Lecture 4 Sampling Techniques.ppt
- 1. Arba-Minch University College of Medicine and Health sciences Department of Public Health Types and Techniques of Sampling By: Marelign Tilahun (Assistant Professor, MPH in Epidemiology & Biostatistics)
- 3. What is Sampling? Why Sampling? Types of Sampling? Advantage and disadvantage of each sampling technique? When to use each sampling? Sample size estimation? Sampling & non-sampling error? How to reduce sampling error?
- 4. For each of the following research questions identify the appropriate specific sampling technique a. Contraceptive use among women in the reproductive age group in Arba Minch town b. Malaria infection among under five children in Kebelle “X” in Arba Minch town c. Malnutrition among under 5 children in Amhara region. d. Factors associated with Age at first sexual initiation among youths visiting HIV testing and counseling centers in Gamo-Gofa Zone, South West Ethiopia
- 5. Selecting a sampling method Population to be studied – Size/geographical distribution – Heterogeneity with respect to variable Availability of list of sampling units Level of precision required Resources available
- 6. Sampling is a procedure by which some members of the given population are selected as representative of the entire population The population is too large for us to consider collecting information from all its members. Instead we select a sample of individuals hoping that the sample is representative of the population.
- 7. Types of sampling I. Probability sampling probability sampling method is any method of sampling that utilizes some form of random selection. Every individual of the target population has equal chance to be included in the sample. Generalization is possible (from sample to population) A sampling frame exists or can be compiled. Involve random selection procedures.
- 8. A) Simple random sampling (SRS) This is the most basic scheme of random sampling. Each unit in the sampling frame has an equal chance of being selected Representativeness of the sample is ensured. However, it is costly to conduct SRS. Moreover, minority subgroups of interest in the population my not be present in the sample in sufficient numbers for study.
- 9. B) Systematic Sampling • Individuals are chosen at regular intervals (for example, every kth) from the sampling frame. • The first unit to be selected is taken at random from among the first k units. • For example, a systematic sample is to be selected from 1200 students of a school. The sample size is decided to be 100. • The sampling fraction is: 100 /1200 = 1/12. • Hence, the sample interval is 12.
- 10. • The number of the first student to be included in the sample is chosen randomly, for example by blindly picking one out of twelve pieces of paper, numbered 1 to 12. • If number 6 is picked, every twelfth student will be included in the sample, starting with student number 6, until 100 students are selected. • The numbers selected would be 6,18,30,42,etc
- 11. Merits • Systematic sampling is usually less time consuming and easier to perform than simple random sampling. • It provides a good approximation to SRS. • Unlike SRS, systematic sampling can be conducted without a sampling frame (useful in some situations where a sampling frame is not readily available). • E.g. In patients attending a health center, where it is not possible to predict in advance who will be attending
- 12. Demerits • If there is any sort of cyclic pattern in the ordering of the subjects which coincides with the sampling interval, the sample will not be representative of the population. Examples - List of married couples arranged with men's names alternatively with the women's names (every 2nd, 4th, etc.) will result in a sample of all men or women).
- 13. C) Stratified Sampling • It is appropriate when the distribution of the characteristic to be studied is strongly affected by certain variable (heterogeneous population). • The population is first divided into groups (strata) according to a characteristic of interest (eg., sex, geographic area, prevalence of disease, etc.). • A separate sample is then taken independently from each stratum, by simple random or systematic sampling.
- 14. Merit • The representativeness of the sample is improved. • That is, adequate representation of minority subgroups of interest can be ensured by stratification and by varying the sampling fraction between strata as required. Demerit • Sampling frame for the entire population has to be prepared separately for each stratum.
- 15. D) Cluster sampling • In this sampling scheme, selection of the required sample is done on groups of study units (clusters) instead of each study unit individually. • The sampling unit is a cluster, and the sampling frame is a list of these clusters. Procedure • The reference population (homogeneous) is divided into clusters. • These clusters are often geographic units (eg districts, villages, etc.) • A sample of such clusters is selected • All the units in the selected clusters are studied
- 16. Merit • A list of all the individual study units in the reference population is not required. • It is sufficient to have a list of clusters. Demerit • It is based on the assumption that the characteristic to be studied is uniformly distributed throughout the reference population, which may not always be the case. • Hence, sampling error is usually higher than for a simple random sample of the same size.
- 17. E) Multi-stage sampling • This method is appropriate when the reference population is large and widely scattered. • Selection is done in stages until the final sampling unit (e.g. households or persons) are arrived at. • The primary sampling unit (PSU) is the sampling unit (usually large size) in the first sampling stage. • The secondary sampling unit (SSU) is the sampling unit in the second sampling stage, etc. • Example - The PSUs could be kebeles and the SSUs could be households.
- 18. II. Non-probability sampling No random selection (unrepresentative of the given population) Used when a sampling frame does not exist Inappropriate if the aim is to measure variables and generalize findings obtained from a sample to the population. They are cheaper and easier. Good for pretests, pilot studies, In-depth interviews
- 19. Snowball: A key informant is identified first and the key informant in turn identifies members of the target group. The process continues until the required size is achieved Purposive: Selection of subjects on the basis of your knowledge of the population, its elements and the nature of the research objective Convenience: Selection of a sample based on easy accessibility. The study units that happen to be available at the time of data collection are selected – friends, workmates Quota: selection of samples based on a fixed quota. Investigator interviews as many people as he can find until he has filled his quota.
- 20. Sampling error(random error) When we take a sample, our results will not exactly equal the correct results for the whole population. That is, our results will be subject to errors. This error has two components: sampling and non-sampling errors a) Sampling error (random error) Consists of random deviations from the true value which can occur in any direction The opposite of reliability (i.e., Precision or repeatability)
- 21. Sampling error(random error) Sampling error (random error) can be minimized by increasing the size of the sample Reliability or precision: This refers to the repeatability of a measure, i.e., the degree of closeness between repeated measurement of the same value. Reliability addresses the question, if the same thing is measured several times, how close are the measurements to each other?
- 22. b) Non Sampling error (bias) b) Non Sampling error (bias) Consists of systematic deviations from the true value, always in the same direction. Bias is the opposite of validity It is possible to eliminate or reduce the non-sampling error (bias) by careful design of the sampling procedure. Validity: This refers to the degree of closeness between a measurement and the true value of what is being measured. Validity addresses the question, how close is the measured value to the true value?
- 23. b) Non Sampling error (bias) Examples of types of bias in sampling include: Accessibility bias, seasonability bias, self-reporting bias, volunteer bias, non-response bias etc. Non-response bias refers to failure to obtain information on some of the subjects included in the sample to be studied. Non-response should be kept to a minimum. E.g. below 15%
- 24. Methods of maintaining non-response at a low level: Training data collectors to initiate contact with study subjects in a respectful way Offering incentives to encourage participation(should be done by taking account of the potential problems that may arise in conducting future research) By making repeated attempts (at least 3 times) to contact study subjects who were absent at the time of the initial visit.
- 25. Sample Size Determination: Single population proportion Let p denotes proportion of success, then