Explain the concept of sampling. Essay



Semester:2nd ( 2neodymiumShift ) Section B


Capable codification:MS-108

Subject of assignment:Sampling ( QUES-3 )

Date of entry:27ThursdayNovember 2014


Date of entry of assignment: 27Thursdaynov’ 2014

Signature of receiving systemStamp ( office )

Question 2 Explain the construct of trying. Differentiate between bunch and stratified sampling.



Sampling is the procedure of choosing a subset of the mark population that will let dependable and valid illations about the mark population. The primary end of sampling is to make a little group from the population that is as similar to the larger population as possible. A sample is a subset of the mark population who will take part in the survey.

Samplingis concerned with the choice of a subset of persons from within a statistical populationto estimation features of the whole population.Acceptance samplingis used to find if a production batch of material meets the governingspecifications. Two advantages of

trying are that the cost is lower and informations aggregation is faster than mensurating the full population.

Advantages of Sampling

1. Sampling is cheaper than a nose count study. It is evidently more economical, for case, to cover a sample of families than all families in a district although the cost per unit of survey may be higher in a sample study than in a nose count.

2. Since magnitude of operations involved in a sample study is little, both the executing of the fieldwork and the analysis of the consequences can be carried out quickly.

3. Sampling consequences in greater economic system of attempt as comparatively little staffs is required to transport out the study and to table and treat the study informations.

4. A sample study enables the research worker to roll up more elaborate information than would otherwise be possible in a nose count study. Besides, information of a more specialized type can be collected, which would non be possible in a nose count study on history of handiness of a little figure of specializers.

5. Since the graduated table of operations involved in a sample study is little, the quality of interviewing, supervising and other related activities can be better than the quality in a nose count study.


1. Hazard of rejecting a “good” batch ( producer’s hazard )

2. Hazard of accepting a “bad” batch ( consumer’s hazard )

3. Greater disposal costs

4. Requires extra planning and certification

l5.Yields less existent information about the merchandise

6. Will non observe all faulty merchandise in a batch

7. Designed to keep a given degree of quality ; will non drive betterment

Stratified and Cluster Sampling

Stratified Sampling

Given the same figure of trying units, stratified trying frequently provides a more representative sample than does simple random trying. Under this design the list of trying units is first grouped ( or stratified ) based on certain features. A simple random sample is so taken for each group ( or stratum ) . For illustration, all Texas pupils can be grouped by part, so pupils are sampled indiscriminately from each part. This manner, a known per centum of trying units with features based on the grouping variables such as geographical part, gender, or ethnicity, are ever in the sample. Consequently, stratified trying typically produces a more representative sample and leads to more accurate appraisal of the parametric quantities of involvement.

Stratification additions preciseness without increasing sample size. Stratification does non connote any going from the rules of entropy it simply denotes that before any choice takes topographic point, the population is divided into a figure of strata, so random samples taken within each stratum. It is merely possible to make this if the distribution of the population with regard to a peculiar factor is known, and if it is besides known to which stratum each member of the population belongs. Examples of features which could be used in marketing to stratify a population include: income, age, sex, race, geographical part, ownership of a peculiar trade good.

Stratification can happen after choice of persons, e.g. if one wanted to stratify a sample of persons in a town by age, one could easy acquire figures of the age distribution, but if there is no general population list demoing the age distribution, anterior stratification would non be possible. What might hold to be done in this instance at the analysis phase is to rectify relative representation. Burdening can easy destruct the premises one is able to do when construing informations gathered from a random sample and so stratification prior to choice is advisable. Random stratified trying is more precise and more convenient than simple random sampling.

When stratified sampling designs are to be employed, there are 3 cardinal inquiries which have to be instantly addressed:

1 The bases of stratification, i.e. what features should be used to subdivide the universe/population into strata?

2 The figure of strata, i.e. how many strata should be constructed and what stratum boundaries should be used?

3 Sample sizes within strata, i.e. how many observations should be taken in each stratum.

Bunch Sampling

Another common chance trying design is cluster trying. With bunch sampling, the

list of all trying units is first grouped into bunchs based on certain features or

variables of involvement. Then, unlike stratified sampling, a preset figure of bunchs are

selected and all trying units within the chosen bunchs are observed. For illustration, all Texas

campuses can be grouped into parts. Then, a preset figure of parts can be

selected and all campuses within the chosen parts would be selected.

