sample 7

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 populationsLess costsLess field timeMore 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.

* SAMPLING BREAKDOWN

SAMPLING……. * TARGET POPULATION
STUDY POPULATION
SAMPLE

It should accurately reflect the characteristics of the population from which it is drawn.

Sampling Methods
Sampling involve the selection of a No. of study units from a defined study population

non probability probability

1.Nonprobability methods
Convenience sampling study units are selected because they happen to be available at the time of data collection Members of population are chosen for the sample, or e.g. If a doctor wants to study typhoid he will not study each patient with typhoid, but chose cases that reach him in his clinic.

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


A.1. Simple random sampling: Each member of population has an equal possibility of being chosen for the sample with chance alone responsible for selection of any member can be chosen by table of random number. Simple random sampling ( not haphazard) selected by the following methods: 1. Lottery method. 2. computer generated random sampling. 3. Using the random number table.

Simple random sampling

A.2. Systematically sample: A random starting point at the beginning of sample chosen according to the predominant selection schedule e.g. 100 students are ranked by age then begin with 4th students and every 10th student chosen (4th, 14th, 24th,…).

Systematic sampling

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”.

A.3.Stratified sampling: The population is divided into sampling unites that contain individuals and then a random sample of individuals proportionate to the size of the sampling unites. E.g. in your college four classes then chose 20% from each class.

STRATIFIED SAMPLING……. * Draw a sample from each stratum

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. clusters include e.g. schools, districts, hospitals, villages, clinics, factories….


Cluster sampling
Section 4
Section 5
Section 3
Section 2
Section 1

Multistage sampling carried out in phases and usually involves more than one sampling method . It is used or a very large number of population. E.g. as if we study Iraqi people so we divide them into governments, then districts, village, and so on.

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.

Thank you