Sampling
Download as PPTX, PDF0 likes96 views
This document provides an overview of key concepts in sampling, including population, sample, sampling frame, probability sampling, and non-probability sampling. It discusses the qualities of a probability sample, including how findings from a random sample can be generalized to the population. It also covers sample size considerations and different types of error in sampling, such as sampling error and non-sampling error.
![Normal distribution in statistics
• What is the probability that a candidate selected at random will take between 500 and 650 hours
to complete training program? Here 𝜇 = 500, 𝛿=100
Z=
𝑥−𝜇
𝛿
=
650−500
100
= 1.5 [ And at z value of 1.5, probability is 0.4332. using normal
distribution table]
• If standard error is lower the range of the population mean would be narrower.
• In stratified sampling the standard error of the mean will be smaller, as the variation between
strata is eliminated.
• In cluster sample without stratification exhibits a larger standard error of the mean than a simple
random sample.](https://image.slidesharecdn.com/sampling-191129135105/85/Sampling-26-320.jpg)
Sampling
- 1. Welcome to Our Presentation
- 2. Sampling
- 3. Contents Basic Terms & Concepts Probability Sampling The Qualities of a Probability Sample Non-Probability Sampling Sample Size Types of Error
- 4. Basic Terms & Concepts Population Basically, the universe of unit from which the sample is to be selected. Sample The segment of the population that is selected for investigation.
- 5. Basic Terms & Concepts Sampling Frame The listing of all units in the population from which the sample will be selected.
- 6. Population Sampling Frame Sample 21st Batch of A&IS Section “B” 20 Students from Section “B”
- 7. Basic Terms & Concepts Representative Sample A sample that reflect the population accurately.
- 9. Basic Terms & Concepts Probability Sample A sample that has been selected using random selection model. Non-Probability Sample A sample that has not been selected using random selection model.
- 11. Basic Terms & Concepts Sampling Error The difference between a sample and its population. Non-Sampling Error Difference between the population and the sample that arise either from deficiencies in the sampling approach.
- 12. Basic Terms & Concepts Non Response It occurs whenever some members of the sample refuse to cooperate. Census The enumeration of an entire population.
- 14. Probality Sampling Types of Probability Sampling Simple Random Sampling Systematic Sampling Stratified Random Sampling Cluster Random Sampling Multi-Stage Cluster Sampling
- 15. Simple Random Sampling Here, a random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. A simple random sample is meant to be an unbiased representation of a group.
- 16. Systematic Sampling where the elements are chosen from a target population by selecting a random starting point and selecting other members after a fixed ‘sampling interval’. Sampling interval is calculated by dividing the entire population size by the desired sample size.
- 17. Two Types Linear Systematic Sampling Circular Systematic Sampling
- 18. Stratified Random Sampling Sampling that involves the division of a population into smaller groups known as strata. In stratified random sampling or stratification, the strata are formed based on members' shared attributes or characteristics
- 19. Cluster Sampling where multiple clusters of people are created from a population where they are indicative of homogeneous characteristics and have an equal chance of being a part of the sample. In this sampling method, a simple random sample is created from the different clusters in the population.
- 20. Multi Stage Cluster Sampling Multistage sampling is the taking of samples in stages using smaller and smaller sampling units at each stage. Multistage sampling can be a complex form of cluster sampling because it is a type of sampling which involves dividing the population into groups. For example; Want to do a research on the sanitation of labor, participation of female students in classroom etc.
- 21. Garments industries Rods & Mills Transportation Bank Child Aged Women labor Division 1 D-2 D-3 Industries Labor Division Conducting A Research On: The Labor Rights
- 22. The qualities of a probability sample
- 23. The qualities of a probability sample We can generalize findings derived from a sample to the population. In quantitative research to generalize we can compare sample mean & population mean. The variation of the sample means around the population mean is the sampling error and is measured using a statistic known as the standard error of the mean. We have to ensure equivalence in a cross-cultural validation, a sample that was representative of the relevant target population.
- 24. Generalizing from a random sample to the population ABC company wants to measure the level of skill development, sample of 450 employees. As skill development standard, number of training days completed in the previous 12 months is considered. The mean number of trainings days undertaken by the sample (x) can be used to estimate the population mean (𝜇). But with known margins of error. Normal distribution technique. Sample mean of ABC company is 6.7 days training per employee. 95% probability. Standard deviation or standard error of the mean is 1.3.
- 25. The distribution of sample mean -1.96 Population mean +1.96SE (Z value) probability 0.4750 Numberofsample Value of the mean 0.4750 Population mean will lie between: • Sample mean+(1.96*standard error) • Sample mean-(1.96*standard error) So, 6.7+(1.96*1.3)=9.248 & 6.7-(1.96*1.3)=4.152 Between 9.248 and 4.152
- 26. Normal distribution in statistics • What is the probability that a candidate selected at random will take between 500 and 650 hours to complete training program? Here 𝜇 = 500, 𝛿=100 Z= 𝑥−𝜇 𝛿 = 650−500 100 = 1.5 [ And at z value of 1.5, probability is 0.4332. using normal distribution table] • If standard error is lower the range of the population mean would be narrower. • In stratified sampling the standard error of the mean will be smaller, as the variation between strata is eliminated. • In cluster sample without stratification exhibits a larger standard error of the mean than a simple random sample.
- 28. Non-Probability Sampling Types of Non-Probability Sampling Convenience Sampling Snowball Sampling Quota Sampling
- 31. Quota Sampling
- 32. Sample Size
- 33. Sample size Types of Sample size Sample size for population Sample size for statistical analysis
- 34. Sample size for population To determine the sample size of population, a researcher need to know population size confidence interval or margin of error confidence level (typically 95%). Standard of deviation
- 35. Sample size for statistical analysis Types of statistical analysis. The effect size, alpha, and desired statistical power. The effect size may be small, medium, and large. And alpha is usually set at .05
- 36. Calculation of sample size Sample size = (Z-score)2 * StdDev*(1-StdDev) / (margin of error)2 Confidence level corresponds to a Z-scores, this is a constant value needed for this equation. Example: What is the sample size that a candidate assuming to choose a 95% confidence level, .5 standard deviation, and a margin of error (confidence interval) of +/- 5%.
- 37. Calculation of sample size sample size= ((1.96)2 x .5(.5)) / (.05)2 (3.8416 x .25) / .0025 .9604 / .0025 384.16 So, 385 respondents are needed. If the sample size is too large, by decreasing confidence level or increasing margin of error – this will increase the chance for error in sampling, but it can greatly decrease the number of responses that need.
- 38. Other considerations Time and cost The sample size is profoundly affected by time and cost. The larger the sample size the greater the uneconomic proposition.
- 39. Other considerations Non response The selecting sample may not participate in interview, in that case the researcher can calculate response rate. Response rate=( no. of usable questionnaires/ total sample – unsuitable sample)*100
- 40. Other considerations Heterogeneity of the population When a sample is heterogeneous, like a population of whole country or city, the population is highly varied. The larger the heterogeneous , the greater the sample.
- 41. Types of Error
- 42. Types of Error Error Sampling Error Non- Sampling Error Measurement Error Processing Error Sampling Related Error
- 43. Sampling Error Degree of Sampling error Have performance appraisal Do not have performance appraisal Do not have performance appraisal Have performance appraisal
- 44. Limits to Generalization Representativeness Time Sample size Lack of available and/or reliable data Measure used to collect the data Self-reported data