Sampling Methods for health research.ppt
- 2. Sampling • The process of selecting a portion of the population to represent the entire population. • A main concern in sampling: – Ensure that the sample represents the population, and – The findings can be generalized.
- 3. • Researchers often use sample survey methodology to obtain information about a larger population by selecting and measuring a sample from that population. • Since population is too large, we rely on the information collected from the sample. • Cost minimization
- 4. • However, due to the variability in the characteristics of the population, scientific sample designs should be applied to select a representative sample. • If not, there is a high risk of distorting the view of the population. • A sample is a collection of individuals selected from a larger population. • Sampling enables us to estimate the characteristic of a population by directly observing a portion of the population.
- 6. • While selecting a SAMPLE, there are basic questions: – What is the group of people (STUDY POPULATION) from which we want to draw a sample? – How many people do we need in our sample? – How will these people be selected?
- 7. • Reference population (or target population): the population of interest to whom the researchers would like to make generalizations. • Sampling/study population: the subset of the target population from which a sample will be drawn. • Sample: the actual group in which the study is conducted • Study unit: the units on which information will be collected: persons, housing units, etc.
- 8. Researchers are interested to know about factors associated with ART use among HIV/AIDS patients attending certain hospitals in a given Region Target population = All ART patients in the Region Sampling population = All ART patients in, e.g. 3, hospitals in the Region Sample
- 9. Advantages of sampling: • Feasibility: Sampling may be the only feasible method of collecting information. • Reduced cost: Sampling reduces demands on resource such as finance, personnel, and material. • Greater accuracy: Sampling may lead to better accuracy of collecting data • Sampling error: Precise allowance can be made for sampling error • Greater speed: Data can be collected and summarized more quickly
- 10. Disadvantages of sampling: • There is always a sampling error. • Sampling may create a feeling of discrimination within the population. • Sample-to-sample variation causes sampling error ↑ Sample size ≡ ↑ Precision ≡ ↑ Cost
- 11. Errors in sampling 1) Sampling error: Errors introduced due to errors in the selection of a sample. – They cannot be avoided or totally eliminated. 2) Non-sampling error: - Observational error - Respondent error - Lack of preciseness of definition - Errors in editing and tabulation of data
- 12. Sampling Methods Two broad divisions: A. Probability sampling methods B. Non-probability sampling methods
- 13. A. Probability sampling • Involves random selection of a sample • Every sampling unit has a known and non-zero probability of selection into the sample. • Involves the selection of a sample from a population, based on chance.
- 14. • Probability sampling is: – more complex, – more time-consuming and – usually more costly than non-probability sampling. • However, because study samples are randomly selected and their probability of inclusion can be calculated, – reliable estimates can be produced and – inferences can be made about the population.
- 15. There are several different ways in which a probability sample can be selected. The method chosen depends on a number of factors, such as – the available sampling frame, – how spread out the population is, – how costly it is to survey members of the population
- 16. Most common probability sampling methods 1. Simple random sampling 2. Systematic random sampling 3. Stratified random sampling 4. Cluster sampling 5. Multi-stage sampling
- 17. 1. Simple random sampling • The required number of individuals are selected at random from the sampling frame, a list or a database of all individuals in the population • Each member of a population has an equal chance of being included in the sample.
- 18. • To use a SRS method: Make a numbered list of all the units in the population Each unit should be numbered from 1 to N (where N is the size of the population) Select the required number. • Use of “lottery’ methods • Table of random numbers • Computer programs
- 19. • SRS has certain limitations: – Requires a sampling frame. – Difficult if the reference population is dispersed. – Minority subgroups of interest may not be selected.
- 20. 2. Systematic random sampling • Sometimes called interval sampling • Selection of individuals from the sampling frame systematically rather than randomly • Individuals are taken at regular intervals down the list • The starting point is chosen at random
- 21. • Important if the reference population is arranged in some order: – Order of registration of patients – Numerical number of house numbers – Student’s registration books • Taking individuals at fixed intervals (every kth ) based on the sampling fraction, eg. if the sample includes 20%, then every fifth.
