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Sampling technique ppt
- 1. UNIVERSITY OF MYSORE MANASAGANGOTHRI FOURTH SEMESTER Paper :Research Methodology Seminar on :Sampling Method Presented to, Dr Khaizer Nikham Professor Presented By Dos in Library and Information Science Shalini B.G Manasagangotri, 2nd year MLISC Mysuru
- 3. sampling is the reduction of a continuous signal to a discrete signal. A common example is the conversion of a sound wave (a continuous signal) to a sequence of samples (a discrete-time signal). A sample is a value or set of values at a point in time and/or space. A sampler is a subsystem or operation that extracts samples from a continuous signal.
- 4. Sampling Method A process used in statistical analysis in which a predetermined number of observations will be taken from a larger population. The methodology used to sample from a larger population will depend on the type of analysis being performed, but will include simple random sampling, systematic sampling and observational sampling.
- 5. Basic Principles of Sampling Theory of sampling is based on the following laws- Law of Statistical Regularity This law comes from the mathematical theory of probability. According to King, Law of Statistical Regularity says that a moderately large number of the items chosen at random from the large group are almost sure on the average to possess the features of the large group. According to this law the units of the sample must be selected at random. Law of Inertia of Large Numbers According to this law, the other things being equal the larger the size of the sample; the more accurate the results are likely to be.
- 6. Process The sampling process comprises several stages: o Defining the population of concern o Specifying a sampling frame, a set of items or events possible to measure o Specifying a sampling method for selecting items or events from the frame o Determining the sample size o Implementing the sampling plan o Sampling and data collecting o Reviewing the sampling process
- 7. Sampling Sampling methods can be split into two distinct groups: 1. Probability samples 2. Non-probability samples
- 8. 01. Probability Samples Probability samples offer each respondent an equal probability or chance at being included in the sample. They are considered to be: Objective Empirical Scientific Quantitative Representative
- 9. 02. Non Probability Samples A non probability sample relies on the researcher selecting the respondents. They are considered to be: Interpretive Subjective Not scientific Qualitative Unrepresentative
- 10. Probability Sampling Methods Random Sampling Systematic Random Sampling Stratified Random Sampling Cluster Random Sampling Quota Random Sampling Multi-Stage Sampling
- 11. Simple Random Sampling This involves selecting anybody from the sample frame entirely at random. Random means that each person within the sample frame has an equal chance of being selected. In order to be random, a full list of everyone within a sample frame is required. Random number tables or a computer is then used to select respondents at random from the list.
- 12. Systematic Random Sampling In systematic random sampling, the researcher first randomly picks the first item or subject from the population. Then, the researcher will select each n'th subject from the list. The procedure involved in systematic random sampling is very easy and can be done manually. The results are representative of the population unless certain characteristics of the population are repeated for every n'th individual, which is highly unlikely.
- 13. Stratified Random Sampling In some cases, a population may be viewed as comprising different groups where elements in each group are similar to one another in some way. In such cases, we may gain sampling precision (i.e., reduce the variance of our estimators) as well as reduce the costs of the survey by treating the different groups separately. If we consider these groups, or strata, as separate subpopulations and draw a separate random sample from each stratum and combine the results, our sampling method is called stratified random sampling
- 14. Cluster Random Sampling Cluster sampling is a sampling technique used when "natural" but relatively homogeneous groupings are evident in a statistical population. It is often used in marketing research. In this technique, the total population is divided into these groups (or clusters) and a simple random sample of the groups is selected. Then the required information is collected from a simple random sample of the elements within each selected group. This may be done for every element in these groups or a subsample of elements may be selected within each of these groups.
- 15. Advantages of Sampling Method Reduce Cost :- It is cheaper to collect data from a part of the Whole population and is economically in advance. Greater speed :- Sampling gives more time in collection of data, so it is quickly and has a lot of time for collection of inflammation. Detailed Information : -Investigator during studyng a small universe provides a detail and Comprehensive Information. Practical Method :- Sampling is the only one practical method when the population is Infinite.
- 16. Disadvantages of Sampling Method Careful sampling Selection is difficult. Experts are required for careful study of the universe. If the informations is required for each and every unit in the study then it is difficult to interview each and every person in sampling method.
- 17. Bibliography http://www.studylecturenotes.com/social-research-methodology/advantages- disadvantages-of-sampling-method-of-data-collection. http://en.wikipedia.org/wiki/Cluster_sampling http://cs.fit.edu/~jpmcgee/classes/CSE5800/SamplingTechniques.pdf