Sampling Methods


The plan, method, or technique through which a researcher identifies and selects the potential sampling units from the sampling frame or the target population, to form a relevant sample for the study is termed as "sample design".

Sample designing is the working principle of any research process. Without proper designing of sample it is not possible to start a survey, as it is the base which provides the responses of relevant members of the population. The designing of sample may be simple or complex depending on the method used for it.

The two factors namely, the element selection technique and the representation basis are responsible for different types of sample technique. The sample. based on representation, may be either probability sampling (random sampling) or non-probability sampling (non-random sampling).

On the basis of element selection the sample can be restrictive or unrestrictive sampling. When each sample element is selected individually from the large population then it is termed as 'unrestrictive sample and all the other types of sampling are termed as 'restrictive sampling'.

What are Sampling Methods ?


Thus, sample designs are basically of two types, viz., probability sampling and non-probability sampling as shown in figure below:

Types of Sampling

A) Probability Sample (Probability Sampling) :


The method in which all units of the universe is given equal chance of being selected in the sample, is known as Probability Sampling. There is assurance of the results in terms of probability that are obtained through probability or random sampling. The significance of the results lies in measuring the errors of estimation obtained from a random sample which brings predominance of the random sampling designs over the intentional sample design.

Advantages of Probability Sampling


Following are the advantages of probability sampling:

i) Unbiased Estimates: 
The sampling method which basically provides unbiased estimates are called random sampling, having significant precision. If this level of objectivity is required by the investigator, then some alternative probability sampling is important.

ii) Relative Efficiency: 
Evaluation in quantitative terms of the relative efficiency of other sampling techniques under a given situation by the researcher is permitted by random sampling which is normally impossible in non-probability sampling.

iii) Less Universe Knowledge Required: 
Comparatively, little knowledge of universe is required. Basically, two things are required to be known only:
  • A unique way of identifying each universe, clement, and 
  • The total number of universe elements.

iv) Fair: 
Every item in the population has an equal chance of being selected and analyzed. 

v) Easy: 
Easy data analysis and error calculation is allowed by this method of sampling.

Disadvantages of Probability Sampling


The disadvantages of probability sampling are as follows: 

i) Less Efficient: 
Other sampling methods are more statistically efficient in comparison to this method. 

ii) Non-Utilization of Additional Knowledge: 
No additional knowledge is used in this method about how the population is organized.

iii) Complex and Time Consuming: 
In many cases, this method of selection is time consuming and complicated, particularly for marketing research in which the budget constraints and limitations of time provide preference to non-probability methods of sampling.

iv) High Level Skills: 
A very high level of skill and experience is required for using probability sampling.

v) More Time Required: 
For planning and execution of a probability sample a lot of time is required.

vi) High Costs: 
In comparison to non-probability sampling, probability sampling involves higher cost. 

Types of Probability Sampling


Following are the various probability sampling techniques:

Types of Probability Sampling

1) Simple Random Sample :

This is the most famous and simple method of sampling where each unit of the population is equally probable of getting included in the sample. Let us is 'N' from consider that the size of the population is which n units are to be selected at random for a sample such that 'Cn' sample has the probability of being selected equally. Simple random sampling says that:
  • There is an equal chance for each element of the population to be included in the sample and the choices are independent to each other.
  • Each possible sample combination has an equal chance of being chosen.
The possibility of bias or personal prejudices in the selection of the units is eliminated by the method of simple random sampling. 

Methods of Simple Random Sampling

Some of the common methods of drawing simple random samples are: 

i) Lottery Method: 
This method includes the following steps. Let N be the size of population
and a sample of size n is to be drawn: 
  • The units of the population are numbered from 1 to N.
  • N identical pieces of papers, cards, capsules or balls are taken and are numbered from 1 to N.
  • These are mixed carefully in a bag, bowl or some other container and n items are selected either by replacement or without replacement in one stroke.
  • The number of units in the population bearing the numbers on the items drawn in step (iii) constitutes the desired random sample.
The application of this method is simple and easy but as the size of the population increases to infinity this method is unfeasible.

ii) By Using Random Numbers: 
The random number table that helps in selecting a sample has been constructed by some experts, The most popular of all the existing tables, Tipple's Tables is very popular and in use. Out of the given population, random selection of the samples is done through these numbers. This table ranges from 0 to 9 having an equal chance of appearing in any position of the table. The actual utilization of the random numbers is for large population where the random number table consist of several columns and rows out of which one is selected at random and then the samples are continuously selected in sequence for the desired size of the samples. Without any bias it provides a set of random numbers.

