sampling plan 123

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Guide by:

Rhythm

Maam

PRESENTED BY:-

Rahul Kumar


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Sampling is the act, process, or technique ofselecting a suitable sample, or a representativepart of a population for the purpose of determiningparameters or characteristics of the wholepopulation.

Sampling is that part ofstatistical practice concerned withthe selection of individual observations intended to yieldsome knowledge about a population of concern, especiallyfor the purposes ofstatistical inference.

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http://en.wikipedia.org/wiki/Statisticalhttp://en.wikipedia.org/wiki/Population_(statistics)http://en.wikipedia.org/wiki/Statistical_inferencehttp://en.wikipedia.org/wiki/Statistical_inferencehttp://en.wikipedia.org/wiki/Population_(statistics)http://en.wikipedia.org/wiki/Statistical


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Sampling is the process of selecting units(e.g., people, organizations) from apopulation of interest so that by studyingthe sample we may fairly generalize our

results back to the population from whichthey were chosen.

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To draw conclusions about populations from samples,

we must use inferential statistics which enables us to

determine a population's characteristics by directlyobserving only a portion (or sample) of the

population. We obtain a sample rather than a

complete enumeration (a census ) of the population

for many reasons.

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Topic cover under the

samplingprocess

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Define the Population

Determine the Sampling

Frame

Select Sampling Technique(s)

Determine the Sample Size

Threats to Sampling Techniques


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Population: is the aggregate of all elements,sharing some common set of characteristics, thatcomprise the universe for the purpose ofResearch Problem.

A census involves complete enumeration of the

elements of a population or study objects. Advantages:

Information can be obtained for each and every unit ofpopulation.

Greater accuracy in research results.

Disadvantages: It is very costly. It is very time consuming and requires a lot of effort and

energy.

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The population is defined in term of ElementsUnits ExtentTime

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A sampling frame is a mean ofrepresenting the elements of thepopulation.

A sampling frame may be a telephone book, a city directory,an employee roster, a listing of all student attending theuniversity, or a list of possible phone number .

A perfect sampling frame is one in which every element ofthe population is represented once but only once.

Example of perfect frames are rare, however, when one isinterested in sampling from any appreciable segment of ahuman population.

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SamplingTechniques

NonprobabilitySampling Techniques

ProbabilitySampling Techniques

Convenience

Sampling

Judgmental

Sampling

Quota

Sampling

Snowball

Sampling

Systematic

Sampling

Stratified

Sampling

Cluster

Sampling

Multistagesampling

SimpleRandomSampling


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A nonprobability sample is one in which chanceselection procedures are not used.

''Nonprobability sampling'' is any sampling method

where some elements of the population have ''no''chance of selection (these are sometimes referredto as 'out of coverage'/'undercovered' ), or wherethe probability of selection can't be accuratelydetermined.

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''Example: We visit every household in agiven street, and interview the first personto answer the door. In any household withmore than one occupant, this is a

nonprobability sample, because somepeople are more likely to answer the door(e.g. an unemployed person who spendsmost of their time at home is more likely toanswer than an employed housemate whomight be at work when the interviewer calls)and it's not practical to calculate theseprobabilities

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Convenience Sampling

Judgmental Sampling

Quota Sampling

Snowball Sampling

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A convenience sample is one in which theonly criterion for selecting the samplingunits is the convenience of the sample.

An example of convenience sampling is the

testing by food product manufactured ofpotential new product by adding them tothe menu of the company cafeteria ofpotential new cake mix for example, can be

tasted by adding it to the desert section andnoting how well it sells relative to the otherkinds of cake offered.

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A judgment sample is one in which there isan attempt to draw a new representativesample of the population using judgmentalselection procedures.

An example is a sample of address taken bya municipal agency to which questionnaireson bicycle-riding habit were sent.

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In '''quota sampling''', the population is firstsegmented into [[mutually exclusive]] sub-groups, just as in [[stratified sampling]].

Then judgment is used to select thesubjects or units from each segment basedon a specified proportion. For example, aninterviewer may be told to sample 200females and 300 males between the age of45 and 60.

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It is this second step which makes the technique one ofnon-probability sampling. In quota sampling theselection of the sample is non-[[random]].

