Sampling –

Definition, Purpose, TheoryN1

Social Work Research

and Statistics

Submitted By,

Rubin M.

2nd

Semester

MSW

Submitted To,

Ashwin Mathew

(Asst.Prof.)N2

Dept. of Social

Work

Submitted On,

15.01.18.

Content

Sl.No.

Title

Page No.

1

Introduction

3-4

2

Sampling Definition

3-4

3

Advantages of Sampling

4

4

Disadvantages of Sampling

4

5

What is the purpose of Sampling

5

6

Sampling Theory

5-8

7

Conclusion

8

8

Reference

N3

8

IntroductionM4

In the Research Methodology, practical formulation

of the research is very much important and so should be done very carefully

with proper concentration and in the presence of a very good guidance.

But during the formulation of the research on the practical grounds, one tends

to go through a large number of problems. These problems are generally related

to the knowing of the features of the universe or the population on the basis

of studying the characteristics of the specific part or some portion, generally

called as the sample. Sampling is an important

component of any piece of research because of the significant impact that it

can have on the quality of

your results/findings.

Sampling Definition

Accordingto “Hagan, (2010)”

“Sampling is a procedure used in research by which a selected subunit of a

population is studied in order to analyse the entire population.”

Sampling is the process of

selecting a number of individuals for a study in such a way that the individuals represent a larger group from which they

were selected. In Simple words, M5 Sampling is the process of selecting a subset

of units from the population.

The sampling strategy that you

select in your dissertation should naturally flow from your chosen research design and research methods, as well as taking

into account issues of research

ethics. To set the sampling

strategy that you will use in your dissertation, you need

to follow three steps: (a) understand

the key terms and basic principles; (b) determine which sampling technique you will use

to select the units that will make up your sample; and (c) consider the practicalities of choosing such

a sampling strategy for your dissertation (e.g., what time you have available,

what access you have, etc.). In the

sampling model, you start by identifying the population you would like to

generalize to. Then, you draw a fair sample from that population and conduct

your research with the sample. Finally, because the sample is representative of

the population, you can automatically generalize your results back to the

population. There are several problems with this approach. First, perhaps you

don’t know at the time of your study who you might ultimately like to

generalize to. Second, you may not be easily able to draw a fair or

representative sample. Third, it’s impossible to sample across all times that

you might like to generalizeM6 to the sampling process

comprises several stages:

? Defining the population of concern

? Specifying a sampling frame, a set of items or events

possible to measure

? Specifying a sampling method for selecting items or events from the frame

? Determining the sample size

? Implementing the sampling plan

? Sampling and data collecting

? Reviewing the sampling process

Advantages of Sampling

1. Very accurate.

2. Economical in nature.

3. Very reliable.

4. High suitability ratio towards the different surveys.

5. Takes less time.

6. In cases, when the universe is very large, then the sampling method is the

only practical method for collecting the data.

Disadvantages of sampling

1.

Inadequacy of the samples.

2. Chances for bias.

3. Problems of accuracy.

4. Difficulty of getting the representative sample.

5. Untrained manpower.

6. Absence of the informants.

7. Chances of committing the errors in sampling.

What is the purpose of sampling?

Ø To gain an impression of an area or collection of things.

Ø To estimate a population parameter.

Ø To test hypotheses unproven theories or suppositions which are the

basis for further investigation.

Ø To get improved quality of data.

Ø To gather data about the population in order to make an inference

that can be generalized to the population.

Ø To provide various types of statistical information of a qualitative

or quantitative nature about the whole by examining a few selected unit.

Sampling TheoryM7

Sampling theory is a study of relationships existing between

a population and samples drawn from the population. Sampling theory is

applicable only to random samples. For this purpose the population or a

universe may be defined as an aggregate of items possessing a common trait or

traits. In other words, a universe is the complete group of items about which

knowledge is sought. The universe may be finite or infinite. Infinite universe

is one which has a definite and certain number of items, but when the number of

items is uncertain and infinite, the universe is said to be an infinite

universe. Similarly, the universe may be hypothetical or existent. In the

former case the universe in fact does not exist and we can only imagine the

items constituting it. Tossing of a coin or throwing a dice are examples of

hypothetical universe. Existent universe is a universe of concrete objects

i.e., the universe where the items constituting it really exist. On the other

hand, the term sample refers to that part of the universe which is selected for

the purpose of investigation. The theory of sampling studies the relationships

that exist between the universe and the sample or samples drawn from it.