Advantages of Simple Random Sampling 

Salient advantages of simple random sampling are as follows:

i) Freedom from Bias: 
The biggest advantage of simple random sampling is that it offers absence of human bias and error of classification as every member of the population is provided with fair chance of selection.

ii) Representativeness: 
Without any errors in the execution of simple random sampling, the results are fully typical to the population of interest. Thus, theoretically a bad luck can only compromise the researcher's access to all the important data about a given population not representing the sample.

iii) Ease of Sampling and Analysis: 
Comparatively, simple random sampling only requires a complete list of the elements in the population whereas, the requirement of other methods of sampling is deep research and advance knowledge of the population before the selection of subjects. The highly representative sample of the population through simple random sampling simplifies the analysis of the results and data interpretation.

Disadvantages of Simple Random Sampling 

Disadvantages of simple random sampling are as follows:

i) Cost:
 Simple random sampling usage is limited by cost factor. According to this method there is a same chance for every possible, item in the universe to be chosen, the actual sample chosen often consist of widely dispersed geographical items. For conducting personal interviews by the interviewers involves extensive travelling which enhances the cost of field operations.

ii) Availability of a Current Listing of Universe Elements: 
The practical use of simple random sampling requires accurate list of universe elements which has serious limitations.

iii) Statistical Efficiency: 
There is a possibility of a standard error in getting a statistically efficient sample when compared to the other sample design which is statistically more efficient for the same size sample. This is the third difficulty associated with this sampling.

iv) Administrative Difficulties: 
There are number of administrative difficulties associated with simple random sampling. In this method the selection of sample is conceptually simple yet problematic at times. There is another administrative problem in simple random sampling is the problem related to maintaining supervisory control during home personal interviews.

2) Systematic Sample :

After the selection of one unit at random from the universe the other units are selected systematically at a specified interval of time. This method is applicable when the size of the population is finite and on the basis of any system the units of the universe are arranged such as alphabetic arrangement, numerical arrangement, or geographical arrangements.

Advantages of Systematic Sampling 

Systematic sampling has certain advantages, which are as follows: 

i) Simple and Convenient: 
The results obtained by this system are generally satisfactory as it is simple and convenient method. 

ii) Gives Similar Results: 
The results obtained by proportionate stratified random sampling are similar to the results given by systematic sampling when the population size is large.

iii) Independent:
The sample obtained is a true representative sample as this method of selection is not dependent on the property of universe under study.

iv) Little Chance of Biasness: 
The sample is considered to be free from bias or least affected.

v) Helps in Random Selection: 
The popularity of this method is in the selection of the candidate contesting in a tie position and in random drawing of the prizes.

Disadvantages of Systematic Sampling

Following are the disadvantages of systematic sampling: 

i) High Sampling Error: 
There are several clusters in the universe but in this method only one cluster of the population is selected. Due to this reason, the sampling error is very high and it cannot be evaluated properly.

ii) Possibility of Selecting Impracticable Units: 
Sometimes unrealistic units of the population are selected, but in a practical situation it is very easy to determine the presence of periodicity or determination of its significance.

iii) Biased: 
The drawer's bias affects the selection if all the chits are not identically folded and are not same in size, shape and color.

iv) Not Suitable for Large Population: 
This method is not suitable for a large universe because it is very difficult to write down the names or particulars of à large number of units on the chits of paper.

