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Sampling Prepared by Dr. Manal Moussa. Sampling Prepared by Dr. Manal Moussa.

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Presentation on theme: "Sampling Prepared by Dr. Manal Moussa. Sampling Prepared by Dr. Manal Moussa."— Presentation transcript:


2 Sampling Prepared by Dr. Manal Moussa

3 Outlines: Introduction Sampling terms Types of Sampling -Probability 1-Simple random sample 2-Stratified random sample 3-Cluster sampling 4-Systematic sampling -Non- probability 1-Convenience sampling 2-Quota sampling 3-Purposive or judgmental sampling

4 Introduction:- The first two questions most researchers ask
once a research has been defined are '' how many subjects will I need to complete my study?'' and '' how will I select them?''.

5 Sampling terms:- Sampling: - is a process of selection a portion of the population to obtain data regarding a problem. Population: - is a group of subjects having common characteristics. The population may consist of events, animals, places, and individuals. The target population: - is the population under study which the researcher wants to generalize the research findings.

6 Types of Sampling:- There are two types of sampling probability & non-probability. Probability sampling includes the following:- 1-Simple random sample. 2-Stratified random sample. 3-Cluster sampling. 4-Systematic sampling.

7 Non-probability sampling includes the following:-
1- Convenience sampling. 2- Purposive sampling. 3- Quota sampling.

8 Probability Sampling:-
Probability sampling: is a process of selecting a representative sample of the target population. Its purpose is to ensure that each element has an equal, and independent.

9 *Four types of probability sampling designs are:-
1- Simple Random Sample:- The simplest form of random sampling is called simple random sample. It is characterized by:- An independent chance for each element to be selected into the study. A complete list of the accessible population. A one-stage selection process. To start, the researcher identifies the target population. Then list of all elements of the target population (sampling frame), the next task is randomly selected the participants.

10 Methods of collection:-
1-Placing the number in a bowl and drawing out the numbers one at time. 2-Using a table of random members. 3-Using a computer –generated selection of random members.

11 Advantages of simple random sampling:-
It eliminates researcher bias. Require limited knowledge about population. Provides a means for estimating sampling error.

12 Disadvantages of simple random sampling:-
A complete list of the accessible population is needed. Very time consuming unless the computer is used to assist in the process. Need a large sample size.

13 2- Stratified Random Sampling:-
In stratified sampling, subjects are chosen so that certain subgroups (two or more strata) in the target population will be represented in the sample. Strata are determined by mutually exclusive variables such as gender, age, and educational level. After the population divided, a simple random sample is taken within each stratum. It is used to ensure representatives of different groups within the population.

14 Advantages of stratified random sample:-
Ensure the representation of a particular segment of population. Disadvantages of stratified random sample:- It requires extensive knowledge of the population under the study to stratify it accurately. A complete list of a target population is needed. It can be quickly but very complex and time consuming.

15 3- Systematic Sampling:-
It refers to a sampling strategy that involves the selection of every case drawn from a population list at fixed interval. First; the listing the population must be random in relation to the variable of interest .Systematic random sampling can be conducted when an ordered list of all members of the population is available.

16 Second; the first element or member of the
sample must be selected randomly. The sampling interval; is the standard distance between the elements chosen for the sample.

17 Advantages of systematic random sample:-
It is a fast, easy, and inexpensive way to draw a probability sample. Its simplicity may be reducing error.

18 Disadvantages of systematic random sample:-
Not suitable in large or small number of population. Some time we can't obtain required sample size.

19 4- Cluster Sampling:- In cluster sampling, a sampling frame is developed that includes a list of all the states, cities, institutions with which elements of the identified population would be linked.

20 A randomized sample of these states, cities,
institutions, or organizations would then be used in the study. In some cases, this randomized selection continues through several stages and is then referred to as '' multistage sampling''.

21 For ex. The researcher might first randomly
select states, then randomly select cities, within the sample states, then randomly select hospitals within selected cities, then randomly select the patient on nursing unit who fit the criteria for the study within selected hospitals.

22 Advantages of cluster sample:-
It is economical. It is particularly when the population is large and geographically dispersed.

23 Disadvantages:- More sampling errors tend to occur than other type of sampling. The appropriate handling of the statistical data from cluster sampling is very complex.

24 Non-probability Sampling:-
Non-probability sampling; is an alternative approach to probability sampling , because random selection is not used, the sample may not be representative of a large population , and thus the results can't be generalized beyond the sample study.

25 There are three types are:-
1- Convenience Sampling:- Is the uses participant who are easily accessible to the researcher and who meet the criteria of the study. For ex. Take all patient admitted in surgical department with a particular diagnosis during this month.

26 Advantages:- It easier for researcher to obtain subjects. Save in time and money. Disadvantages:- The potential for sampling bias. The use of a sample that may not represent population. The limited generalization of the results.

27 2- Quota Sampling:- Quota sampling is similar to stratified random sampling in that participants are divided into strata based on specific characteristics to provide representativeness of different groups within the population. Quota sampling differs from stratified random sampling is that the participants are not randomly selected from each strata.

28 3- Purposive or Judgmental Sampling:-
It involves the conscious selection by the researcher of certain subjects or elements to include the study. A purposive sampling is used also when a highly unusual group is being studied such as a population with a rare genetic disease. It used to describe lived experience of particular phenomena (postpartum depression).

29 Advantages:- Its allowance for the researcher to '' hand-pick'' the sample based on knowledge of the phenomena of the study.

30 Disadvantages:- The potential for sampling bias. Uses of sample that dose not represent the population. The very limited to generalization of results.


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