report on sampling techniques

7/27/2019 Report on Sampling Techniques

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SAMPLING

TECHNIQUES

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EFD 502 ADVANCED STATISTICS WITH COMPUTER APPLICATIONS

A REPORT PREPARED BY GROUP 3


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WHY SAMPLE?

Can you gather data from the entire population?

Can you finish your study in a given period of

time considering you have to use the entire

population of your study? How can you save money, resources and lessen

your efforts on your study?

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SAMPLING

A sample is a smaller (but hopefullyrepresentative) collection of units from apopulation used to determine truths about thatpopulation

The sampling frame is the list from which thepotential respondents are drawn

Registrars office

Class rosters

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POPULATION DEFINITION

A population can be defined as all people or itemswith the characteristic one wishes to understand.

A population is a collection of data whoseproperties are analyzed. The population is

the complete collection to be studied, it

contains allsubjects of interest.

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GENERAL REASONS WHY WE SAMPLE

Saves resources (time, money) andeffort/workload

Gives results with known accuracy thatcan be calculated mathematically

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WHEN MIGHT YOU SAMPLE THEENTIRE POPULATION?

When your population is very small

When you have extensive resources

When you dont expect a very highresponse

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Sampling procedure

Sample size

Participation (response)

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FACTORS THAT INFLUENCE SAMPLEREPRESENTATIVENESS?

SAMPLING


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TYPES OF SAMPLING

Probability (Random) Sampling

Non-Probability Sampling

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PROBABILITY SAMPLING

A probability sampling method is any method of samplingthat utilizes some form of random selection. In order to

have a random selection method, you must set up some

process or procedure that assures that the different

units in your population have equal probabilities of beingchosen. Humans have long practiced various forms of

random selection, such as picking a name out of a hat, or

choosing the short straw. These days, we tend to use

computers as the mechanism for generating randomnumbers as the basis for random selection.

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A probability samplingscheme is one inwhich every unit in the population has a

chance (greater than zero) of being selected

in the sample, and this probability can be

accurately determined.

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PROBABILITY SAMPLING.

Probability Sampling includes: Simple Random Sampling

Systematic Sampling

Stratified Random Sampling

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NON PROBABILITY SAMPLING

A type of unit sampling where it is not known whichof the units will be picked to be sampled, and wheresome of the units have a zero probability of beingchosen. In addition, nonresponse effects may turnanyprobability design into a nonprobability design ifthe characteristics of nonresponse are not wellunderstood, since nonresponse effectively modifieseach element's probability of being sampled.

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SIMPLE RANDOMSAMPLING

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In statistics, a simple random sample is a subset of

individuals (a sample) chosen from a larger set (a

population). Each individual is chosen randomly and entirely

by chance, such that each individual has the same

probability of being chosen at any stage during the samplingprocess, and each subset of k individuals has the same

probability of being chosen for the sample as any other

subset of k individuals.

Simple random sampling is a basic type of sampling, since itcan be a component of other more complex sampling

methods. The principle of simple random sampling is that

every object has the same probability of being chosen.

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SIMPLE RANDOM SAMPLING


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Applicable when population is small, homogeneous &readily available

All subsets of the frame are given an equal probability.

Each element of the frame thus has an equal

probability of selection.

It provides for greatest number of possible samples.

This is done by assigning a number to each unit in the

sampling frame. A table of random number or lottery system is used to

determine which units are to be selected.

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WHEN TO USE:

Simple random sampling best suits situations

where not much information is available

about the population and data collection can

be efficiently conducted on randomlydistributed items, or where the cost of

sampling is small enough to make efficiency

less important than simplicity.

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Advantages Advantages are that it is free of classification error, and it

requires minimum advance knowledge of the populationother than the frame.

Disadvantages If sampling frame large, this method impracticable.

Minority subgroups of interest in population may not bepresent in sample in sufficient numbers for study.

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SYSTEMATICSAMPLING

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SYSTEMATIC SAMPLING

A method of sampling from a list of the population sothat the sample is made up of every kth member onthe list, after randomly selecting a starting point from1 to k.

Systematic sampling relies on arranging the target

population according to some ordering scheme andthen selecting elements at regular intervals throughthat ordered list.

A simple example would be to select every 10th name

from the telephone directory (an 'every 10th' sample,also referred to as 'sampling with a skip of 10').

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EXAMPLE

Consider choosing a systematic sample of 20 members

from a population list numbered from 1 to 836.

To find k, divide 836 by 20 to get 41.8.

Rounding gives k = 42.

Randomly select a number from 1 to 42, say 18.

Start at the person numbered 18 and then choose every

42nd member of the list.

The sample is made up of those numbered:

18, 60, 102, 144, 186, 228, 270, 312, 354, 396, 438, 480,522, 564, 606, 648, 690, 732, 774, 816

Sometimes rounding may cause the sample size to be

one more or one less than the desired size.