3) Stratified Random Sample :

In the Stratified Random Sampling, the sample is selected from different homogeneous strata or parts of a universe instead of heterogeneous universe as a whole. The summary of this sampling procedure is us follows :
  • The sampled universe is divided (or stratified) into groups that are mutually exclusive and include all items in the universe. 
  • A simple random sample is then chosen independently from each group or stratum.
There is a difference in the process of stratified random sampling from that of simple random sampling. In simple random sampling the sample items are chosen at random from the whole universe whereas in stratified sampling the design of the sample is the selection of the separate random sample from each stratum. The distribution of the sample among strata is based on chance in simple random sampling.

Advantages of Stratified Probability Sampling 

Advantages of stratified probability sampling are as follows:

i) More Representatives: 
This is the case heterogeneous universe. When it is stratified homogeneous groups the element of heterogeneity is reduced due to different strata being heterogeneous and any particularity regarding heterogeneity is nowhere. When sampling fractions are same (i) within the strata then it is called 'proportionate stratified random sampling. When sampling fractions are different (ii) within the strata then it is ca 'disproportionate stratified random sampling'.

ii) Certainty: 
The uncertainty of random sampling against bias of intentional selection is balanced by this type of sampling.

iii) Greater Precision: 
In comparison to simple random sampling the estimates provided by stratified random sampling are more precise to reduction in the variability in each stratum.

iv) Administrative Convenience: 
Division of the universe into different strata of sub-groups red in administrative convenience. 

Disadvantages of Stratified Probability Sampling

Disadvantages of stratified probability sampling are as follows: 

i) Needs More Attention: 
Stratified sampling results in proportionate or disproportionate sampling designs. In proportionate sampling, the sample size is proportional to the stratum size where, there is a higher precision level magnified by a homogeneous population. Disproportion stratification provides changing sample size each stratum. Criteria used for allocating the strata points determine whether the precision of the design is excellent or pitiful. It is best suit for strata with varying characteristics because the accuracy of one study is optimized only t cannot be transferred to consecutive surveys.

ii) Time Consuming: 
Obtaining the sample by the method involves several steps along with the record of the population being studied. Sometimes due to non-availability of the list a to develop it, makes the work tougher as t strata should be mutually exclusive resulting increased sample size and increases expenditure. The time of study is also extended.

iii) Complicated: 
Decisions on stratification completed before the start of the study. The information collected becomes invalid in choosing drawing conclusions if the choices are wrong. Data analysis is also complex due to the consideration of number and size of strata population, size of total population and sample population.

iv) Expensive: 
Use of the design requires large sample size which enhances the cost particularly in those cases where there is a classification of the needed list to be purchased. There are other instances where the list is easily accessible but the people are scattered geographically and made necessary arrangements to reaching them, which adds expenses.

4) Cluster Sampling :

According to this method there is further noticeable sub-division of the universe into clusters. Simple random sampling is performed and clusters are drown accordingly constituting a sample of all the units belonging to the selected clusters. For example, if we have to conduct a survey in the city of Mumbai, then the city may be divided into, say, 40 blocks and out of these 40 blocks, 5 blocks can be picked up by random sampling and the people in these five blocks are interviewed to give their opinion on a particular issue.

The clusters chosen should be small in size, i.e.. more or less the same number of sample units should be there in each cluster. This method is used in the collection of data about some common traits of the population. Comparing it with random sampling cluster, sampling is certainly less precise and there is no doubt about it in the reduction of the cost by intensifying surveys in selected clusters. Comparatively, within a cluster there is not much information in 'n' observations that appears to be in 'n' randomly drawn observations. Due to its economic advantages cluster sampling is used. Cluster sample estimates are most reliable and effective per unit cost.

Advantages of Cluster Sampling 

Advantages of cluster sampling are as follows: 

i) Cheap, Quick and Easy: 
The technique of this. sampling is cheap, quick and easy. The researcher allocates his limited resources only to the randomly selected few clusters or areas while using cluster samples rather than sampling the whole country by using simple random sampling.

ii) Larger Sample Size: 
The size of the sample can be increased by the researcher by this technique. Due to accessibility, the subjects of research can be increased, considering that the researcher has to only collect the sample from a given population

iii) Convenient to Obtain: 
Obtaining of the clusters is convenient and the cost of sampling reduces because the scope of study is limited only to clusters instead of whole population.

iv) Cost Effective: 
The scope of cluster sampling method is widely used in marketing research due to its complete cost-effectiveness and feasibility of implementation, especially in the situations of area sampling.

