SAMPLING_ used for resrnheg AND_ITS_TYPE.pptx

Sampling technical

1. SAMPLING AND ITS TYPES By Dr . Anil Kumar A.P . (MAE) DSEU , New Delhi

2. SAMPLING Target Population or Universe The population to which the investigator wants to generalize his results Sampling Unit: smallest unit from which sample can be selected Sampling frame The sampling frame is the list from which the potential respondents are drawn Telephone directory List of five star Hotel List of student Sampling scheme Method of selecting sampling units from sampling frame Sample: all selected respondent are sample

3. SAMPLE TARGET POPULATION SAMPLE UNIT SAMPLE A population can be defined as including all people or items with the characteristic one wishes to understand. Because there is very rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample (or subset) of that population.

4. SAMPLING BREAKDOWN All university in India All university Haryana List of Haryana university Three university in Haryana

5. Why Sample ? Get information about large populations Lower cost More accuracy of results High speed of data collection Availability of Population elements. Less field time When it’s impossible to study the whole population

6. SAMPLING……. 3 factors that influence sample representativeness- Sampling procedure Sample size Participation (response) When might you sample the entire population? When your population is very small When you have extensive resources When you don’t expect a very high response

7. The sample must be: 1. representative of the population; 2. appropriately sized (the larger the better); 3. unbiased ; 4. random (selections occur by chance); What is Good Sample? Merits of Sampling Size of population Fund required for the study Facilities Time

8. 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. Types of Sampling

9. Probability (Random) Samples Simple random sample Systematic random sample Stratified random sample Cluster sample

10. Non-Probability Samples Convenience samples (ease of access) sample is selected from elements of a population that are easily accessible Purposive sample (Judgmental Sampling) You chose who you think should be in the study Quota Sampling Snowball Sampling (friend of friend….etc.)

11. Difference between Probability sampling and Non Probability

12. 1. SIMPLE RANDOM SAMPLING Applicable when population is small, homogeneous & readily available All subsets of the frame are given an equal probability. Each element of the frame thus has an equal probability of selection. A table of random number or lottery system is used to determine which units are to be selected. Advantage Easy method to use No need of prior information of population Equal and independent chance of selection to every element Disadvantages If sampling frame large, this method impracticable. Does not represent proportionate reprenation

13. Simple random sampling Every subset of a specified size n from the population has an equal chance of being selected

14. Suitability This method is suitable for small homogeneous Randomly selecting units from a sampling frame. ‘ Random ’ means mathematically each unit from the sampling frame has an equal probability of being included in the sample. Stages in random sampling:

15. REPLACEMENT OF SELECTED UNITS Sampling schemes may be without replacement or with replacement For example, if we catch fish, measure them, and immediately return them to the water before continuing with the sample, this is a with replacement design, because we might end up catching and measuring the same fish more than once. However, if we do not return the fish to the water (e.g. if we eat the fish), this becomes a without replacement design.

16. Similar to simple random sample. No table of random numbers – select directly from sampling frame. Ratio between sample size and population size Systematic Sampling

17. ADVANTAGES: Sample easy to select Suitable sampling frame can be identified easily Sample evenly spread over entire reference population Cost effective DISADVANTAGES: Sample may be biased if hidden periodicity in population coincides with that of selection. Each element does not get equal chance Ignorance of all element between two n element 2. Systematic Sampling

18. Systematic sampling Every member ( for example: every 20th person) is selected from a list of all population members.

19. 3. Stratified Random Sample The population is divided into two or more groups called strata, according to some criterion, such as geographic location, grade level, age, or income, and subsamples are randomly selected from each strata.

20. STRATIFIED SAMPLING…… Advantage : Enhancement of representativeness to each sample Higher statistical efficiency Easy to carry out Disadvantage: Classification error Time consuming and expensive Prior knowledge of composition and of distribution of population

21. 4. CLUSTER SAMPLING Cluster sampling is an example of 'two-stage sampling' . First stage a sample of areas is chosen; Second stage a sample of respondents within those areas is selected. Population divided into clusters of homogeneous units, usually based on geographical contiguity. Sampling units are groups rather than individuals. A sample of such clusters is then selected. All units from the selected clusters are studied. The population is divided into subgroups (clusters) like families. A simple random sample is taken of the subgroups and then all members of the cluster selected are surveyed

22. Cluster sampling Section 4 Section 5 Section 3 Section 2 Section 1

23. CLUSTER SAMPLING……. Advantages : Cuts down on the cost of preparing a sampling frame. This can reduce travel and other administrative costs. Disadvantages: sampling error is higher for a simple random sample of same size. Often used to evaluate vaccination coverage in EPI

24. 1. Non Probability CONVENIENCE SAMPLING Sometimes known as grab or opportunity sampling or accidental or haphazard sampling. Selection of whichever individuals are easiest to reach It is done at the “convenience” of the researcher For example, if the interviewer was to conduct a survey at a shopping center early in the morning on a given day, the people that he/she could interview would be limited to those given there at that given time, which would not represent the views of other members of society in such an area, if the survey was to be conducted at different times of day and several times per week. This type of sampling is most useful for pilot testing. In social science research, snowball sampling is a similar technique, where existing study subjects are used to recruit more subjects into the sample.

25. Advantage: A sample selected for ease of access, immediately known population group and good response rate. Disadvantage: cannot generalise findings (do not know what population group the sample is representative of) so cannot move beyond describing the sample. Problems of reliability Do respondents represent the target population Results are not generalizable Convenience Sampling Use results that are easy to get

26. 2. Judgmental sampling or Purposive sampling The researcher chooses the sample based on who they think would be appropriate for the study. This is used primarily when there is a limited number of people that have expertise in the area being researched Selected based on an experienced individual’s belief Advantages Based on the experienced person’s judgment Disadvantages Cannot measure the representativeness of the sample

27. 3. QUOTA SAMPLING The population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. Then judgment used to select subjects or units from each segment based on a specified proportion. For example, an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60. It is this second step which makes the technique one of non-probability sampling. In quota sampling the selection of the sample is non-random. For example interviewers might be tempted to interview those who look most helpful. The problem is that these samples may be biased because not everyone gets a chance of selection. This random element is its greatest weakness and quota versus probability has been a matter of controversy for many years

28. Quota sampling Based on pre-specified quotas regarding demographics, attitudes, behaviors, etc Advantages Contains specific subgroups in the proportions desired May reduce bias easy to manage, quick Disadvantages Dependent on subjective decisions Not possible to generalize only reflects population in terms of the quota, possibility of bias in selection, no standard error

29. 4. Snowball Sampling Useful when a population is hidden or difficult to gain access to. The contact with an initial group is used to make contact with others. Respondents identify additional people to included in the study The defined target market is small and unique Compiling a list of sampling units is very difficult Advantages Identifying small, hard-to reach uniquely defined target population Useful in qualitative research access to difficult to reach populations (other methods may not yield any results). Disadvantages Bias can be present Limited generalizability not representative of the population and will result in a biased sample as it is self-selecting.

30. The larger the sample size the more likely error in the sample will decrease. But, beyond a certain point increasing sample size does not provide large reductions in sampling error. Accuracy is a reflection of the sampling error and confidence level of the data. Sampling Error and Confidence

31. Errors in Sampling Non-Observation Errors Sampling error: naturally occurs Coverage error: people sampled do not match the population of interest Underrepresentation Non-response: won’t or can’t participate

32. Errors of Observation Interview error: interaction between interviewer and person being surveyed Respondent error : respondents have difficult time answering the question Measurement error : inaccurate responses when person doesn’t understand question or poorly worded question Errors in data collection

33. Thank You