- 22. Steps in systematic random sampling 1. Number the units on your frame from 1 to N (where N is the total population size). 2. Determine the sampling interval (K) by dividing the number of units in the population by the desired sample size. 3. Select a number between one and K at random. This number is called the random start and would be the first number included in your sample. 4. Select every Kth unit after that first number Note: Systematic sampling should not be used when a cyclic repetition is inherent in the sampling frame.
- 23. Example To select a sample of 100 from a population of 400, you would need a sampling interval of 400 ÷ 100 = 4. Therefore, K = 4. You will need to select one unit out of every four units to end up with a total of 100 units in your sample. Select a number between 1 and 4 from a table of random numbers.
- 24. If you choose 3, the third unit on your frame would be the first unit included in your sample; The sample might consist of the following units to make up a sample of 100: 3 (the random start), 7, 11, 15, 19...395, 399 (up to N, which is 400 in this case).
- 25. 3. Stratified random sampling • It is done when the population is known to be have heterogeneity with regard to some factors and those factors are used for stratification • Using stratified sampling, the population is divided into homogeneous, mutually exclusive groups called strata, and • A population can be stratified by any variable that is available for all units prior to sampling (e.g., age, sex, province of residence, income, etc.). • A separate sample is taken independently from each stratum. • Any of the sampling methods mentioned in this section (and others that exist) can be used to sample within each stratum.
- 26. Why do we need to create strata? • It can make the sampling strategy more efficient. • A larger sample is required to get a more accurate estimation if a characteristic varies greatly from one unit to the other. • For example, if every person in a population had the same salary, then a sample of one individual would be enough to get a precise estimate of the average salary. • Stratified sampling ensures an adequate sample size for sub-groups in the population of interest. • When a population is stratified, each stratum becomes an independent population and you will need to decide the sample size for each stratum.
- 27. • Equal allocation: – Allocate equal sample size to each stratum • Proportionate allocation: – nj is sample size of the jth stratum – Nj is population size of the jth stratum – n = n1 + n2 + ...+ nk is the total sample size – N = N1 + N2 + ...+ Nk is the total population size n n N N j j
- 28. Example: Proportionate Allocation • Village A B C D Total • HHs 100 150 120 130 500 • S. size ? ? ? ? 60
- 29. 4. Cluster sampling • Sometimes it is too expensive to carry out SRS • Cluster sampling is the most widely used to reduce the cost • The clusters should be homogeneous, unlike stratified sampling where the strata are heterogeneous • Cluster sampling divides the population into groups or clusters
- 30. Steps in cluster sampling A number of clusters are selected randomly to represent the total population, and then all units within selected clusters are included in the sample. This differs from stratified sampling, where some units are selected from each group. In a school based study, we assume students of the same school are homogeneous We can select randomly sections and include all students of the selected sections only
- 31. Advantages • Cost reduction • Sometimes a list of all units in the population is not available, while a list of all clusters is either available or easy to create. • Disadvantages • Creates a loss of efficiency when compared with SRS. • It is usually better to survey a large number of small clusters instead of a small number of large clusters.
- 32. 5. Multi-stage sampling • Similar to the cluster sampling, except that it involves picking a sample from within each chosen cluster, rather than including all units in the cluster. • This type of sampling requires at least two stages. • The primary sampling unit (PSU) is the sampling unit in the first sampling stage. • The secondary sampling unit (SSU) is the sampling unit in the second sampling stage, etc.
- 34. • In the first stage, large groups or clusters are identified and selected. These clusters contain more population units than are needed for the final sample. • In the second stage, population units are picked from within the selected clusters (using any of the possible probability sampling methods) for a final sample. • However, multi-stage sampling still saves a great amount of time and effort by not having to create a list of all the units in a population.
- 35. • If more than two stages are used, the process of choosing population units within clusters continues until there is a final sample. • With multi-stage sampling, you still have the benefit of a more concentrated sample for cost reduction.
- 36. B. Non-probability sampling • In non-probability sampling, every item has an unknown chance of being selected. • In non-probability sampling, there is an assumption that there is an even distribution of a characteristic of interest within the population. • For probability sampling, random is a feature of the selection process. • This is what makes the researcher believe that any sample would be representative and because of that, results will be accurate.
