SAMPLING METHODS

by

R.SUJITHA (MSc.stats

II YEAR)

What is sampling?

Why sampling?

Sampling terms

Purpose of sampling

Methods of sampling

Probability sampling

Non- probability sampling

Application

Limitation

• A process of choosing a representative portion of the entire population.

• It is an integral part of research methodology.

• It involves selecting a group of people, events, behaviors or other elements with which to conduct a study.

WHAT IS SAMPLING?

WHY SAMPLING...Get information about large populations.

More accuracy i.e. Can Do A Better Job of Data Collection.

When it’s impossible to study the whole population.

Less field time.

Less costs.

SAMPLING TERMS...

TARGET POPULATION

STUDY POPULATION

SAMPLE

PURPOSE OF SAMPLINGTo provide various types of

statistical information of a qualitative or quantitative nature about the whole by examining a few selected units.

Scientific procedure of selecting those sampling units which would provide the required estimates with associated margins of uncertainty, arising from examining only a part and not the whole.

CLASSIFICATION OF SAMPLING

sampling

Probability

Simple random

sytematic

Stratified random

cluster

Non- probability

convinience

Quota

Snowball

Judgment

PROBABILITY SAMPLING

A sampling technique in which every member of the population will have a known, nonzero probability of being selected.

Probability sampling includes

Simple Random SamplingSystematic SamplingStratified Random SamplingCluster Sampling

SIMPLE RANDOM SAMPLINGIn this method each item of the data(population) has the same probability of being selected in the sample. The selection is usually made with the help of random numbers.Advantages

minimal knowledge of population neededExternal validity high; internal validity high; statistical estimation of errorEasy to analyze data

DisadvantagesHigh cost; low frequency of useRequires sampling frameDoes not use researchers’ expertiseLarger risk of random error than stratified

SYSTEMATIC SAMPLING

Advantages Moderate cost; moderate usageExternal validity high; internal validity high; statistical estimation of errorSimple to draw sample; easy to verify

DisadvantagesPeriodic orderingRequires sampling frame

An initial starting point is selected by a random process, and then every nth number on the list is selected

STRATIFIED RANDOM SAMPLING Type of probability sampling which

selects members of the sample proportionally from each sub- population or stratum.

Used when the population is too large to handle and is divided into subgroups (called strata)

Samples per stratum are then randomly selected,but considerations must be given to the sizes of the random samples to be drawn from the subgroups.

STRATIFIED RANDOM SAMPLINGAdvantages Assures representation of all groups

in sample population needed

Characteristics of each stratum can be estimated and comparisons made

Reduces variability from systematic

Disadvantages Requires accurate information on

proportions of each stratum.

Stratified lists costly to prepare

CLUSTER SAMPLING

One-stage sampling: All of the elements within selected clusters are included in the sample.Two-stage/multistage sampling:A subset of elements within selected clusters are randomly selected for inclusion in the sample.

• Used when population is divided into groups or clusters• Samples are selected in groups rather than individuals which

is employed into a large-scale survey

Section 4

Section 5

Section 3

Section 2Section 1

CLUSTER SAMPLING

Advantages• Low cost/high frequency of use• Requires list of all clusters, but only

of individuals within chosen clusters• Can estimate characteristics of both

cluster and population• For multistage, has strengths of

used methods

Disadvantages• Larger error for comparable size

than other probability methods• Multistage very expensive and

validity depends on other methods used

CLUSTER SAMPLING

NON- PROBABILITY SAMPLING Involves the selection of elements

from a population using non random procedures.

The members of sample are drawn or selected based on the judgment of the researcher.

The results of these techniques are relatively biased.

The techniques lack objectivity in terms of the selection of samples

The sample are not so reliable. The techniques are convenient and economical to use.

NON- PROBABILITY SAMPLING

Non probability sampling includes

Convenience sampling

Snowball sampling

Quota sampling

Judgment sampling

CONVENIENCE SAMPLING-The sampling procedure involves the nonrandom selection of subjects based on their availability or convenient accessibility

Advantages • Very low cost• Very extensively

used/understood• No need for list of population

elementsDisadvantages• Variability and bias cannot be

measured or controlled. • Projecting data beyond sample

not justified.

QUOTA SAMPLING-The sampling procedure that ensure that a certain characteristic of a population sample will be represented to the exact extent that the investigator desires.

Advantages • low cost• Useful in specific circumstances• Useful for locating rare populations

Disadvantages• Bias because sampling units not independent• Projecting data beyond sample not justified.

SNOWBALL SAMPLING

Advantages • moderate cost• Very extensively used/understood• No need for list of population

elementsDisadvantages• Variability and bias cannot be

measured or controlled (classification of subjects)

• Projecting data beyond sample not justified.

-The sampling procedure in which the initial respondents are chosen by probability or non-probability methods, and then additional respondents are obtained by information provided by the initial respondents

JUDGMENT SAMPLINGThe sampling procedure in which an experienced research selects the sample based on some appropriate characteristic of sample members… to serve a purpose

Advantages • Moderate cost• Commonly used/understood• Sample will meet a specific

objective

Disadvantages• Bias!• Projecting data beyond sample

not justified.

ADVANTAGESGreater economy: The total cost of a sample will be much less than that of the whole lot.

Shorter time-lag: With smaller number of observations it is possible to provide results much faster as compared to the total number of observations.

Greater scope: Sampling has a greater scope regarding the variety of information by virtue of its flexibility and adaptability.

Actual appraisal of reliability

LIMITATIONSErrors due to sampling may be high for small

administrative areas.

Sampling may not be feasible for problems that require very high accuracy.

APPLICATIONSAuto manufacturing industry

Survey about employment and unemployment in the nation

Local housing authority

Local transportation

Journals , magazine.

TV networks etc.,

Thank You…