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SYSTEMATIC SAMPLING

ADVANTAGES: Sample easy to select

Suitable sampling frame can be identified easily

Sample evenly spread over entire reference population

DISADVANTAGES: Sample may be biased if hidden periodicity in population

coincides with that of selection.

Difficult to assess precision of estimate from one survey.

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STRATIFIEDSAMPLING

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STRATIFIED SAMPLING

A stratified sample is a probability sampling

technique in which the researcher divides the

entire target population into differentsubgroups, or strata, and then randomly selects

the final subjects proportionally from the

different strata.

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STRATIFIED SAMPLING

When surveying a large population that is verydiverse

When the researcher wants to highlight specific

subgroups within the population. When they want to observe relationships between

two or more subgroups

When the researchers are interested in rare

extremes of a population

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WHEN TO USE:


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ADVANTAGES

Guarantees better coverage of the

population

Always achieves greater precision than

simple random sampling (largely unbiased

and accurate)

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STRATIFIED SAMPLING


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It can be difficult to identify appropriate strata

for a study

It is more complex to organize and analyze

the results

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DISADVANTAGES

STRATIFIED SAMPLING


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ACCIDENTAL

SAMPLING

NONPROBABILTY SAMPLING

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ACCIDENTAL SAMPLING

Sometimes known as grab or opportunity sampling orconvenience or haphazard sampling. A type of non-probability sampling which involves the

sample being drawn from that part of the populationwhich is close to hand. That is a sample population

selected because it is readily available and

convenient.

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The Advantages of this type of sampling are

the availability and the quickness with whichdata can be gathered

The disadvantages are the risk that the

sample might not represent the population as

a whole, and it might be biased by volunteers.

ACCIDENTAL SAMPLING...


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QUOTA

SAMPLING


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DEFINITION

Quota sampling is a non-probability

sampling technique wherein the

assembled sample has the same

proportions of individuals as the entirepopulation with respect to known

characteristics, traits or focused

phenomenon.

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CREATING A QUOTA SAMPLE

To create a quota sample, there are threesteps:

(1) choosing the relevant stratification and

dividing the population accordingly;(2) calculating a quota for each stratum; and

(3) continuing to invite cases until the quota

for each stratum is met.

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ADVANTAGES AND DISADVANTAGES


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(LIMITATIONS) OF QUOTA SAMPLING

ADVANTAGES Quota sampling is particularly useful when you are

unable to obtain a probability sample,

Quota sampling is much quicker and easier tocarry out because it does not require a samplingframe and the strict use of random samplingtechniques

The quota sample improves the representation of

particular strata (groups) within the population, aswell as ensuring that these strata are not over-represented.

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(LIMITATIONS) OF QUOTA SAMPLING

DISADVANTAGES In quota sampling, the sample has not been chosen

using random selection, which makes it impossible to

determine the possible sampling error. This can lead

to problems of generalization.

Quota sampling be biased because not everyone gets

chance of selection

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PURPOSIVE

SAMPLING


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SNOWBALL

SAMPLING


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Also known as chain sampling, chain-referralsampling, referral sampling Snowball sampling uses a small pool of initial

informants to nominate, through their social networks,

other participants who meet the eligibility criteria andcould potentially contribute to a specific study. The

term "snowball sampling" reflects an analogy to a

snowball increasing in size as it rolls downhill

SNOWBALL SAMPLING

SNOWBALL SAMPLING...


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Draft up a participation program (likely to be subject to

change, but indicative).

1. Approach stakeholders and ask for contacts.

2. Gain contacts and ask them to participate.

3. Community issues groups may emerge that can be

included in the participation program.4. Continue the snowballing with contacts to gain more

stakeholders if necessary.

5. Ensure a diversity of contacts by widening the profile of

persons involved in the snowballing exercise.

METHOD:



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Pre-assumption: The participants are likely to know others whoshare the characteristics that makes them eligible for inclusionin the study.

There are many reasons why an individual may want to use

snowball sampling across any industry, research, job, etc.Specific to business and marketing, however, snowball sampling

can be used to things such as identify experts in a certain field,

product, manufacturing processes, customer relation methods,

etc

WHEN TO USE:



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1. Locate hidden populations: It is possible for the surveyorsto include people in the survey that they would not haveknown.

2. Locating people of a specific population: There is no listsor other obvious sources for locating members of the

population of specific interest.3. The process is cheap, simple and cost-efficient. This

sampling technique needs little planning and fewer workforce

compared to other sampling techniques.

ADVANTAGES:



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1. The researcher has little control over the sampling method.

The subjects that the researcher can obtain rely mainly onthe previous subjects that were observed.

2. Representativeness of the sample is not guaranteed. The

researcher has no idea of the true distribution of the

population and of the sample.

3. Sampling bias is also a fear of researchers when using this

sampling technique. Initial subjects tend to nominate

people that they know well. Because of this, it is highly

possible that the subjects share the same traits and

characteristics, thus, it is possible that the sample that theresearcher will obtain is only a small subgroup of the entire

population.

DISADVANTAGES:



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