TOPIC: TYPES OF

RANDOM SAMPLING

POPULATION;

• Including all peoples or items with the characteristics on wishes to understand.

• Research population is also known as a well define collection of individuals or objects known to have similar characteristics.

Lets understand concepts

sampleStudy populationTarget population

STATISTICAL APPROACH;

• Sampling is the statistical approach used in every field for the purpose of collecting information and on the basis of this information deduction about the trait of population can made (chaudhry.2008)

SAMPLING;

“ A sampling is a process of selecting a number of individuals for a study in such a way that the individuals represent the large group from which they are selected”.

“ Group of people who take part in the investigation”. The people who take part are referred to as “participants”.

Sampling is the process of selecting a group of subject for a studying such a way that the individuals represent the large group from which they were selected. This representative portion of population is called sample.

WHY NOT STUDY EVERY ONE?

• In research we are interested in learning about large group of people who all have something in common. We call the group that we are interested in studying our target population. In some type of research the target population might be a smaller group such as teenager, per school children. It is more or less impossible to study every person in a target population so researchers select a sample or sub group of the population that is likely to the representative of the target population we are interested in . Group is geographically scattered it result in considerable expenditure, time, money and effort.

Types of sampling

RANDOM SAMPLING

NON RANDOM SAMPLIG

RANDOM SAMPLING

• The term random means that each unit (individual)in the selected population has the equal chance of selection and selection of one individual in no way affects selection of another individual.

• The key to random selection is that there is no bias involved in the selection of sample. Sampling bias refer to the situation where the sample does not reflect the characteristics of the target population.

• Any variation between the sample characteristics and the population is only a matter of chance .

EXPLAINATION THROUGH EXAMPLE;

• This is similar to national lottery. If the population is every one who has bought the lottery ticket, than each person has an equal chance of winning the lottery assuming they all have one ticket.

Random sampling requires a way of naming or numbering the target population and then using some type of referral to choose those to make the sample. Random sampling is the best method of selecting sample from population of interest.

RANDOM SAMPLING

SUB TYPES

CLUSTER RANDOM SAMPLING

SYSTEMATIC RANDOM SAMPLING

CLUSTER SAMPLING;

• Cluster sampling is sampling in which groups, not individuals, are randomly selected. All the members of selected groups have similar characteristics.

• Cluster sampling is more convenient when the population is very large or spread over large geographical area.

CLUSTER (AREA) RANDOM SAMPLING;

• Cluster sampling refer to a type of sampling method, with cluster sampling, the researcher divide the population in to separate group called cluster. Than simple random sampling of cluster is selected from the population.

Various definition;

• In this technique, the total population is divided in to groups and sample random sample of these group is selected than the required information is collected from a simple random sample of the elements with each selected group.

• It is sampling method where different groups with in a population are used as a sample.

Sampling method has following characteristics;

• The population is divided in to N groups, called cluster.

• The researcher randomly select N cluster to include in the sample.

• The number of observations within each cluster Mi is known and M=M1+M2…..Mn

• Each element of the population can be assigned to one and only on cluster.

EXPLAINATION;

• In cluster sampling, instead of selecting all the subjects from the entire population right off, the researcher take several steps in gathering his sample population.

• First the researcher selects groups • From each group researcher (cluster) the

researcher select the individual subject by either simple random sampling or systematic random sampling.

ASPECTS OF CLUSTER SAMPLING;

• The most common cluster used in research is a geographical cluster.

• For example a researcher wants to survey academic performance of a high school student in Pakistan.

• Geographically dispersed population can be expensive to survey, greater economy than simple random sampling.

• He divide the entire population in to different cluster(group).

• Than from the selected cluster the researcher can either include all the high school students from each group through simple or systematic random sampling.

TYPES OF CLUSTER SAMPLING;

One stage cluster sampling

Two stage cluster sampling

ONE STAGE CLUSTER SAMPLING;

• All of the elements within selected clusters are included in the sample.

• One stage cluster sample occurs when the researcher includes all high school students from all randomly selected cluster or sample.

TWO STAGE CLUSTER SAMPLING;

• A subset of elements within selected clusters are randomly selected for inclusion in the sample.

• Obtain when researcher only select a number of students from each cluster by using simple or systematic random sampling.

MULTISTAGE SAMPLING;

• Cluster sampling can be done in many stages involves the selection of clusters within clusters this process is called multi stage sampling.

• Schools can be randomly selected and then classrooms within each selected school can be randomly selected.

ADVANTAGES AND DISADVANTAGES

• This technique is quick, cheap, easy beside using simple random sampling.

• Feasibility ;this method takes large population into account, since these group are so large, developing any other technique will be difficult task

• Economy; The regular major concern expenditure i.e. travelling and listing effort will be greatly reduced.

DISADVANTAGES;

• Other probabilistic methods give fewer errors than this method, for this reason it is discouraged for the beginners.

• Biased sample; if the group in population that is chosen as sample has a biased opinion then entire population is inferred to have the same opinion.

Systematic random sampling;

• In systematic random sampling the researcher first randomly pick the first item or subject from the population than the researcher will select each nth subject from the list.

• The process of obtaining systematic random sampling is much like an arithmetic progression.

Starting number +INTERVAL:

• STARTING NUMBER:• The researcher select an integer that must b

less than the total number of individual in the population this integer will correspond to the first subject.

• INTERVAL:• The researcher will pick another integer

which will serve as the constant difference b/w any two consecutive number in the progression.

FORMULA:

• TOTAL POPULATION=100• NEEDED POPULATION=12• PICK ANY STARTING INTERVAL=5• PICK ANY INTERVAL=8• THE NUMBER OF SAMPLE WILL

BE=5,13,21,29,37,45,53,61,69,77,85,93.

2ND METHOD:

• A type of random sampling method in which sampling members from a large population are selected according to random starting point and a fix periodic interval. This interval is called periodic interval and calculating by divide the population size by sample size.

• Total population 50000,required sample size=1000,interval 50000/1000=50 thus every 50th person is our sample for study.

ADVANTAGES AND DISADVANTAGES:

simplicity

foolproofConvenient and easy to

administer