Disadvantages of Cluster Sampling 

Given below are the disadvantages of cluster sampling:

i) Least Representative: 
This technique of sampling is least representative of the population. There is a tendency of having similar characteristics of the individuals within the cluster and with a cluster sample the results of the study are skewed due to the chance of over- representation.pr under-representation of the cluster.

ii) High Sampling Error: 
The percentage of error is high as it is a probability sampling technique. This is due to the cluster being limited in numbers which are included in the sampling and it leaves behind significant proportion of the un-sampled population.

iii) Less Efficient: 
Cluster sampling may be statistically less efficient than simple random sampling if the elements of a cluster are similar.

iv) Sometimes not Appropriate: 
The sampling from the cluster is better than sampling a single unit from the cluster in the situation, when the elements of a cluster are same.

5) Multi-Stage Sampling :

Modification of cluster sampling is multi-stage sampling where in cluster sampling, a sample is constituted by all the units selected in a cluster but in multi-stage sampling the selection of the sample units is in two, three, four stages. Firstly, the universe is divided into first-stage sample units and then further sub-divided into second stage units out of which another sample is selected and similarly third and fourth stage sampling is performed in the same way. For example, in an urban survey first stage sampling will be of selection of towns and then for each selected towns households will be taken as sub- sample and depending upon the need, individuals will be selected as samples of third-stage sampling.

Advantages of Multi-Stage Sampling 

Advantages of multi-stage sampling are as follows: 

i) Flexible: 
It is very flexible method of sampling.

ii) Saves Time: 
It saves a lot of time, energy and cost as subsequent stages of samples are required only for units which are selected in the prior stages and limited in number.

iii) Administrative Efficiency: 
Comparatively, administering is easier than single stage designs due to the reason that frame of sampling is developed in partial units under multi-stage sampling

iv) Helps in Survey of Undeveloped Areas: 
For the surveys of undeveloped areas it is totally useful as there is no requirement of updated and accurate frame for sub-dividing the materials into rational small sample units.

v) Sampling of Large Units: 
Under multi-stage sampling large number of units can be sampled for a given cost due to consecutive clustering. which is not possible in most of the simple designs.

Disadvantages of Multi-Stage Sampling 

Multi-stage sampling has the following disadvantages: 

i) Large Number of Errors: 
It is subject to a larger amount of errors due to the process of dividing the strata or cluster in different stages into divisions and sub-divisions.

ii) Greater Variability: 
Them is a greater amount of variability, in the estimates than any other method of sampling.

iii) Less Efficient: 
Generally, It is less efficient than an appropriate single stage random sampling.

6) Area Sampling :

The area sampling is a form of multi-stage sampling in which maps instead of lists or registers is used as sampling frame'. It is commonly used in those countries which do not have proper sampling frame like a population list.

For geographic sub-divisions, 'clusters sampling' is the other name of 'area sampling'. The cluster of units based on geographical area is the primary sampling units known as 'cluster designs' which are famous as area sampling. The positive and negative features of cluster sampling are also applicable to area sampling. The total area for sampling is sub-divided into several smaller areas within which a random sample is selected.

For example, consider a city map, which is used for area sampling. City map has various blocks which are known as "frame" in case of area sampling and each frame has a unique number for its identification. For this sampling (which is based on the geographical considerations) block stratification is employed. Sample of dwellings is taken by identifying the blocks. At the end, each block is subdivided into segments (more or less equal size) and these 'segment samples' may be selected in the sample.

Advantages of Area Sampling

Advantages of area sampling are as follows: 

i) Convenience: 
Obtaining of the clusters is convenient and the cost of sampling reduces because the scope of study is limited only to clusters instead of whole population.

ii) Cost: 
Comparatively, the cost per element is reduced in area sampling than in stratified sampling because of reduced listing of the elements.

iii) Feasible: 
The area sampling proves feasible in many situations due to unavailability of the Sampling frames of the individual elements of the population and hence no other random sampling technique is possible to apply.

