4. Sampling Methods

Kamarul Imran MD MCM [email protected]

SAMPLING METHODS

Kamarul Imran Musa

MD MCommMed(Epid&Stat)

Dept of Community Medicine

www.kk.usm.my/jpm

http://www.kk.usm.my/jpm


• Sampling is – a statistical practice – concerned with the selection of individual

observations – intended to yield some knowledge about a population

of concern, especially for the purposes of statistical inference.

http://en.wikipedia.org/wiki/Statistical_practice

http://en.wikipedia.org/wiki/Population_(statistics)

http://en.wikipedia.org/wiki/Statistical_inference


• Sampling frame– is where samples are selected. – It has the property that we can identify every single

element and include any in our sample. • Sampling frames include:

– Disease registry – Drug slips – List of health centers staffs– School children list

http://en.wikipedia.org/wiki/Electoral_register


• The sampling frame must be representative of the population.

• People not in the frame have no prospect of being sampled.

• Statistical theory tells us about the uncertainties in extrapolating from a sample to the frame.

• We extrapolate results from frame to the target population.

• In defining the frame, practical, economic, ethical and technical issues need to be addressed.


Sampling Method: Probability sampling

• Probability sampling involves the selection of a sample from a population, based of chance.

• Probability sampling is more complex, more time-consuming and usually more costly than non-probability sampling.

• However, because units from the population are randomly selected and each unit's probability of inclusion can be calculated, reliable estimates can be produced along with estimates of the sampling error, and inferences can be made about the population.

http://www.statcan.ca/english/edu/power/ch13/non_probability/non_probability.htm


• Probability Samples:– A probability sample is one in which each element of

the population has a known non-zero probability of selection.

– Not a probability sample if probabilities of selection are not known.


1. Simple random sampling

• In simple random sampling, each member of a population has an equal chance of being included in the sample.

• each combination of members of the population has an equal chance of composing the sample.

• To select a simple random sample, you need to list all of the units in the survey population (sampling frame).


• Advantages of this technique – it does not require any additional information on the

frame (such as geographic areas) other than the complete list of members of the survey population along with information for contact.

• although it is easy to apply simple random sampling to small populations, – it can be expensive and unfeasible for large

populations because all elements must be identified and labeled prior to sampling.


Example: To draw a simple random sample from school children

in a primary school, each child (sampling unit) would need to be numbered sequentially.

If there were 1000 children and the sample size needed were 200, then the sampling frame ranges from number 1 and 1000.

Each number will have the same chance of being generated by the computer (in order to fill the simple random sampling requirement of an equal chance for every unit).

Use Random Table or sofware (SPSS, Stata etc) to select the 200 children


2. Systematic sampling

• Sometimes called interval sampling, systematic sampling means that there is a gap, or interval, between each selected unit in the sample.

• In order to select a systematic sample, you need to follow these steps:– Number the units on your frame from 1 to N (where N

is the total population size). – Determine the sampling interval (K) by dividing the

number of units in the population by the desired sample size.


• 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.

• Select every Kth unit after that first number. • For example, 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 the desired sample size).


The advantages of systematic sampling are that the sample selection is easier -you only get one random number—the random start

—and the rest of the sample automatically follow) -the sample is distributed evenly over the listed

population. The biggest drawback of the systematic sampling

method is that if there is some cycle in the way the population is arranged on a list and if that cycle coincides in some way with the sampling interval, the possible samples may not be representative of the population.


3. Stratified sampling

• Using stratified sampling, the population is divided into homogeneous, mutually exclusive groups called strata, and then independent samples are selected from each stratum.

• Any of the sampling methods can be used to sample within each stratum.

• The sampling method can vary from one stratum to another.

http://www.statcan.ca/english/edu/power/glossary/gloss.htm#mutualexclusive


• When simple random sampling is used to select the sample within each stratum, the sample design is called stratified simple random sampling. A population can be stratified by any variable that is available for all units on the sampling frame prior to sampling (e.g., age, sex, province of residence, income, etc.).


