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Jagan Sampling
- 1. Sampling Techniques By: Jagan.S M.Tech URP IDS, UOM, Mysore 1
- 2. Index • Sampling • Need for sampling • Sampling Design • Types of sampling • Probability sampling • Non probability sampling • Sample Size • The differences between Probability and Non-Probability Sampling • Conclusion • References 2
- 3. Sampling • Sampling may be defined as the selection of some part of an aggregate or totality, on the basis of which a judgment or inference about the aggregate or totality is made. • A sample is a finite part of a statistical population whose properties are studied to gain information about the whole(Webster, 1985). •Sampling •Need for sampling •Sampling Design •Types of sampling •Probability sampling •Non probability sampling •Sample Size •The differences between Probability and Non- Probability Sampling •Conclusion •References SAMPLE STUDY POPULATION TARGET POPULATION 3
- 4. Characteristics of Good Samples • Representative • Accessible • Low cost …this (bad)… ...this (VERY bad)… 4 Population Sample Population Sample •Sampling •Need for sampling •Sampling Design •Types of sampling •Probability sampling •Non probability sampling •Sample Size •The differences between Probability and Non- Probability Sampling •Conclusion •References
- 5. Need for sampling • Resources (time, money) and workload can be reduced. • saves usage of work force. • Gives results with known accuracy that can be calculated mathematically. • Provides much better results. • Improvement in workability compare to the whole group. • Coverage is more. • Quality of a study is often better with sampling than with a complete. •Sampling •Need for sampling •Sampling Design •Types of sampling •Probability sampling •Non probability sampling •Sample Size •The differences between Probability and Non- Probability Sampling •Conclusion •References 5
- 6. •Sampling •Need for sampling •Sampling Design •Types of sampling •Probability sampling •Non probability sampling •Sample Size •The differences between Probability and Non- Probability Sampling •Conclusion •References Steps in Sampling Design 1.Define the population 2.Identify the sampling frame 3.Select a sampling design or procedure 4.Determine the sample size 5.Draw the sample 6 Define Population Determine Sampling Frame Determine Sampling Procedure Probability Sampling Type of Procedure Simple Random Sampling Stratified Sampling Cluster Sampling Non-Probability Sampling Type of Procedure Convenience Judgmental Quota Determine Appropriate Sample Size Execute Sampling Design
- 7. Types of sampling Sampling Probability sampling Simple random Systematic random Stratified random Cluster Non-probability sampling Convenience Purposive Quota •Sampling •Need for sampling •Sampling Design •Types of sampling •Probability sampling •Non probability sampling •Sample Size •The differences between Probability and Non- Probability Sampling •Conclusion •References 7
- 8. Probability sampling • Probability sampling is a sampling technique wherein the samples are gathered in a process that gives all the individuals in the population equal chances of being selected. I will be I will be I will be I will be I will be I will be •Sampling •Need for sampling •Sampling Design •Types of sampling •Probability sampling •Non probability sampling •Sample Size •The differences between Probability and Non- Probability Sampling •Conclusion •References 8
- 9. Non probability sampling • Non probability sampling plans are those that provide no basis for estimating how closely the sample characteristics approximate the parameters of the population from which the sample was obtained. • These samples focus on volunteers, easily available units, or those that just happen to be present when the research is done I will be I will be I will be I will be I will be •Sampling •Need for sampling •Sampling Design •Types of sampling •Probability sampling •Non probability sampling •Sample Size •The differences between Probability and Non- Probability Sampling •Conclusion •References 9
- 10. Simple random sampling • Each unit in the population is identified, and each unit has an equal chance of being in the sample. The selection of each unit is independent of the selection of every other unit. Selection of one unit does not affect the chances of any other unit. • A sample selected by randomization method is known as simple random sample and this technique is simple random- sampling. Randomization is a method and is done by using a number of techniques. a)Tossing a coin •Sampling •Need for sampling •Sampling Design •Types of sampling •Probability sampling •Non probability sampling •Sample Size •The differences between Probability and Non- Probability Sampling •Conclusion •References 10