The procedure of trying complete groups or units is called bunch sampling, state of affairss where there is any sub-sampling within the bunchs chosen at the first phase are covered by the term multistage sampling. For illustration, say that a study is to be done in a big town and that the unit of enquiry ( i.e. the unit from which informations are to be gathered ) is the single family. Suppose further that the town contains 20,000 families, all of them listed on convenient records, and that a sample of 200 families is to be selected. One attack would be to pick the 200 by some random method. However, this would distribute the sample over the whole town, with attendant high fieldwork costs and much incommodiousness. ( All the more so if the study were to be conducted in rural countries, particularly in developing states where rural countries are sparsely populated and entree hard ) . One might make up one’s mind hence to concentrate the sample in a few parts of the town and it may be assumed for simpleness that the town is divided into 400 countries with 50 families in each. A simple class would be to choose state 4 countries at random ( i.e. 1 in 100 ) and include all the families within these countries in our sample. The overall chance of choice is unchanged, but by choosing bunchs of families, one has materially simplified and made cheaper the fieldwork.

A big figure of little bunchs is better, all other things being equal, than a little figure of big bunchs. Whether individual phase bunch trying proves to be as statistically efficient as a simple random trying depends upon the grade of homogeneousness within bunchs. If respondents within bunchs are homogenous with regard to such things as income, socio-economic category etc. , they do non to the full represent the population and will, hence, supply larger standard mistakes. On the other manus, the lower cost of bunch trying frequently outweighs the disadvantages of statistical inefficiency. In short, cluster trying tends to offer greater dependability for a given cost instead than greater dependability for a given sample size.

B ) What sample design would you choose in each of the followers?

I ) A survey to find consumer reactions to a new trade name of tea.


Simple Random Sampling ( SRS )

Asimple random sampleis hello selected so that all samples of the same size have an equal opportunity of being selected from the population.

In simple random trying ( SRS ) , trying units are indiscriminately selected from the list of all trying units. Random choice means that all trying units in the mark population have the same chance of being selected. For illustration, an SRS of 3rd grade pupils is constructed by indiscriminately choosing from the complete list of all 3rd grade pupils in Texas with each pupil holding the same opportunity of being in the sample. Simplicity in doing illations is one of the chief advantages in SRS. Another advantage is that every trying unit ( e.g. , pupil or campus ) has an equal opportunity of engagement. However, SRS can ensue in a non-representative sample of certain features such as ethnicity, gender, societal economic position, or geographical location. For illustration, since all pupils in the province have an equal opportunity of being selected in an SRS, it is possible ( though unlikely ) to hold a disproportionately high per centum of pupils from North Texas.


1.Representativeness and Freedom from Bias

Freedom from human prejudice and categorization mistake remains one of the biggest advantages simple random trying offers, as it gives each member of a population a just opportunity of being selected. If done right, simple random trying consequences in a sample extremely representative of the population of involvement. In theory, if a research worker has entree to all the necessary informations about a given population, merely bad fortune can compromise his sample ‘s representativeness.

2. Ease of Sampling and Analysis

  • Other trying methods require much in-depth research and progress cognition of a population prior to the choice of topics. In simple random sampling, merely the complete listing of the elements in a population ( known as the sampling frame ) is needed. A simple random sample, being extremely representative of a population, besides simplifies informations reading and analysis of consequences. Tendencies within the sample act as first-class indexs of tendencies in the overall population. Many consider generalisations derived from a well-assembled simple random sample to hold sufficient external cogency.


1. Mistakes in Sampling

  • While the entropy of the choice procedure ensures the indifferent pick of topics, it could besides, by opportunity, take to the assembly of a sample which does non stand for the population good. This random fluctuation, independent of all human prejudice and in many instances hard to nail, is known as “ trying mistake. ” The chance of incurring mistakes in trying additions with reduced sample size. Research workers hence set a sample size large plenty to minimise the likeliness of freak consequences.

