
- Snowball sampling: respondents name others like them. It is the only way to reach hidden groups, such as people who gave up seeking legal aid.
Sample size
There is no single correct number. The size depends on how varied the population is, how precise the answer must be, and how many groups the results will be broken into. The researcher should ensure enough units in the smallest group he wants to report on separately.
A worked example
A student wants to study users of a legal aid clinic. The population is everyone who used the clinic in one year. The frame is the clinic's register of visitors. The unit is a register entry. The method is systematic: every fifth entry from a random start, giving about ninety users. Those who cannot be contacted are recorded, not replaced by others, so that the report can state how many were missed.
The rule of honest reporting
A study must state its population, frame, method and size, and what its sample may have missed. A non-probability sample may describe the people studied, but it may not claim to describe the whole district. Forty respondents chosen by a stated method are better research than four hundred chosen carelessly.
| Method | Chance of selection known? | Legal example |
|---|---|---|
| Simple random | Yes | Random files from a register |
| Systematic | Yes | Every tenth case file |
| Stratified | Yes | Clinic users by sex |
| Cluster | Yes | All households in chosen villages |
| Convenience | No | Litigants at the court gate |
| Purposive | No | Selected model clinics |
| Snowball | No | People who gave up on legal aid |
Conclusion. Sampling lets a researcher study part of a population to learn about the whole. Probability methods, such as simple random, systematic, stratified, cluster and multi-stage sampling, allow results to be generalised. Non-probability methods, such as convenience, purposive, quota and snowball sampling, describe only those studied. Whatever the method, an honest statement of how the sample was chosen is what makes the findings trustworthy.
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