1. SAMPLING METHODS
Lecture handouts

2. Sampling fundamentals
Sampling is the process of selecting a number of study units from a defined study population. In other words; it is the process of obtaining information about an entire population by examining only a part of it.
A sample size has to be drawn from a study population.
Population refers to the total of items about which the information is desired.
A list containing all sampling units is called a sampling frame.
Recruitment of the sample must be considered, if the sample consists of volunteers only, it is not likely to be representative of the true population.

3. Sampling fundamentals cont.
A sampling frame consists of a list of items from which the sample is to be drawn.
A sampling design is a definite plan for obtain- ining a sample from the sampling frame.
Sampling remains the only choice when the population contains infinitely many members
It saves time and money
Sample study is usually less expensive than a census study and produces results at a relatively faster speed

4. Questions to be considered
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?
The study population has to be clearly defined, e.g., according to age, sex and residence. Apart from persons, a study population may consist of villages, instructions, records, etc.

5. Sample Representativeness
A representative sample has all the important characteristics of the population from which it is drawn.
A sample should be truly representative of a population characteristics without any bias so that it may result into valid and reliable conclusions.
The generalizations/conclusions drawn have to be based on samples about the parameters of populations from which the samples are taken.

6. Sample representativeness cont.
If many class-groups are to be formed, (groups/sub-groups) a large sample is needed. A small sample might not be able to give a reasonable number of items in each class-group.
For general survey, the size of the sample should be large, but a small sample is considered appropriate in technical surveys.

7. Type of sampling
Sampling technique plays a vital role in determining a sample size.
A small random sample is apt to be much superior to a larger but badly selected sample.
In practice, the sample size depends upon the amount of money available for the study purposes. Therefore, the availability of finance is a very important factor for sample size selection.

8. Probability sampling
This involves using random selection procedures to ensure that each unit of the sample is chosen on the basis of chance(chance sampling).
Simple random sampling
It is an unbiased sampling
Each member of the population has an equal chance of being chosen for the sample.
The sample can be chosen by using a table of random numbers.
Alternatively, random numbers can be generated from computer databases such as excel.
It is considered as the best technique of selecting a representative sample.

9. Simple random sampling cont.
Each individual in the population is numbered and a list of random numbers is drawn from the table.
With the sample size needed determining how many numbers to draw.
This list of numbers represents the individuals chosen for the simple random numbers. This type of simple random sampling method has the disadvantage that small groups in which the researcher is interested may hardly appear in the sample.

10. Systematic sampling
In systematic sampling individuals are chosen at regular intervals(for example every 5th or every 10th ) from the sampling frame.
To start, we randomly select a number to tell us where to start selecting individuals from the list.
For example, a systematic sample is to be selected from 1200 students of a school.
The sample size selected is 100. The sampling fraction is determined first by ; sample size divided by the study population. The sampling interval in this case is therefore 12.(sampling fraction =1/12).

11. Systematic sampling cont.
Systematically selected sample
A random starting point at the beginning of an ordered population is chosen, then the remainder of the sample is chosen according to a pre-determined selection schedule. For example, 100 students are ranked by age.
Beginning with the 4th student, every 10th student is chosen(students numbered 4,14,24, etc.)

12. Systematic sampling cont.
It is the least time-consuming and most convenient way to obtain a sample from an available listing of potential subjects. However, there is risk of bias, as the sampling interval may coincide with a systematic variation in sampling frame, e.g. by selecting a random sample of days on which to count clinic attendance, systematic sampling with a sampling interval of 7 days would be inappropriate, as all study days would fall on the same day of the week(e.g. Tuesdays only, that might be a market day).
It is often used in reviews of hospital records or in studies of health workers.

13. Stratified random sampling
If it is important that the sample includes representative study units of small groups with specific characteristics(for example, residents from urban and rural areas,
Or different religious or ethnic groups, then the sampling frame must be divided into groups, or strata, according to characteristics.
Random or systematic samples of a pre-determined size will then have to be obtained from each group(stratum).

14. Stratified random sampling cont.
It is more likely to produce a more representative sample through stratification than simple random sampling
The stratified sampling approach presents an opportunity to strengthen a research design.
It allows a researcher to get a sample that is big enough to enable him to draw valid conclusions about a relatively small group without having to collect an unnecessarily large(and hence expensive) sample of the other larger groups.

15. Cluster sampling
The selection of groups of study units(clusters) instead of the selection of study units individually is called cluster sampling.
Clusters are often geographic units(e.g., districts, villages, or organizational units(e.g., clinics, training groups.
The population is divided into sampling units, or groups, and a random sample of groups is chosen.
The sample is made up of groups not individuals. For example, in a city all of the residents who live in randomly selected blocks are chosen.

16. Cluster sampling cont.
Cluster sampling is conveniently efficient when dealing with large populations.
It is most frequently used in surveys of widely dispersed populations.
However, this comes at the price of increased sampling error.
This disadvantage can be minimized by choosing as large a sample as possible within each cluster and by stratifying within any stage of sampling.
Cluster samples are commonly used because they are simple and quick.

