Please keep at least one language enabled. || కనీసం ఒక భాషను ఎంచుకోండి.
Question 1
ప్రశ్న 1
In research, the population refers to:
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• A population is the entire set of units relevant to the research question.
• It may consist of people, literary texts, institutions, documents, linguistic items or events.
• A sample is selected from the population for actual investigation.
• The population must be clearly defined before an appropriate sampling method can be chosen.
• It may consist of people, literary texts, institutions, documents, linguistic items or events.
• A sample is selected from the population for actual investigation.
• The population must be clearly defined before an appropriate sampling method can be chosen.
వివరణ:
Question 2
ప్రశ్న 2
Which statement best defines a sample?
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• A sample is a subset selected from the larger population.
• Researchers study the sample when investigating the entire population is impractical or unnecessary.
• The quality of conclusions depends partly on how appropriately the sample was selected.
• A sampling frame is a list or operational representation from which the sample may be drawn.
• Researchers study the sample when investigating the entire population is impractical or unnecessary.
• The quality of conclusions depends partly on how appropriately the sample was selected.
• A sampling frame is a list or operational representation from which the sample may be drawn.
వివరణ:
Question 3
ప్రశ్న 3
A sampling frame is:
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• A sampling frame identifies the accessible units from which the researcher selects a sample.
• Examples include an enrolment register, employee directory or catalogue of eligible texts.
• An incomplete or outdated frame may exclude relevant members and create coverage error.
• A sampling frame should correspond as closely as possible to the defined population.
• Examples include an enrolment register, employee directory or catalogue of eligible texts.
• An incomplete or outdated frame may exclude relevant members and create coverage error.
• A sampling frame should correspond as closely as possible to the defined population.
వివరణ:
Question 4
ప్రశ్న 4
Which feature distinguishes probability sampling from non-probability sampling?
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• Probability sampling uses a random mechanism that gives eligible units known chances of selection.
• This feature supports the estimation of sampling error and statistical generalisation.
• Probability sampling does not guarantee perfect representation in every realised sample.
• Simple random, systematic, stratified and cluster sampling are major probability methods.
• This feature supports the estimation of sampling error and statistical generalisation.
• Probability sampling does not guarantee perfect representation in every realised sample.
• Simple random, systematic, stratified and cluster sampling are major probability methods.
వివరణ:
Question 5
ప్రశ్న 5
In simple random sampling:
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• Simple random sampling gives each eligible population member an equal probability of selection.
• Selection may be conducted through random numbers, software or a lottery procedure.
• It requires an adequate sampling frame containing all eligible units.
• Convenience and purposive selection are non-probability methods.
• Selection may be conducted through random numbers, software or a lottery procedure.
• It requires an adequate sampling frame containing all eligible units.
• Convenience and purposive selection are non-probability methods.
వివరణ:
Question 6
ప్రశ్న 6
A researcher selects every tenth name from a student register after choosing a random starting point. This is:
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• Systematic sampling selects units at a regular interval from an ordered sampling frame.
• The researcher first calculates a sampling interval and selects a random starting position.
• Every tenth name is then chosen in the given example.
• Hidden periodic patterns in the list can bias the sample and should be examined.
• The researcher first calculates a sampling interval and selects a random starting position.
• Every tenth name is then chosen in the given example.
• Hidden periodic patterns in the list can bias the sample and should be examined.
వివరణ:
Question 7
ప్రశ్న 7
Which sampling method divides a population into relevant subgroups and randomly samples from each subgroup?
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• Stratified sampling divides the population into internally defined subgroups called strata.
• Random samples are then selected from every stratum.
• Strata may be based on variables such as region, institution type, gender or language background.
• The method helps ensure that important subgroups are adequately represented.
• Random samples are then selected from every stratum.
• Strata may be based on variables such as region, institution type, gender or language background.
• The method helps ensure that important subgroups are adequately represented.
వివరణ:
Question 8
ప్రశ్న 8
In cluster sampling, the researcher initially selects:
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• Cluster sampling selects naturally occurring groups such as schools, districts, classrooms or institutions.
• Researchers may study all units within selected clusters or take a further sample within them.
• The method can reduce travel and administrative costs when populations are geographically dispersed.
• Cluster sampling differs from stratification because only selected clusters may be studied.
• Researchers may study all units within selected clusters or take a further sample within them.
• The method can reduce travel and administrative costs when populations are geographically dispersed.
• Cluster sampling differs from stratification because only selected clusters may be studied.
వివరణ:
Question 9
ప్రశ్న 9
Purposive sampling involves:
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• Purposive sampling selects information-rich cases on the basis of predefined criteria.
• It is common in qualitative research, case studies and specialised literary or linguistic investigations.
• Examples include selecting experienced translators or novels displaying a particular narrative technique.
• Because selection is non-random, statistical generalisation must be made cautiously.