- 37. • In non-probability sampling, since elements are chosen arbitrarily, there is no way to estimate the probability of any one element being included in the sample. • Also, no assurance is given that each item has a chance of being included, making it impossible either to estimate sampling variability or to identify possible bias • Reliability cannot be measured in non-probability sampling; the only way to address data quality is to compare some of the survey results with available information about the population. • Still, there is no assurance that the estimates will meet an acceptable level of error.
- 38. Advantage • Secondly, they are quick, inexpensive and convenient. • There are also other circumstances, such as researches, when it is unfeasible or impractical to conduct probability sampling.
- 39. The most common types of non-probability sampling 1. Convenience or haphazard sampling 2. Quota sampling 3. Volunteer sampling 4. Snowball sampling technique 5. Judgment sampling
- 40. 1. Convenience or haphazard sampling • Convenience sampling is sometimes referred to as haphazard or accidental sampling. • It is not normally representative of the target population because sample units are only selected if they can be accessed easily and conveniently. • The obvious advantage is that the method is easy to use, but that advantage is greatly offset by the presence of bias. • Although useful applications of the technique are limited, it can deliver accurate results when the population is homogeneous.
- 41. • For example, a scientist could use this method to determine whether a lake is polluted or not. • Assuming that the lake water is well-mixed, any sample would yield similar information. • A scientist could safely draw water anywhere on the lake without bothering about whether or not the sample is representative
- 42. 2. Volunteer sampling • As the term implies, this type of sampling occurs when people volunteer to be involved in the study. • In psychological experiments or pharmaceutical trials (drug testing), for example, it would be difficult and unethical to enlist random participants from the general public. • In these instances, the sample is taken from a group of volunteers. • Sometimes, the researcher offers payment to attract respondents.
- 43. • In exchange, the volunteers accept the possibility of a lengthy, demanding or sometimes unpleasant process. • Sampling voluntary participants as opposed to the general population may introduce strong biases. • Often in opinion polling, only the people who care strongly enough about the subject tend to respond.
- 44. 3. Quota sampling • This is one of the most common forms of non- probability sampling. • Sampling is done until a specific number of units (quotas) for various sub-populations have been selected. • Since there are no rules as to how these quotas are to be filled, quota sampling is really a means for satisfying sample size objectives for certain sub- populations.
- 45. • The main argument against quota sampling is that it does not meet the basic requirement of randomness. • Some units may have no chance of selection or the chance of selection may be unknown. • Quota sampling is generally less expensive than random sampling. • It is also easy to administer, • is an effective sampling method when information is urgently required and can be conducted without sampling frames.
- 46. 3. Snowball sampling • A technique for selecting a research sample where existing study subjects recruit future subjects from among their acquaintances. • Thus the sample group appears to grow like a rolling snowball. • This sampling technique is often used in hidden populations which are difficult for researchers to access; example populations would be drug users or commercial sex workers.
- 47. Thank you
Editor's Notes
- #18: The randomness of the sample is ensured by:
- #26: This is the idea behind the efficiency gain obtained with stratification. If you create strata within which units share similar characteristics (e.g., income) and are considerably different from units in other strata (e.g., occupation, type of dwelling) then you would only need a small sample from each stratum to get a precise estimate of total income for that stratum. Then you could combine these estimates to get a precise estimate of total income for the whole population. If you use a SRS approach in the whole population without stratification, the sample would need to be larger than the total of all stratum samples to get an estimate of total income with the same level of precision.
- #29: Population may be large and scattered. Complete list of the study population unavailable Travel costs can become expensive if interviewers have to survey people from one end of the country to the other.
- #31: It is usually better to survey a large number of small clusters instead of a small number of large clusters. This is because neighboring units tend to be more alike, resulting in a sample that does not represent the whole spectrum of opinions or situations present in the overall population
- #46: Because sample members are not selected from a sampling frame, snowball samples are subject to numerous biases. For example, people who have many friends are more likely to be recruited into the sample.