Sampling theory is designed to attain one or more

of the following objectives:

Statistical estimation: Sampling theory helps in estimating unknown

population parameters from a knowledge of statistical measures based on sample

studies. In other words, to obtain an estimate of parameter from statistic is

the main objective of the sampling theory. The estimate can either be a point

estimate or it may be an interval estimate. Point estimate is a single estimate

expressed in the form of a single figure, but interval estimate has two limits

viz., the upper limit and the lower limit within which the parameter value may lie.

Interval estimates are often used in statistical induction.

Testing of hypotheses: The second objective of sampling theory is to

enable us to decide whether to accept or reject hypothesis; the sampling theory

helps in determining whether observed differences are actually due to chance or

whether they are really significant.

Statistical inference: Sampling theory helps in making generalisation

about the population/ universe from the studies based on samples drawn from it.

It also helps in determining the accuracy of such generalisations. Thus, sampling theory is the field of statistics that is

involved with the collection, analysis and interpretation of data gathered from

random samples of a population under study. The application of sampling theory

is concerned not only with the proper selection of observations from the

population that will constitute the random sample; it also population

parameters, to analyse the data from the random sample involves the use of

probability theory, along with prior knowledge about the and develop

conclusions from the analysis. The normal distribution, along with related

probability distributions, is most heavily utilized in developing the

theoretical background for sampling theory.

Sampling theory provides the tools and techniques for data

collection keeping in mind the objectives to be fulfilled and nature of

population.

There are two ways of obtaining the information

1. Sample surveys

2. Complete enumeration or census

Sample surveys collect information on a fraction of total

population whereas in census, the information is collected on the whole

population. Some surveys, e.g. economic surveys, agricultural surveys etc. are

conducted regularly. Some surveys are need based and are conducted when some

need arises, e.g., consumer satisfaction surveys at a newly opened shopping

mall to see the satisfaction level with the amenities provided in the mall.

1. Sampling unit:

An element or a group of elements on which observations can

be taken is called a sampling unit. The objective of the survey helps in

determining the definition of sampling unit.For example, if the objective is to

determine the total income of all the persons in the household, then the sampling

unit is household. If the objective is to determine the income of any particular

person in the household, then the sampling unit is the income of the particular

person in the household. So the definition of sampling unit depends and varies

as per the objective of the survey. Similarly, in another example, if the

objective is to study the blood sugar level, then the sampling unit is the

value of blood sugar level of a person. On the other hand, ifthe objective is

to study the health conditions, then the sampling unit is the person on whom

the readings on the blood sugar level, blood pressure and other factors will be

obtained. These values will together classify the person as healthy or

unhealthy.

2. Population:

Collection of all the sampling units in a given region at a

particular point of time or a particular period is called population. For

example, if the medical facilities in a hospital are to be surveyed through the

patients, then the total number of patients registered in the hospital during

the time period of survey will be the population. Similarly, if the production

of wheat in a district is to be studied, then all the fields cultivating wheat

in that district will constitute the population. The total number of sampling

units in the population is the population size, denoted generally by N. The

population size can be finite or infinite (N is large).

3. Census:

Complete count of population is called census. The

observations on all the sampling units in the population are collected in a

census. For example, in India, the census is conducted at every tenth year in

which observations on all the persons staying in India is collected.

4. Sample:

One or more sampling units are selected from the population

according to some specified procedure. A sample consists only of a portion of

the population units.

In the context of sample surveys, a collection of units like

households, people, cities, countries etc. is called a finite population. A

census is a 100% sample and it is a complete count of the population.

5. Representative Sample:

All salient features of population are present in the sample.

It goes without saying that every sample is considered as a representative

sample .For example, if a population has 30% males and 70% females, then we

also expect the sample to havenearly 30% males and 70% females. In another

example, if we take out a handful of wheat from a 100 Kg. bag of wheat, we

expect the same quality of wheat in hand as inside the bag. Similarly, it is

expected that a drop of blood will give the same information as all the blood

in the body.

6. Sampling Frame:

List of all the units of the population to be surveyed

constitutes the sampling frame. All the sampling units in the sampling frame

have identification particulars. For example, all the students in a particular

university listed along with their roll numbers constitute the sampling frame.

Similarly, the list of households with the name of head of family or house

address in more than one frame – as per automobile registration as well as the

listing in the telephone directory.

CONCLUSION

Sampling refers to the

statistical process of selecting and studying the characteristics of a

relatively small number of items from a relatively large population of such

items, to draw statistically valid inferences about the characteristics about

the entire population. In research terms a sample is a

group of people, objects, or items that are taken from a larger population for

measurement. The sample should be representative of the population to ensure

that we can generalise the findings from the research sample to the population

as a wholeM8 .