Disadvantages of Area Sampling 

Disadvantages of area sampling are as follows: 

i) Similar Elements: 
Statistically, area sampling may be less efficient in comparison to simple random sampling due to similarity in the elements of the area. Only in the extreme case when there is a similarity in the elements of the clusters, a sampling of a single unit from that area is much better than sampling the more units from that area.

ii) Costly: 
In comparison with simple random sampling, area sampling has more costs and
problems of statistical analysis.  

B) Non-Probability Sample (Non-Probability Sampling) :


Non-probability sampling is that type of sampling procedure which does not have any ground for estimating the probability that whether or not each item in the population has been included in the sample. There are different names of non-probability sampling such as deliberate sampling, purposive sampling and judgement sampling. In this type of sampling, the researcher deliberately selects items for the sample and the choice of researcher regarding the item is provided more weightage.

In other words, under non-probability sampling the organizer of the inquiry purposively chooses specific units of the universe to constitute a sample on the basis that the small portion selected by him, out of a huge one is typical or representative of the whole universe. 

Advantages of Non-Probably Sampling


i) True Universe Picture: 
Relevant sections of the universe may be selected in the proportions they appear in the universe.

ii) Economical: 
Geographical concentration can be achieved thus reducing costs. 

iii) Quick: 
Useful and quick method in certain circumstances. 

iv) Specific Cases Types: 
This can be the only technique available for some special cases such as illegal drug users.

v) Specific Members of Population: 
Helpful, if researchers are truly interested in specific members of a population, not the entire population.

vi) Pilot Study: 
Acts as an exploratory research like a pilot study, attempting to determine whether a problem exists or not.

Disadvantages of Non-Probability Sampling


i) Details Needed: 
Detailed initial information of the universe is needed.

ii) Errors: 
Errors in sample selection can easily occur.

iii) Subjective Nature: 
The subjectivity of non- probability sampling prevents making inferences to the entire population.

iv) Selection Blas: 
Validity and credibility are questionable due to selection bias.

v) Reliability: 
The reliability of the resulting estimates cannot be evaluated which results in the user not knowing how much confidence can be placed in any interpretations of the survey finding.

Types of Non-Probability Sampling


The various non- probability sampling designs are:

Types of Non-Probability Sampling

1) Convenience Sampling :

On the basis of convenience and approachability, the choice of the sampling units by the researcher, is known as 'convenience sampling'. Samples that are selected accidentally are called 'accidental samples'. Because of the selection procedure (units are selected from their actual place) it is called as 'sample of the man in the street'. Due to their accessibility, samples units are selected. For example, by adding the new product in the nearby suitable shops, the potential of the product is tested. This is accomplished by observing the purchasing and selling report of the product.

Advantages of Convenience Sampling

Advantages of convenience sampling are as following: 

i) Economical: 
It is less costly and less time- consuming.

ii) Proper Representation: 
Proper representation of the universe is ensured when the knowledge of the composition of the universe is adequate for investigation and it is free from bias.

iii) Avoid Irrelevant Items: 
It restricts the entry of unnecessary and irrelevant items into the sample per chance. 

iv) Intensive Study: 
Intensive study of the selected items is ensured. 

v) Accurate Results: 
Better results are obtained it the investigator has the capacity of keen observation and sound judgement as well as un-biasedness.

Disadvantages of Convenience Sampling

Disadvantages of convenience sampling are as follows: 

i) Personal Bias: 
The investigation plays and affects the selection due to enough scope of bias and prejudices.

ii) No Equal Chance: 
All the items of the universe to be included in the sample stand unequal chance. 

iii) No Degree of Accuracy: 
The degree of accuracy achieved is unknown in the investigation conducted by this method.

iv) No Possibility of Sample Error: 
Possibility of calculating sample error is negligible as it is based on mathematical concept and it is not applicable to non-random methods of sampling. 

v) Unsuitable for Large Samples: 
This method is not appropriate for the large samples where the size of the universe and the sample is significantly large.