Example 6: Suppose you want to estimate how many diabetics with good glucose control at the state level based on gender.

-Stratifying your list by district -Selecti a sample size for each district on the exact

sample size needed for that specific district

District

Samples selected

50 samples will be randomly selectedFrom each district


4. Cluster sampling

• Sometimes it is too expensive to spread a sample across the population as a whole.

• Travel costs can become expensive if interviewers have to survey people from one end of the country to the other.

• To reduce costs, statisticians may choose a cluster sampling technique.

• Cluster sampling divides the population into groups or clusters. A number of clusters are selected randomly to represent the total population, and then all units within selected clusters are included in the sample.


• Example: Imagine that a State Pengarah wants to investigate the characteristics of COAD patients in the states. – First, he/she uses state maps that identify and label

each major hospital in the state.– All major hospitals (sampling unit) are listed as the

sampling frame.– Using the simple random sample approach, he/she

chooses 5 hospitals. All COAD patients in those 5 hospitals make up the survey sample.


5. Multi-stage sampling

• Multi-stage sampling requires at least two stages. • In the first stage, large groups or clusters are identified

and selected. • 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.

• If more than two stages are used, the process of choosing population units within clusters continues until there is a final sample.

http://www.statcan.ca/english/edu/power/ch13/probability/probability.htm


• Example: You wish to determine the pattern of anemic mothers– Stage 1: 5 districts in Kelantan (primary sampling unit)

out of total no. of districts were randomly selected– Stage 2: From each of the 5 districts, 3 health centers

(secondary sampling units) were randomly selected.– Stage 3: 100 pregnant mothers in the each of

selected health centers were selected using random sampling


Non-probability sampling

• The difference between probability and non-probability sampling has to do with a basic assumption about the nature of the population under study.

• In probability sampling, every item has a chance of being selected..

• 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.

http://www.statcan.ca/english/edu/power/glossary/gloss.htm#bias


1. Convenience or haphazard sampling

• selected if subjects can be accessed easily and conveniently.

• Examples of convenience sampling include: – The patients coming to the clinic in the morning were

selected – The first 100 patients to enter pharmaceutical

department were selected


2.Volunteer sampling

• As the term implies, this type of sampling occurs when people volunteer their services for 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 entice respondents. In exchange, the volunteers accept the possibility of a lengthy, demanding or sometimes unpleasant process.


3.Judgment sampling

• This approach is used when a sample is taken based on certain judgments about the overall population.

• The underlying assumption is that the investigator will select units that are characteristic of the population.

• Judgement sampling is subject to the researcher's biases and is perhaps even more biased than haphazard sampling.

• One advantage of judgement sampling is the reduced cost and time involved in acquiring the sample.


4.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.

• The quotas may be based on population proportions. For example, if there are 100 men and 100 women in a population and a sample of 20 are to be drawn to participate in a cola taste challenge, you may want to divide the sample evenly between the sexes—10 men and 10 women.


TABLE OF RANDOM NUMBERS

39634 62349 74088 65564 16379 19713

39153 69459 17986 24537

14595 35050 40469 27478 44526 67331

93365 54526 22356 93208

30734 71571 83722 79712 25775 65178

07763 82928 31131 30196

64628 89126 91254 24090 25752 03091

39411 73146 06089 15630

42831 95113 43511 42082 15140 34733

68076 18292 69486 80468

80583 70361 41047 26792 78466 03395

17635 09697 82447 31405

00209 90404 99457 72570 42194 49043

24330 14939 09865 45906

05409 20830 01911 60767 55248 79253

12317 84120 77772 50103

95836 22530 91785 80210 34361 52228

33869 94332 83868 61672

65358 70469 87149 89509 72176 18103

55169 79954 72002 20582

http://www.mrs.umn.edu/~sungurea/introstat/public/instruction/ranbox/randomcoinsII.html#random


Thank you

Documents

4. Sampling Methods