Sample

Sample

A sample is “a smaller (but hopefully representative) collection of units from a population used to determine truths about that population.
Why sampling?
Get information about large populations
Less costs
Less field time
More accuracy “Gives results with known accuracy that can be calculated mathematically”
When it’s impossible to study the whole population
Target Population:
The population to be studied/ to which the investigator wants to generalize his results
Sampling Unit:
smallest unit from which sample can be selected
Sampling frame
List of all the sampling units from which sample is drawn
Sampling scheme
Method of selecting sampling units from sampling frame
The sample has to be selected to be as representative as possible of the target population, and in enough numbers to provide valid answers.
The term population refers to the material of the study, whether it is human subjects, animals or inanimate objects from the which the samples are taken . Instead of the “target population”, the investigator often depends on the “accessible population”. The accessible population must be representative of the target population, in order to draw conclusions about the target population. It should accurately reflect the characteristics of the population from which it is drawn.
Sampling Methods
non probability
probability


1.Nonprobability methods
Convenience sampling
study units are selected because they happen to be available at the time of data collection
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
Snowball sampling (friend of friend….etc.)
Purposive sampling (judgemental): 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 .

Probability sampling methods

Involve random selection procedures to ensure that each unit of the sample is chosen on the basis of chance. All units of the study population should have an equal or at least a known chance of being included in the sample.
Randomization was commonly done manually using a table of random numbers. Now it is usually done using a computer program

Simple random sampling

requires a numbered list of all units in the population from which one wants to draw a sample , a decision about how many units will be required to produce valid results , and selection of units using a table of random numbers
SYSTEMATIC SAMPLING
relies on arranging the target population according to some ordering scheme and then selecting elements at regular intervals through that ordered list.
Systematic sampling involves a random start and then proceeds with the selection of every kth element from then onwards. In this case, k=(population size/sample size).
It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within the first to the kth element in the list.
A simple example would be to select every 10th name from the telephone directory (an 'every 10th' sample, also referred to as 'sampling with a skip of 10').
Stratified sampling
Stratified random sampling is a special type of sampling to ensure that all subgroups in the accessible population are represented in the sample. This is particularly important if certain subgroups are present in small numbers in the population, or are important to be included. In stratified random sampling, key subgroups are defined, for example by sex, social class, income groups, geographic locations, etc. and samples are drawn at random from each of these “strata”.


Cluster sampling
It is based first on the random selection of certain subgroups, from which the sample can be taken. For example, in a community survey certain streets or blocks are selected at random first. Then a random sample is selected from each randomly selected cluster. In a health services study, a number of districts are randomly selected. Then a random sample of health service units is selected from each
Multistage sampling
carried out in phases and usually involves more than one sampling method .
Problems of sampling
Improper sampling procedures is another occasion for introducing bias (systematic error) into a study that can so distort the critical issue of representativeness as to render the study useless at best-- or dangerous if inappropriate health care changes are based on results.
Common sources of sampling bias
Non-response
studying volunteers only
sampling registered patients only
missing cases of short duration
Ways to deal with this problem and reduce the possibility of bias
Data collection tools
If non response is due to absence of the subjects, follow-up of non respondents may be considered.
If nonresponse is due to refusal to cooperate an extra separate study of non respondents may be considered to discover to what extent they differ from respondents .
To include additional people in the sample.
Sample size is usually a compromise between what is desirable and what is feasible in term of time , manpower transport , money -- for data collection and for analysis of it.