Download to read offline
Uploaded byRenad Magdy
Sample tecnique
This document discusses different sampling techniques used in research. It defines key terminology like population, sample, and sampling frame. It explains the difference between probability samples, which use random selection, and non-probability samples. The document then describes four sampling techniques: simple random sampling, systematic sampling, stratified sampling, and cluster sampling. For each technique, it provides details on the method and lists advantages and disadvantages.
More Related Content
Similar to Sample tecnique
Sample tecnique
- 1. SAMPLE B Y W AS A N M A G D Y K H A M I S 5 2 1 2 3 3 3 R E N A D M A G D Y K H A M I S 5 2 1 2 1 4 7
- 2. OUTLINE - Terminology - Differencebetween Probability sample AND Non-probability sample - How to select sample - Probability sample - Questions
- 3. TERMINOLOGY • Population …is the total collection of elements about which we wish to make some inferences • Sample … Subset of a larger population • Sampling Frame … is the listing of all population elements from which the sample will be drawn • Sampling Units … Group selected for the sample • Sampling scheme … Method of selecting sampling units from sampling frame
- 4. GOOD SAMPLE Good sampledepends on the nature of population , time and money - The sample must be: 1. representative of the population 2. appropriately sized (the larger the better) 3. unbiased 4. random (selections occur by chance)
- 5.
- 6. DIFFERENCE BETWEEN … •Probability sample – a method of sampling that uses of random selection so that all units/ cases in the population have an equal probability of being chosen. • Non-probability sample – does not involve random selection and methods are not based on the rationale of probability theory.
- 7. SIMPLE RANDOM SAMPLING •A sampling procedure that ensures that each element in the population will have an equal chance of being included in the sample Advantages • Easy to implement with random dialing Disadvantages • Requires list of population elements • Time consuming • Larger sample needed • Produces larger errors • High cost
- 8. SYSTEMATIC SAMPLING • Asimple process , Every name from the list will be drawn and systematically chose sample Advantages • Simple to design • Easier than simple random • Easy to determine sampling distribution of mean or proportion Disadvantages • Trends in list may bias results • Moderate cost
- 9. STRATIFIED SAMPLING • Subsamplesare drawn within different strata • Each stratum is more or less equal on some characteristic Advantages • Control of sample size in strata • Increased statistical efficiency • Provides data to represent and analyze subgroups • Enables use of different methods in strata Disadvantages • Increased error if subgroups are selected at different rates • Especially expensive if strata on population must be created • High cost
- 10. CLUSTER SAMPLING the researcherdivides the population into separate groups, called clusters then, a simple random sample of clusters is selected from the population. The researcher conducts his analysis on data from the sampled clusters • Advantages • Provides an unbiased • Economically more efficient than simple random • Lowest cost per sample • Easy to do without list Disadvantages • Often lower statistical efficiency due to subgroups being homogeneous rather than heterogeneous • Moderate cost
- 12.
- 13.
- 14. REFERENCE • https://www.mathstopia.net/sampling/systematic-random-sampling • https://en.wikipedia.org/wiki/Sampling_(statistics) •http://www.simplypsychology.org/sampling.html