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Probability sampling is a sampling technique wherein the samples are gathered in aprocess that gives all the individuals in the population equal chances of being

selected.

In this sampling technique, the researcher must guarantee that every individual hasan equal opportunity for selection and this can be achieved if the researcher utilizes

randomization.

The advantage of using a random sample is the absence of both systematic and

sampling bias. If random selection was done properly, the sample is thereforerepresentative of the entire population.

The effect of this is a minimal or absent systematic bias which is the differencebetween the results from the sample and the results from the population. Sampling

bias is also eliminated since the subjects are randomly chosen.

TYPES OF PROBABILITY SAMPLING

SIMPLE RANDOM SAMPLING

Simple random sampling is the easiest form of probability sampling. ll theresearcher needs to do is assure that all the members of the population are included

in the list and then randomly select the desired number of subjects.

There are a lot of methods to do this. It can be as mechanical as pic!ing strips of

paper with names written on it from a hat while the researcher is blindfolded or it

can be as easy as using a computer software to do the random selection for you.

STRATIFIED RANDOM SAMPLING

Stratified random sampling is also !nown as proportional random sampling. This is aprobability sampling technique wherein the subjects are initially grouped into

different classifications such as age, socioeconomic status or gender.

Then, the researcher randomly selects the final list of subjects from the different

strata. It is important to note that all the strata must have no overlaps.

"esearchers usually use stratified random sampling if they want to study a particular

subgroup within the population. It is also preferred over the simple random sampling

because it warrants more precise statistical outcomes.

SYSTEMATIC RANDOM SAMPLINGSystematic random sampling can be li!ened to an arithmetic progression wherein thedifference between any two consecutive numbers is the same. Say for e#ample you

are in a clinic and you have $%% patients.$. The first thing you do is pic! an integer that is less than the total number of

the population& this will be your first subject e.g. '().*. Select another integer which will be the number of individuals between

subjects e.g. '+).

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(. ou subjects will be patients (, -, $(, $-, *(, and so on.

There is no clear advantage when using this technique.

CLUSTER RANDOM SAMPLING

luster random sampling is done when simple random sampling is almost impossiblebecause of the size of the population. /ust imagine doing a simple random sampling

when the population in question is the entire population of sia.$. In cluster sampling, the research first identifies boundaries, in case of our

e#ample& it can be countries within sia.*. The researcher randomly selects a number of identified areas. It is important

that all areas 'countries) within the population be given equal chances ofbeing selected.

(. The researcher can either include all the individuals within the selected areasor he can randomly select subjects from the identified areas.

MIXED/MULTI-STAGE RANDOM SAMPLING

This probability sampling technique involves a combination of two or more samplingtechniques enumerated above. In most of the comple# researches done in the field

or in the lab, it is not suited to use just a single type of probability sampling.

0ost of the researches are done in different stages with each stage applying a

different random sampling technique.

Non-probabilit !a"plin# is a sampling technique

where the samples are gathered in a process that does not give all the individuals inthe population equal chances of being selected.

In any form of research, true random sampling is always difficult to achieve.

0ost researchers are bounded by time, money and wor!force and because of theselimitations, it is almost impossible to randomly sample the entire population and it is

often necessary to employ another sampling technique, the non1probability sampling

technique.

In contrast with probability sampling, non1probability sample is not a product of a

randomized selection processes. Subjects in a non1probability sample are usuallyselected on the basis of their accessibility or by the purposive personal judgment ofthe researcher.

The downside of this is that an un!nown proportion of the entire population was notsampled. This entails that the sample may or may not represent the entire

population accurately. Therefore, the results of the research cannot be used ingeneralizations pertaining to the entire population.

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TYPES OF NON-PROBABILITY SAMPLING

CON$ENIENCE SAMPLING

onvenience sampling is probably the most common of all sampling techniques. 2ith

convenience sampling, the samples are selected because they are accessible to theresearcher. Subjects are chosen simply because they are easy to recruit. This

technique is considered easiest, cheapest and least time consuming.

CONSECUTI$E SAMPLING

onsecutive sampling is very similar to convenience sampling e#cept that it see!s to

include 33 accessible subjects as part of the sample. This non1probability samplingtechnique can be considered as the best of all non1probability samples because it

includes all subjects that are available that ma!es the sample a better representationof the entire population.

%UOTA SAMPLING

4uota sampling is a non1probability sampling technique wherein the researcherensures equal or proportionate representation of subjects depending on which trait is

considered as basis of the quota.

5or e#ample, if basis of the quota is college year level and the researcher needs

equal representation, with a sample size of $%%, he must select *+ $st year students,another *+ *nd year students, *+ (rd year and *+ 6th year students. The bases of the

quota are usually age, gender, education, race, religion and socioeconomic status.

&UDGMENTAL SAMPLING

/udgmental sampling is more commonly !nown as purposive sampling. In this type

of sampling, subjects are chosen to be part of the sample with a specific purpose inmind. 2ith judgmental sampling, the researcher believes that some subjects are

more fit for the research compared to other individuals. This is the reason why theyare purposively chosen as subjects.

SNO'BALL SAMPLING

Snowball sampling is usually done when there is a very small population size. In thistype of sampling, the researcher as!s the initial subject to identify another potential

subject who also meets the criteria of the research. The downside of using asnowball sample is that it is hardly representative of the population.

'(EN TO USE NON-PROBABILITY SAMPLING• This type of sampling can be used when demonstrating that a particular trait

e#ists in the population.

• It can also be used when the researcher aims to do a qualitative, pilot or

e#ploratory study.

• It can be used when randomization is impossible li!e when the population is

almost limitless.

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• It can be used when the research does not aim to generate results that will be

used to create generalizations pertaining to the entire population.

• It is also useful when the researcher has limited budget, time and wor!force.

• This technique can also be used in an initial study which will be carried out

again using a randomized, probability sampling.