- 11. Systematic random sampling: • Each unit in the population is identified, and each unit has an equal chance of being in the sample. • Systematic sampling relies on arranging the target population according to some ordering scheme and then selecting elements at regular start and then proceeds with the selection of every ‘X’th element from the onwards •Sampling •Need for sampling •Sampling Design •Types of sampling •Probability sampling •Non probability sampling •Sample Size •The differences between Probability and Non- Probability Sampling •Conclusion •References 11
- 12. Stratified random sampling: • Each unit in the population is identified, and each unit has a known, non-zero chance of being in the sample. This is used when the researcher knows that the population has sub-groups (strata) that are of interest. • It is an improvement over the earlier method, when employing this techniques, the researcher divides his population in strata on the basis of some characteristics and from each of these smaller homogenous groups (strata) drawn at random a pre- determined number of Units. •Sampling •Need for sampling •Sampling Design •Types of sampling •Probability sampling •Non probability sampling •Sample Size •The differences between Probability and Non- Probability Sampling •Conclusion •References 12
- 13. Cluster sampling: • Cluster sampling views the units in a population as not only being members of the total population but as members also of naturally-occurring in clusters within the population. • To select the intact group as a whole is known as a cluster sampling. In cluster sampling the sample units contain groups of elements (clusters) instead of individual members or items in the population. •Sampling •Need for sampling •Sampling Design •Types of sampling •Probability sampling •Non probability sampling •Sample Size •The differences between Probability and Non- Probability Sampling •Conclusion •References 13
- 14. Convenience sample Purposive sample Quota sample • Also called an "accidental" sample or "man-in- the-street" samples. The researcher selects units that are convenient, close at hand, easy to reach, etc. • The researcher selects the units with some purpose in mind, for example, students who live in dorms on campus, or experts on urban development. • The researcher constructs quotas for different types of units. For example, to interview a fixed number of shoppers at a mall, half of whom are male and half of whom are female. •Sampling •Need for sampling •Sampling Design •Types of sampling •Probability sampling •Non probability sampling •Sample Size •The differences between Probability and Non- Probability Sampling •Conclusion •References 14
- 15. Snow ball sampling: • Is a technique of building up a list or a sample of a special population by using an initial set of its members as informants. For example a researcher wants to study the problem faced by Indians in another country, Say, he may identify an initial group of Indians through some source like Indian Embassy, Then he can ask each one of them to supply names of other Indians known to them and continue this procedure until he gets an exhaustive list from which he can draw a sample or make a census survey. •Sampling •Need for sampling •Sampling Design •Types of sampling •Probability sampling •Non probability sampling •Sample Size •The differences between Probability and Non- Probability Sampling •Conclusion •References 15
- 16. Determining Sample Size • The size of the sample depends on the type of research design being used; the desired level of confidence in the results; the amount of accuracy wanted; and the characteristics of the population of interest. Sample size has little to do with the size of the population, • What data do you need to consider – Variance or heterogeneity of population – The more heterogeneous a population is, the larger the sample needs to be. – Depends on topic – frequently it occurs? – For probability sampling, the larger the sample size, the better. •Sampling •Need for sampling •Sampling Design •Types of sampling •Probability sampling •Non probability sampling •Sample Size •The differences between Probability and Non- Probability Sampling •Conclusion •References 16
- 17. Sample Size Where: • Z = Z value (e.g. 1.96 for 95% confidence level) p = percentage picking a choice, expressed as decimal (.5 used for sample size needed) c = confidence interval, expressed as decimal (e.g., .04 = ±4) •Sampling •Need for sampling •Sampling Design •Types of sampling •Probability sampling •Non probability sampling •Sample Size •The differences between Probability and Non- Probability Sampling •Conclusion •References 17
- 18. The differences between Probability (Random) Sampling and Non- Probability (Non-Random) Sampling Probability (Random) Sampling Non-Probability (Non-Random) Sampling Allows use of statistics, tests hypotheses Exploratory research, generates hypotheses Can estimate population parameters Population parameters are not of interest Eliminates bias Adequacy of the sample can't be known Must have random selection of units Cheaper, easier, quicker to carry out •Sampling •Need for sampling •Sampling Design •Types of sampling •Probability sampling •Non probability sampling •Sample Size •The differences between Probability and Non- Probability Sampling •Conclusion •References 18