2) Panel Sampling :

In panel sampling a group of participants are selected Initially by random sampling method and the same group is asked for the same information repeated number of times during that period of time. This sample is semi-permanent where members are included repeatedly for iterative studies. In this sampling, there is a facility of selecting and contacting samples that fit in getting high response rate, even by mail.

Advantages of Panel Sampling 

Advantages of panel sampling are as follows:

i) Saves Cost and Time: 
In the collection of information it takes lesser time and cost.

ii) Helps In Measuring Changes: 
Due to fixed sample units, the changes in repeated reporting can be measured.

iii) Helps in Trading Shift In Behavior: 
Additional shifts of behavior can be traced.

Disadvantages of Panel Sampling 

Disadvantages of panel sampling are as follows: 

i) Not Representative: 
Under panel sampling the sample is not fully representative.

ii) Members become Conditioned: 
The members of the panel may be trained for some specific situations.

iii) Difficult to Preserve Representative Character of Panel: 
Due to voluntary drop out of the professional members, it is required to replace of those members who are difficult to retain their representative character for a long time.

3) Snowball Sampling :

When the characteristic of the desired sample is limited then the special non-probability method is applicable. In this method, it is difficult to locate the respondents because it will be very costly. Depending on the referrals of the initial subjects snowball sampling generates additional subjects. Though this technique is biased and unable to represent a good cross-section from the population but dramatically it reduces the search cost.

Advantages of Snowball Sampling 

Following are the advantages of snowball sampling:

i) Identifying and Selecting Prospective Respondents: 
The reasonable method of identifying and selecting awaited respondents from small, hard-to-reach, uniquely defined lar populations is known as snowball sampling.

ii) Useful in Qualitative Research: 
For qualitative research practices such as focus Interviews, this non-probability sampling me is very useful.

iii) Needs Little Planning: 
In comparison to o sampling techniques, this sampling techniques requires little planning and fewer workforce.

iv) Less Costly: 
The primary advantage of this sampling method is the reduction of sample sizes and costs.

Disadvantages of Snowball Sampling

Snowball sampling has some disadvantages which are:

i) Biased: 
The overall research study involves biasness in snowball sampling. There are certain problems in this technique because of some important differences between the people who are either known or unknown to social circles.

ii) Limited Data Structure: 
Data structures are limited and unable to generalize the results to members of the target population being larger, defined like, all the other non-probability sampling techniques.

iii) Limited Control: 
The subject obtained by the researcher is based on the previously observed subjects due to researcher's little control over sampling methods.

iv) Researcher has no Idea of Distribution: 
There is no guarantee for the representativeness of the sample. The researcher is unaware of the true distribution of the population and sample.

4) Purposive Sampling :

A non-probability sample which follows certain norms is called purposive sampling. 

Types of Purposive Sampling

Purposive sampling is basically of two types: 
  • Judgement Sampling
  • Quota Sampling

a) Judgement Sampling :

The study which is based on the parameters of population, where the units are selected by a researcher or some other expert on his her judgement, is called judgement sampling'. This technique of sampling is appropriate in the situation where the study of the population is difficult to locate or there are members who are comparatively better than others for an interview in terms of knowledge or interest.

For example, a sample of grocery stores, considered to be "representative", is selected by a group of sales manager. Experimentally this was found to be a failure because there is no unbiased way of evaluating the precision of sample results. In spite of these limitations, when the size of the sample is extremely small this method is useful.

Advantages of Judgement Sampling 

Judgement sampling has the following advantages: 

i) Suitable for Small Sampling Units: 
When the universe includes small number of sampling units then various important elements could not be selected by simple random selection, whereas judgement selection definitely include these elements in the sample.

ii) Studying Unknown Traits of Population: 
F the study of some unknown characteristics of a population and for some population whose traits are known, the population is stratified on the basis of these known properties. Sampling units are selected on the basis of the judgement from each stratum. This method helps in obtaining a more representative sample.

iii) Solving Everyday Business Problems: 
To solve day-to-day problems of business, and in making of public policy decisions, executive and public officials who are short of time fail to wait for probability sample designs. Thus, in solving their urgent problems judgement sampling is the on practical method.