For example interviewers might be tempted to interviewthose who look most helpful. The problem is that thesesamples may be [[Biased samples biased]] because noteveryone gets a chance of selection. This randomelement is its greatest weakness and quota versusprobability has been a matter of controversy for many

years

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snowball sampling is a technique for developing aresearch sample where existing study subjectsrecruit future subjects from among theiracquaintances. Thus the sample group appears togrow like a rolling snowball. As the sample buildsup, enough data is gathered to be useful forresearch. This sampling technique is often used inhidden populations which are difficult forresearchers to access; example populations wouldbe drug users or commercial prostitutes.

snowball samples are subject to numerous biases Forexample, people who have many friends are morelikely to be recruited into the sample

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A probability sample is one in which the sampling units areselected by chance and for which there is a known chanceof each unit being selected.

''Example: We want to estimate the total income of adultsliving in a given street. We visit each household in that

street, identify all adults living there, and randomly selectone adult from each household. (For example, we canallocate each person a random number and select theperson with the highest number in each household). Wethen interview the selected person and find their income.''

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Simple Random Sampling

Systematic Sampling

Stratified Sampling

Cluster Sampling

Multi-Stage Sampling

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It is a procedure in which every possible sample ofa certain size within a population has a knownand equal probability of being chosen as thestudy sample. It is the most basic type of

probability sampling. The actual selection of asimple random sample can be done by randomlypicking the desired number of units from thepopulation.

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A systematic random sample is obtained by selectingone unit on a random basis and choosing additionalelementary units at evenly spaced intervals until thedesired number of units is obtained.

For example, there are 100 students in your class. Youwant a sample of 20 from these 100 and you havetheir names listed on a piece of paper may be in analphabetical order. If you choose to use systematicrandom sampling, divide 100 by 20, you will get 5.Randomly select any number between 1 and five.Suppose the number you have picked is 4, that will beyour starting number. So student number 4 has beenselected. From there you will select every 5th nameuntil you reach the last one, number one hundred. Youwill end up with 20 selected students.

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A stratified sample is obtained by independentlyselecting a separate simple random sample from eachpopulation stratum. A population can be divided intodifferent groups may be based on some characteristicor variable like income or education.

Like any body with ten years of education will be ingroup A, between 10 and 20 group B and between 20and 30 group C. These groups are referred to asstrata You can then randomly select from eachstratum a given number of units which may be basedon proportion like if group A has 100 persons whilegroup B has 50, and C has 30 you may decide you willtake 10% of each. So you end up with 10 from groupA, 5 from group B and 3 from group C.

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Cluster sampling is a probability samplingprocedure in which clusters of populationunits are selected at random and then all orsome units in the chosen clusters are

studied.

For example- cluster sampling would be to

change the sampling unit to city, blocks andto take every household on each blockselected.

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a. divide population intoclusters (usually alonggeographic boundaries)

b. randomly sampleclusters

c. measure all units withinsampled clusters


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The four methods weve covered so far -- simple,stratified, systematic and cluster -- are the simplestrandom sampling strategies. In most real applied socialresearch, we would use sampling methods that areconsiderably more complex than these simple

variations. The most important principle here is that wecan combine the simple methods described earlier in avariety of useful ways that help us address oursampling needs in the most efficient and effectivemanner possible. When we combine sampling methods,we call this multi-stage sampling.

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Example:-Consider the idea of samplingDelhi residents for face-to-face interviews.Clearly we would want to do some type ofcluster sampling as the first stage of the

process. So, we might set up a stratifiedsampling process within the clusters. Inthis case, we would have a two-stagesampling process with stratified samples

within cluster samples.

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Formulas, tables, and power function chartsare well known approaches to determinesample size.

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The sample size formula for estimating a proportion (also called apercentage or share):

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There are three major threats to sampling

techniques because there are three ways youcould be wrong -- people, places or times. Onemay argue that-

The results of your study are due to the unusualtype of people who were in the study.

It might only work because of the unusual placeyou did the study in (perhaps you did youreducational study in a college town with lots ofhigh-achieving educationally-oriented kids).

Or, suggest that you did your study in a peculiartime.


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Telephone and personalinterview surveys: makingcallbacks and spreading out thetime blocks during which

interviews are conducted

Data error (Occurs because ofdistortions in the data collectedas well as mistakes in datacoding, analysis, orinterpretation)

Ensuring that the questionnaire isgood-i.e., is simple, unbiased, etc.Proper selection, training, and

supervision of interviewers

Keeping the interviewers tasksimple and making it clear tothemGive adequate compensation to

the interviewersUsing sound editing and coding

procedures Taking into account the nature

and quality of the data inanalyzing and interpreting them

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