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Unit 7 Sampling

1. Define Population 

A group of individuals or units having one or more characteristics in common which are of interest to the researcher for a particular research. 

2. Define Probability sampling 

Sampling based on some statistical concepts such as the 'Law of Large Numbers', 'Central Limit Theorem', and the 'Normal Distribution' is known as probability sampling. 

3. Define Non-probability sampling 

Sampling based on the judgements of the researcher as the most important element of control is known as non-probability sampling.

4. What are the types of sampling.

i) Simple random: Every unit in the population has equal chance of being selected. Not applicable to heterogeneous population 

ii) Systematic Sampling: Periodic arrangement of elements. Spread more evenly over the population. 

iii) Cluster Sampling:  Sampling unit is not an individual element. Applicable in case of infinite population.

iv) Stratified Random Sampling: Useful in case of heterogeneous population. Listing the elements in sub-population necessary.

5. Distinguish between multi-stage and multi-phase sampling methods. 

The main distinction between multi-stage and multi-phase sampling is the use of unit of sampling at different levels. In multi-stage, sampling is done at various levels such as national, state, district level. In multi-phase, sampling units are of the same type at each phase only a few , of them are asked for more information than others. 

6. What do you understand by an incidental sample? 

When a readily or easily available group is selected as per the convenience of the researcher, it is termed as the 'incidental sample'. 

7. Compare and contrast the purposive and quota sampling methods. 

i) Similarity - Purposive and quota sampling both include stratification.

ii) Difference - In purposive sampling actual selection of the units from a stratum, to be included in the sample is done purposively rather than by random methods. In quota sampling quota is usually determined by the proportion of the strata and quota within the strata is selected as per the availability and convenience and. not randomly.

8. State any five characteristics of a good sample. 

Following are the five characteristics of a good sample: 

i) Free from error due to bias or deliberate selection of some units. 

ii) Originally selected units are not substituted and incomplete coverage of units is not involved.

iii) As far as possible independent units are included. 

iv) It is a smaller image of the population. 

v) It is adequate in size. 

9. Examples of different types of variables

a) Nominal - tribes in India, accession number of books, subject codes

b) Numerical - height of children, number of books

10. With an example explain the use of Likert scale.

Example of the use of Likert Scale: Show your agreement with the following: 

• Early childhood experiences play an important role in further development of individual. 
• Strongly Agree; Agree; Undecided; Disagree; Strongly Disagree 

11. How do you determine the sample size when different research methods are adopted?

Factors to Consider When Determining Sample Size:

• Objectives of the study: The research question and intended use of the results influence the required sample size. For instance, studies aiming for generalizability to larger populations require larger samples than those focusing on specific, localized groups.
• Type of study: Different research methods have varying sample size requirements. Experimental studies, which prioritize internal validity, may not require large samples. Survey studies, however, often demand larger, more representative samples for generalizability.
• Available resources: Time, funding, personnel, and equipment influence sample size decisions.
• Sampling technique: The chosen technique impacts the necessary sample size. For example, stratified random sampling, which divides the population into subgroups, can achieve a representative sample with a smaller size compared to simple random sampling.
• Desired level of precision: The desired level of accuracy for estimating population parameters affects the sample size. A higher desired precision generally requires a larger sample.
• Variability in the population: Greater variability in the population necessitates larger samples to capture the full range of characteristics and ensure representativeness.
• Statistical considerations: Certain statistical tests have minimum sample size requirements.