2. Time and Labor Requirement

  • As a complete and up-to-date frame is the minimal demand for a good simple random sample, informations assemblage frequently entails a batch of clip and labour, particularly in instances affecting big mark populations. The problem with obtaining a complete sampling frame stems from the unavailability of bing informations or from the trouble of building the frame on one ‘s ain. Comprehensive lists, if they do be, are frequently non in the public sphere. To derive entree, the research worker must either wage for the informations or use for permissions — a perchance drawn-out and cumbrous process. These considerations greatly limit simple random trying ‘s pertinence to most population surveies.

two ) A survey to mensurate the audience watching a sponsored telecasting plans.


Purposive or Judgmental Sample

A purposive, or judgmental, sample is one that is selected based on the cognition of a population and the intent of the survey. For illustration, if a research worker is analyzing the nature of school spirit as exhibited at a school ginger mass meeting, he or she might interview people who did non look to be caught up in the emotions of the crowd or pupils who did non go to the mass meeting at all. In this instance, the research worker is utilizing a purposive sample because those being interviewed fit a specific intent or description.

Apurposive sampleis a non-representative subset of some larger population, and is constructed to function a really specific demand or aim. A research worker may hold a specific group in head, such as high degree concern executives. It may non be possible to stipulate the population — they would non wholly be known, and entree will be hard. The research worker will try to zero in on the mark group, questioning whoever is available.

A purposive sample, besides normally called a judgmental sample, is one that is selected based on the cognition of a population and the intent of the survey. The topics are selected because of some characteristic.

Field research workers are frequently interested in analyzing extreme or aberrant instances – that is, instances that don’t tantrum into regular forms of attitudes and behaviours. By analyzing the aberrant instances, research workers can frequently derive a better apprehension of the more regular forms of behaviour. This is where purposive sampling frequently takes topographic point. For case, if a research worker is interested in larning more about pupils at the top of their category, he or she is traveling to try those pupils who fall into the “ top of the category ” class. They will be purposively selected because they meet a certain feature.

Purposive sampling can be really utile for state of affairss where you need to make a targeted sample rapidly and where trying for proportionality is non the chief concern. Researchers ( typicallymarket research workers ) who you might frequently see at a promenade transporting a clipboard and halting assorted people to interview are frequently carry oning research utilizing purposive sampling. They may be looking for and halting merely those people who meet certain features.


  • There are a broad scope ofqualitative research designsthat research workers can pull on. Achieving thegoalsof such qualitative research designs requires different types of trying scheme andsampling technique. One of the major benefits of purposive sampling is the broad scope of trying techniques that can be used across such qualitative research designs ; purposive sampling techniques that range fromhomogeneous samplingthrough tocritical instance sampling, adept sampling, and more.
  • Whilst the assorted purposive trying techniques each have different ends, they can supply research workers with the justification to makegeneralizationsfrom the sample that is being studied, whether such generalisations aretheoretical, analyticand/orlogicalin nature. However, since each of these types of purposive trying differs in footings of the nature and ability to do generalisations, you should read the articles on each of these purposive sampling techniques to understand their comparative advantages.
  • Qualitative research designs can affect multiple stages, with each stage edifice on the old 1. In such cases, different types of trying technique may be required at each stage. Purposive sampling is utile in these cases because it provides a broad scope of non-probability sampling techniques for the research worker to pull on. For illustration, critical instance samplingmay be used to look into whether a phenomenon is deserving look intoing farther, before following anexpert samplingapproach to analyze specific issues further.


  • Purposive samples, irrespective of the type of purposive sampling used, can be extremely prone toresearcher prejudice. The thought that a purposive sample has been created based on thejudgmentof the research worker is non a good defence when it comes to relieving possible research worker prejudices, particularly when compared withprobability trying techniquesthat are designed to cut down such prejudices. However, this judgmental, subjective constituent of purpose sampling is merely a major disadvantage when such opinions areill-conceivedorpoorly considered ; that is, where judgements have non been based on clear standards, whether a theoretical model, adept evocation, or some other recognized standards.
  • The subjectiveness and non-probability based nature of unit choice ( i.e. , choosing people, cases/organizations, etc. ) in purposive sampling means that it can be hard to support the representativeness of the sample. In other words, it can be hard to convert the reader that the judgement you used to choose units to analyze was appropriate. For this ground, it can besides be hard to convert the reader that research utilizing purposive sampling achieved theoretical/analytic/logical generalization. After all, if different units had been selected, would the consequences and any generalizations have been the same?