Unit_4_Sampling_With_Borders book presentation.pptx

This presentation is from c r kothari book

1. Unit 4: Sampling Business Research Methods – C.R. Kothari (Detailed Explanation)

2. Introduction to Sampling Sampling is the process of selecting a few (a sample) from a bigger group (the sampling population) to become the basis for estimating or predicting the prevalence of an unknown piece of information, situation, or outcome regarding the bigger group.

3. Defining Universe, Population & Sample • Universe: Totality of items under study. • Population: Entire group of people, events, or items of interest. • Sample: A subset of the population selected for actual investigation.

4. Characteristics of a Good Sample • Representativeness • Homogeneity • Adequate size • Independence • Practicality A good sample should closely reflect the characteristics of the population.

5. Sampling Frame A sampling frame is a list or database from which a sample is drawn. Example: A voter list can be a sampling frame for a political survey. It should be complete, up-to-date, and accurate to ensure effective sampling.

6. Sampling & Non-Sampling Errors • Sampling Errors: Differences between sample results and population parameters due to chance selection. • Non-Sampling Errors: Errors not related to sampling such as: - Data collection mistakes - Interviewer bias - Non-response bias - Data processing errors

7. Methods to Reduce Errors • Use probabilistic sampling methods • Train data collectors properly • Pre-test instruments • Increase sample size • Use standard data collection methods

8. Sample Size Constraints Factors that limit sample size: • Time • Budget • Access to respondents • Desired level of accuracy

9. Non-Response Non-response occurs when selected individuals do not participate. Causes: • Refusal to respond • Unreachable respondents • Incomplete responses Solutions: • Follow-ups • Incentives • Simplifying questionnaire

10. Probability Sampling Methods • Simple Random Sampling: Equal chance to each member • Systematic Sampling: Every nth unit selected • Stratified Sampling: Population divided into strata, sample drawn from each • Cluster Sampling: Divide population into clusters and randomly sample clusters • Area Sampling: Like cluster sampling, but based on geographical areas

11. Non-Probability Sampling Methods • Judgment Sampling: Based on researcher's judgment • Convenience Sampling: Based on ease of access • Purposive Sampling: For specific purpose or trait • Quota Sampling: Like stratified but non-random • Snowball Sampling: Existing subjects recruit future subjects

12. Sample Size Determination Steps: 1. Define the population 2. Decide the margin of error 3. Choose confidence level 4. Estimate the population variability 5. Use sample size formula or software tools Practical considerations include time, cost, and resource availability.