• It is common in qualitative research, case studies and specialised literary or linguistic investigations.
• Examples include selecting experienced translators or novels displaying a particular narrative technique.
• Because selection is non-random, statistical generalisation must be made cautiously.
వివరణ:
Question 10
ప్రశ్న 10
Snowball sampling is especially useful when:
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• Snowball sampling begins with a small number of eligible participants who refer the researcher to others.
• It is useful for hidden, dispersed or difficult-to-identify populations.
• The method can help researchers gain access through social networks.
• It may produce network bias because participants often refer people similar to themselves.
• It is useful for hidden, dispersed or difficult-to-identify populations.
• The method can help researchers gain access through social networks.
• It may produce network bias because participants often refer people similar to themselves.
వివరణ:
Question 11
ప్రశ్న 11
Which situation is the clearest example of convenience sampling?
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• Convenience sampling selects participants primarily because they are easily accessible.
• Surveying students in the researcher's own class is quick and inexpensive.
• The method may produce substantial selection bias because accessible participants may differ from the wider population.
• Findings from convenience samples should not be generalised without careful qualification.
• Surveying students in the researcher's own class is quick and inexpensive.
• The method may produce substantial selection bias because accessible participants may differ from the wider population.
• Findings from convenience samples should not be generalised without careful qualification.
వివరణ:
Question 12
ప్రశ్న 12
Which factor should be considered when determining an appropriate sample size?
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• Sample size depends on the purpose and design of the study.
• Population variability, desired confidence, acceptable error and planned analysis are important considerations.
• Qualitative studies may prioritise information richness and saturation rather than statistical estimation.
• Time, access, cost and participant availability also affect the feasible sample size.
• Population variability, desired confidence, acceptable error and planned analysis are important considerations.
• Qualitative studies may prioritise information richness and saturation rather than statistical estimation.
• Time, access, cost and participant availability also affect the feasible sample size.
వివరణ:
Question 13
ప్రశ్న 13
Sampling error refers to:
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• Sampling error arises because a sample rather than the entire population is observed.
• Different random samples from the same population may produce slightly different estimates.
• Larger well-designed probability samples generally reduce sampling error.
• Data-entry mistakes and measurement bias are non-sampling errors.
• Different random samples from the same population may produce slightly different estimates.
• Larger well-designed probability samples generally reduce sampling error.
• Data-entry mistakes and measurement bias are non-sampling errors.
వివరణ:
Question 14
ప్రశ్న 14
Statement I: Increasing sample size generally reduces random sampling error.
Statement II: Increasing sample size automatically removes bias caused by a defective sampling frame.
Statement II: Increasing sample size automatically removes bias caused by a defective sampling frame.
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• Statement I is correct because larger probability samples usually produce more precise estimates.
• Statement II is incorrect because a large sample drawn from a defective frame may remain systematically biased.
• Increasing size cannot restore population members who were excluded from the frame.
• Therefore, Statement I is correct, but Statement II is incorrect.
• Statement II is incorrect because a large sample drawn from a defective frame may remain systematically biased.
• Increasing size cannot restore population members who were excluded from the frame.
• Therefore, Statement I is correct, but Statement II is incorrect.
వివరణ:
Question 15
ప్రశ్న 15
Assertion: Stratified sampling may produce more precise estimates than simple random sampling.
Reason: Stratification can ensure adequate representation of important population subgroups.
Reason: Stratification can ensure adequate representation of important population subgroups.
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• The Assertion is correct because effective stratification can reduce sampling variability.
• The Reason is correct because all important subgroups can be represented through planned sampling.
• Precision is especially improved when units within strata are relatively similar and strata differ meaningfully.
• The Reason correctly explains why stratified sampling can outperform an unrestricted simple random sample.
• The Reason is correct because all important subgroups can be represented through planned sampling.
• Precision is especially improved when units within strata are relatively similar and strata differ meaningfully.
• The Reason correctly explains why stratified sampling can outperform an unrestricted simple random sample.
వివరణ:
Question 16
ప్రశ్న 16
Match List I with List II.
List I
1. Simple random sampling
2. Systematic sampling
3. Purposive sampling
4. Snowball sampling
List II
A. Selection through participant referrals
B. Selection of information-rich cases
C. Equal random chance for each eligible unit
D. Selection at a fixed interval after a random start
List I
1. Simple random sampling
2. Systematic sampling
3. Purposive sampling
4. Snowball sampling
List II
A. Selection through participant referrals
B. Selection of information-rich cases
C. Equal random chance for each eligible unit
D. Selection at a fixed interval after a random start
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• Simple random sampling matches C because every eligible unit has an equal random chance.
• Systematic sampling matches D because units are selected at fixed intervals after a random start.
• Purposive sampling matches B because information-rich cases are deliberately chosen.