Disadvantages of Judgment Sampling 

Disadvantages of judgement sampling are as follows :

i) Non-Scientific: 
Due to personal bias and prejudice of the investigator the population units to be sampled are affected and it is concluded that the method is unscientific. The risk which is involved in the judgement sampling is that the investigator establishes predestined conclusion by including those items in the sample which adjust to his preconceived idea.

ii) No Method to Calculate Sampling Error: 
To evaluate the reliability of sample results there does not exist any impartial method. This method can only be successful due to quality of judgement. If the individual has knowledge about the population in the decisions making and has good judgement, then it leads to a representative sample, otherwise if the decision is based on the sample then it may be incorrect.

This should be taken into account that despite the judgement sample being a good representative, no method is impartial to determine the size and probability of sampling error.
 
b) Quota Sampling :

Quota sampling is the most commonly used non- probability sample designs, which is most comprehensively used in consumer surveys. Principle of stratification is also used by this sampling method. In stratified random sampling the researcher begins by building strata. The common bases for stratification in consumer surveys are demographic, eg, age, gender. income and so on. Compound stratification is generally wed, eg gender-wise age groups.

For each stratum sample sizes are fixed. Regarding stratified random sampling, the sampling within strata may be either proportional or disproportional. Interviews are conducted with the designated quotas by the field workers, with the identification of individual respondents being left to the field-workers.

Advantages of Quota Sampling 

Advantages of quote sampling are as follows: 

i) Economical: 
Travelling costs can be reduced by an interviewer because he does not require travelling all over a town for tracking pre- selected respondents. By applying numerous controls in a quota sample, it will be more expensive despite having less bias in selection.

ii) Administratively Convenient: 
Being administratively convenient, with the use of quota sampling, labour of selecting a random sample can be avoided. The problem of call- backs and non-contacts is abolished altogether.

iii) Minimum Memory Errors: 
For minimizing memory errors in the situation when field work is to be done quickly, most appropriate and feasible technique is quota sampling.

iv) Independent: 
It is not related with the existence of sampling frames. Due to unavailability of suitable sampling frames, the only choice
available is quota sampling.

Disadvantages of Quote Sampling 

Disadvantages of quota sampling are as follows: 

i) Difficulty in Calculating Standard Errors:
Random selection is not the criterion for quota sampling and hence it is impossible to calculate estimates of standard errors for the results of the sample.

ii) Difficulty in Obtaining Representative Sample: 
Depending on the mood and convenience of the interviewers the selection of the 'representative' sample is impossible to obtain within the quota.

iii) Hampers Quality of Work: 
The quality of work Ad suffers due to too much of independence of the incomplete interviewers.

iv) Difficult to Supervise and Control: 
Under quota sampling it is totally difficult to supervise and control field investigation.

Criteria for Selection of Sampling Technique


It is very thoughtful decision to select a sampling technique. i.e., probability or non-probability sampling. The advantage of determining and portion controlling sampling error in probability sampling does not make it a common sampling technique. Selection of a particular sampling technique is based on several factors like research purpose, target population, resources available, etc.

Some of the factors determining the selection of sampling technique (probability or non-probability be the sampling) are as follows:

1) High Frequency of Non-response: 
In case of high non-response frequency, it is not wise to use probability sampling. Therefore, non-probability sampling would be used in this case.

2) Inquiry: 
In case of research being conducted for an inquiry purpose only probability sampling is used, as it demands high level of accuracy and objectivity.

3) Sampling Error Estimation:
In order to estimate sampling errors only probability sampling is used, as non-probability sampling lacks randomness.

4) Presence of Constraints (Suitable Sample Frame, Time or Money): 
Sometimes in order to effectively collect information from the population, lot of resources are utilized causing constraints in terms of money, lime and suitable sample frame. In such cases, non-probability sampling is more suitable.