17. Multi-stage sampling
This type of sampling is often used in community-based studies, in which people are to be interviewed from different villages, and villages are chosen from different areas.
It is frequently used in HSR. E.g., you wish to look at the five leading causes of admission in each of the five hospital services.
It is also used in large and diverse populations.
Sampling may be done in two or more stages.

18. Multi-stage sampling cont.
A multi-stage sampling procedure is carried out in phases and it usually involves more than one sampling method.
The sample is easier to select than a simple random sample of similar size, because the individual units in the sample are physically together in groups, instead of scattered allover the study population.
The main disadvantage of this type of sampling is that; compared to simple random sampling, there is a larger probability that the final sample will not be representative of the total population.

19. Non-probability sampling
Purposeful sampling
Qualitative research methods are typically used when focusing on a limited number of informants, whom we select strategically so that their in-depth information will give optimal insight into an issue about which little is known.
This is called purposeful sampling.
A researcher hand-picks subjects on the basis of specific criteria. For some types of studies, the researcher may be looking for a wide range of characteristics.

20. Non-probability sampling cont.
When using a qualitative research approaches, however, representativeness of the sample is not a primary concern.
You may only aim at getting a rough impression of how certain variables manifest themselves in a study population.
You may also try to select study units which give you the richest possible information. Then;
You go for Information-rich cases!
Personal element has a great chance of entering into the selection of the sample. Thus investigators have to exercise impartiality with necessary experience to take sound judgment for better and reliable results.

21. Bias in sampling
Bias is a deviation of results from the truth, due to way(s) in which the study is conducted.
Bias in sampling is a systematic error in sampling procedures, which leads to a distortion in the results of the study.
Bias can arise from the improper sampling procedures which result in the sample not being representative of the study population.
The most well known source of bias is non-response in an interview, occurs mostly in large-scale surveys with self administered questionnaires.

22. Bias in sampling
Respondents may refuse or forget to fill in the questionnaire.
Sampling of registered patients only
Missing cases of short duration, this may mean missing fatal cases with short illness episodes and mild cases.
Seasonal bias
Tarmac bias
Studying volunteers only.

23. Bias in sampling cont. How to reduce the possibility of bias.
Data collection tools should be pre-tested.
Follow-up of non-respondents may be considered if non-response is due to absence
Note: Not necessarily true that the bigger the sample size, the better the study outcome.
Better the accuracy and richness of data collection, also make extra efforts to get a representative sample rather than to get a very large sample.

24. Sample size determination
Things to bear in mind when considering the sample size are:
The purpose of the study
The population size.
What should be the size of the sample, or how large or small should be (n)?
If the sample size (n) is too small, it may not serve to achieve the objectives, and
If it is too large, may lead into spending huge costs and waste of resources.
Generally, the sample must be of an optimum size, i.e., it should neither be excessively large nor too small.
The sample size should be representative of the study population.
The level of precision, confidence level and variability

25. Strategies for determining sample size
1.Using a census for small populations, e.g., 200 subjects or less. It reduce costs, such as questionnaire design and developing sampling frame are fixed. They will be same for samples of 50 or Using a sample size of a similar study. The same sample size as of studies similar to the one you plan, however, literature review in the respective study can provide guidance about typical sample sizes that are used.

26. Strategies cont.
3. Using published tables. To rely on published tables which provide the sample size for a given set of criteria. 4.Using formulas to calculate a sample size. Tables can provide useful guide to determine the sample size, but you may need to calculate the necessary sample size for a different combination of levels of precision, confidence level and variability. The purpose of a sample size is to gain knowledge about a population using an unbiased representation that can be easily observed and measured. Appropriate sample size is required for validity.

27. Sample size calculation
Sample size calculation depends on:
How much data required to make correct decision on a particular research.
More data collection stands good chance to be more accurate and less error of the parameter estimate.(Doesn’t mean that more is always the better)
The use of statistical formula of sample size.

28. Sample size calculation cont.
The formulae for calculating a desired sample size are divided into two categories, depending on whether the study:
Seeks to measure one single variable(e.g. mean, rate or proportion.
Tries to demonstrate a significant difference between two groups.

29. Sample size calculation cont.
Steps:
1. Estimate how big is the proportion might be(say 80%)
2. Choose the margin of error you allow in the estimate of the proportion(say + or – 10%). This means that, if the survey reveals that indeed 80% of the study population have been let’s say vaccinated, this proportion will probably be btn 70 and 90% in the whole study population from which the sample was drawn.
3.Choose the level of confidence at which you want to be able to state the vaccination coverage in the whole population is indeed between 70 and 90% . You can never be 100% sure. A commonly used confidence level in HSR studies is 95%.

30. Assignment
Train yourselves how to determine/calculate an appropriate sample sizes from various sources pending on the given situation.