• Snowball sampling matches A because existing participants refer the researcher to additional participants.
• Systematic sampling matches D because units are selected at fixed intervals after a random start.
• Purposive sampling matches B because information-rich cases are deliberately chosen.
• Snowball sampling matches A because existing participants refer the researcher to additional participants.
వివరణ:
Question 17
ప్రశ్న 17
Consider the following statements:
1. Probability sampling supports estimation of sampling error.
2. Convenience sampling selects participants mainly because they are accessible.
3. Cluster sampling requires every cluster in the population to be studied.
4. Purposive sampling uses criteria relevant to the research purpose.
Which statements are correct?
1. Probability sampling supports estimation of sampling error.
2. Convenience sampling selects participants mainly because they are accessible.
3. Cluster sampling requires every cluster in the population to be studied.
4. Purposive sampling uses criteria relevant to the research purpose.
Which statements are correct?
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• Statement 1 is correct because known selection probabilities permit estimation of sampling error.
• Statement 2 is correct because accessibility is the defining basis of convenience sampling.
• Statement 3 is incorrect because cluster sampling usually selects only some clusters.
• Statement 4 is correct; therefore, the correct combination is 1, 2 and 4 only.
• Statement 2 is correct because accessibility is the defining basis of convenience sampling.
• Statement 3 is incorrect because cluster sampling usually selects only some clusters.
• Statement 4 is correct; therefore, the correct combination is 1, 2 and 4 only.
వివరణ:
Question 18
ప్రశ్న 18
A university has a numbered list of 2,000 students and requires a sample of 200. After a random start, every tenth student is selected. Which method is being used?
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• The sampling interval is obtained by dividing 2,000 by 200, which gives 10.
• A random starting point is selected before taking every tenth student.
• This procedure is systematic sampling.
• The researcher should verify that the ordered list contains no periodic structure related to the study variable.
• A random starting point is selected before taking every tenth student.
• This procedure is systematic sampling.
• The researcher should verify that the ordered list contains no periodic structure related to the study variable.
వివరణ:
Question 19
ప్రశ్న 19
A researcher wants rural, urban and semi-urban teachers to be represented according to their proportions in the population. Which method is most appropriate?
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• The population should first be divided into rural, urban and semi-urban strata.
• Participants are then randomly selected from each stratum in proportion to its population size.
• This preserves the subgroup proportions in the final sample.
• Snowball and convenience methods do not provide equivalent probability-based representation.
• Participants are then randomly selected from each stratum in proportion to its population size.
• This preserves the subgroup proportions in the final sample.
• Snowball and convenience methods do not provide equivalent probability-based representation.
వివరణ:
Question 20
ప్రశ్న 20
A national study randomly selects ten districts and then surveys every college within those selected districts. This is primarily an example of:
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• Districts function as naturally occurring geographic clusters.
• The researcher randomly selects some districts rather than drawing institutions from the entire country directly.
• Surveying colleges within selected districts is a form of cluster-based sampling.
• The design may reduce costs but can have larger sampling error when units within clusters are highly similar.
• The researcher randomly selects some districts rather than drawing institutions from the entire country directly.
• Surveying colleges within selected districts is a form of cluster-based sampling.
• The design may reduce costs but can have larger sampling error when units within clusters are highly similar.
వివరణ:
Question 21
ప్రశ్న 21
A researcher studying an informal community of unpublished translators asks each participant to recommend other eligible translators. Which sampling method is most suitable?
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• The population is difficult to identify through a complete public list.
• Initial participants can provide referrals to other eligible members.
• This recruitment process characterises snowball sampling.
• The researcher should acknowledge possible network bias and protect participant confidentiality.
• Initial participants can provide referrals to other eligible members.
• This recruitment process characterises snowball sampling.
• The researcher should acknowledge possible network bias and protect participant confidentiality.
వివరణ:
Question 22
ప్రశ్న 22
Which statement correctly distinguishes sampling error from non-sampling error?
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• Sampling error arises because a sample estimate may differ from the true population value.
• Non-sampling errors include measurement error, non-response, inaccurate recording and coverage problems.
• Both kinds of error can affect quantitative research.
• Increasing sample size may reduce random sampling error but does not necessarily reduce systematic non-sampling bias.
• Non-sampling errors include measurement error, non-response, inaccurate recording and coverage problems.
• Both kinds of error can affect quantitative research.
• Increasing sample size may reduce random sampling error but does not necessarily reduce systematic non-sampling bias.
వివరణ:
Question 23
ప్రశ్న 23
Why may a more heterogeneous population require a larger probability sample?
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• Heterogeneous populations contain greater variation across relevant characteristics.
• More observations may therefore be needed to estimate population parameters with acceptable precision.
• Stratification can sometimes improve efficiency by organising variation into meaningful subgroups.