- 19. Conclusion • In conclusion, it can be said that using a sample in research saves mainly on money and time, the sampling is hart of any research if a suitable sampling strategy is used, appropriate sample size selected and necessary precautions taken to reduce on sampling and measurement errors, then a sample should yield valid and reliable information that can used test hypotheses. • Size--was the size adequate for the purpose of the study, especially if there were many sub- groups included in the analysis, or many variables used simultaneously? • Representativeness--was the sample selected randomly from the population, using probability theory? Was the sampling frame adequate? • Implementation--was the sampling plan carried out carefully, was it adequately supervised, was there some quality control plan, did it result in a good response rate? •Sampling •Need for sampling •Sampling Design •Types of sampling •Probability sampling •Non probability sampling •Sample Size •The differences between Probability and Non- Probability Sampling •Conclusion •References 19
- 20. References • Webster, M. (1985). Webster`s nith new collegiate dictionary. Meriam - Webster Inc. Salant, P. and D. A. Dillman (1994). How to conduct your own survey. John Wiley & Sons, Inc. • Patton, M.Q.(1990). Qualitative evaluation and research methods. SAGE Publications. Newbury Park London New Delhi. • Lapin, L. L. (1987). Statistics for mordern business decisions. Harcourt Brace Jovanovich, Inc • Black James A and Champion, Dean J ,John Wiley & Sons Inc, New York p 266-311 • Http://en.wikipedia.org/wiki/sampling (Statistics) • http://www.csulb.edu/~msaintg/ppa696/696samp l.htm • http://www.surveysystem.com/sample-size- formula.htm • https://profiles.uonbi.ac.ke/fridah_mugo/files/mu go02sampling.pdf • Kothari (CR) Research Methodology methods and techniques, 2nd edition, Wishwa Prakashan, New Delhi 2002, P 68-84. •Sampling •Need for sampling •Sampling Design •Types of sampling •Probability sampling •Non probability sampling •Sample Size •The differences between Probability and Non- Probability Sampling •Conclusion •References 20
Editor's Notes
- Probability-based (random) samples Non-probability (non-random) samples:
- For example, to select a sample of 25 people who live in your college dorm, make a list of all the 250 people who live in the dorm. Assign each person a unique number, between 1 and 250. Then refer to a table of random numbers. Starting at any point in the table, read across or down and note every number that falls between 1 and 250. Use the numbers you have found to pull the names from the list that correspond to the 25 numbers you found. These 25 people are your sample. This is called the table of random numbers method.
- For example, to select a sample of 25 dorm rooms in your college dorm, make a list of all the room numbers in the dorm. Say there are 100 rooms. Divide the total number of rooms (100) by the number of rooms you want in the sample (25). The answer is 4. This means that you are going to select every fourth dorm room from the list. But you must first consult a table of random numbers. Pick any point on the table, and read across or down until you come to a number between 1 and 4. This is your random starting point. Say your random starting point is "3". This means you select dorm room 3 as your first room, and then every fourth room down the list (3, 7, 11, 15, 19, etc.) until you have 25 rooms selected.
- For example, if you wanted to find out the attitudes of students on your campus about immigration, you may want to be sure to sample students who are from every region of the country as well as foreign students. Say your student body of 10,000 students is made up of 8,000 - West; 1,000 - East; 500 - Midwest; 300 - South; 200 - Foreign. If you select a simple random sample of 500 students, you might not get any from the Midwest, South, or Foreign. To make sure that you get some students from each group, you can divide the students into these five groups, and then select the same percentage of students from each group using a simple random sampling method. This is proportional stratified random sampling.
- divide population into clusters (usually along geographic boundaries) randomly sample clusters measure units within sampled clusters eg:- Rather than listing all elementary school children in a given city and random selecting 15 per cent these students for the sample, a researcher lists all of the elementary schools in the city, selects at random 15 percent of these clusters of units, and uses all of the children in the selected schools as the sample.