• A larger sample reduces random error but cannot automatically remove systematic bias.
• More observations may therefore be needed to estimate population parameters with acceptable precision.
• Stratification can sometimes improve efficiency by organising variation into meaningful subgroups.
• A larger sample reduces random error but cannot automatically remove systematic bias.
వివరణ:
Question 24
ప్రశ్న 24
A population contains 1,200 units, and a researcher needs a systematic sample of 100 units. What is the appropriate sampling interval?
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• The sampling interval is calculated by dividing the population size by the desired sample size.
• Here, 1,200 divided by 100 equals 12.
• After selecting a random start between 1 and 12, every twelfth unit is chosen.
• The researcher should examine the ordering of the frame for possible periodicity.
• Here, 1,200 divided by 100 equals 12.
• After selecting a random start between 1 and 12, every twelfth unit is chosen.
• The researcher should examine the ordering of the frame for possible periodicity.
వివరణ:
Question 25
ప్రశ్న 25
Which combination represents the strongest sampling plan for estimating the views of teachers across a diverse state?
Select an option first. || ముందుగా ఒక సమాధానాన్ని ఎంచుకోండి.
Explanation:
• A defensible sampling plan begins with a clearly defined target population and an adequate sampling frame.
• Stratification ensures that important regional groups are represented.
• Random selection reduces researcher selection bias and supports statistical inference.
• Sample size, response rate, coverage and remaining sampling limitations should also be reported.
• Stratification ensures that important regional groups are represented.
• Random selection reduces researcher selection bias and supports statistical inference.
• Sample size, response rate, coverage and remaining sampling limitations should also be reported.
వివరణ:
Answer Key సమాధానాల పట్టిక
-
Question 1 ప్రశ్న 1Answer: A. the complete group of persons, texts, events or units about which conclusions are intended సమాధానం: A.
-
Question 2 ప్రశ్న 2Answer: B. A subset of the population selected for investigation సమాధానం: B.
-
Question 3 ప్రశ్న 3Answer: C. the accessible list or representation of population units from which a sample is selected సమాధానం: C.
-
Question 4 ప్రశ్న 4Answer: D. Each population unit has a known, non-zero probability of selection. సమాధానం: D.
-
Question 5 ప్రశ్న 5Answer: A. each eligible population member has an equal chance of selection సమాధానం: A.
-
Question 6 ప్రశ్న 6Answer: B. systematic sampling సమాధానం: B.
-
Question 7 ప్రశ్న 7Answer: C. Stratified sampling సమాధానం: C.
-
Question 8 ప్రశ్న 8Answer: D. naturally occurring groups containing population members సమాధానం: D.
-
Question 9 ప్రశ్న 9Answer: A. deliberately selecting cases that possess characteristics relevant to the research question సమాధానం: A.
-
Question 10 ప్రశ్న 10Answer: B. members of a specialised or difficult-to-reach population identify further participants సమాధానం: B.
-
Question 11 ప్రశ్న 11Answer: C. Surveying students who are immediately available in the researcher's own class సమాధానం: C.
-
Question 12 ప్రశ్న 12Answer: D. Population variability, desired precision, design and available resources సమాధానం: D.
-
Question 13 ప్రశ్న 13Answer: A. the difference between a sample estimate and the corresponding population value arising from sampling సమాధానం: A.
-
Question 14 ప్రశ్న 14Answer: B. Statement I is correct, but Statement II is incorrect సమాధానం: B.
-
Question 15 ప్రశ్న 15Answer: A. Both Assertion and Reason are correct, and Reason is the correct explanation of Assertion సమాధానం: A.
-
Question 16 ప్రశ్న 16Answer: B. 1-C, 2-D, 3-B, 4-A సమాధానం: B.
-
Question 17 ప్రశ్న 17Answer: C. 1, 2 and 4 only సమాధానం: C.
-
Question 18 ప్రశ్న 18Answer: A. Systematic sampling సమాధానం: A.
-
Question 19 ప్రశ్న 19Answer: B. Proportionate stratified sampling సమాధానం: B.
-
Question 20 ప్రశ్న 20Answer: C. cluster sampling సమాధానం: C.
-
Question 21 ప్రశ్న 21Answer: D. Snowball sampling సమాధానం: D.
-
Question 22 ప్రశ్న 22Answer: A. Sampling error results from observing a sample, whereas non-sampling error may arise from measurement, coverage or non-response problems. సమాధానం: A.
-
Question 23 ప్రశ్న 23Answer: B. Greater variability generally requires more observations to achieve a desired level of precision. సమాధానం: B.
-
Question 24 ప్రశ్న 24Answer: C. 12 సమాధానం: C.
-
Question 25 ప్రశ్న 25Answer: D. Defining the population, constructing an adequate frame, stratifying by relevant regions and randomly selecting a sufficient sample సమాధానం: D.