What is Sampling ?

Sampling is a process in which the fixed numbers of observations are taken randomly from a larger population. A technique which is fundamental for behavioral research is known as sampling and without using it, research work is not possible. It is impracticable and Impossible to study the whole population due to practical limitations of cost, time and other factors that are indispensable and operative in studying the whole population. For the sole reason of making the research findings economical and accurate, the concept of sampling has been introduced.

For example, for taking decision about to purchase or not to purchase the fruits, a fruit merchant inspects randomly only a few of them rather than examining each and every fruit. The important objective of sampling is to obtain maximum information of the population under study using minimum of money, labour and time.

Sampling in Research :

A definite plan to obtain a sample from the sampling frame is sample design. The method or technique which is adopted by the researcher in selecting the units of sampling from the population is called sampling design.

The basis for selecting a sample survey is the framework or road-map which is called sample design and it affects other important aspects of the survey. For obtaining some type of relevant information using survey. researchers execute it for some population, or universe. The sampling frame must be defined in such a way that it represents the population of interest out of which a sample is to be drawn at random. This sampling frame should be either identical to the population or may be a part of it. The sampling frame can be indirectly related to the population subject to some under coverage (e.g., the population is preschool children and the frame is a listing of preschools). The design of the sample may be simple or complex which provides the fundamental plan and methodology for the selection of the sample.

Definition of Sampling

According to Davis S. Fox :
"In the social science, it is not possible to collect data from every respondent relevant to our study but only from some fractional part is called sampling".

According to Cocharn :
"In every branch of science we lack the resources, to study more than a fragment of the phenomena that might advance our knowledge". In this definition a fragment is the sample and phenomena is the population.

Basic Concepts of Sampling

Following are the basic concepts of sampling in research methodology :

1) Universe/Population :
The entire group of units, being the focus of study, is represented by the population or universe consisting of all the persons in the country, in a particular geographical location, or a special domestic or economic group based on the purpose and scope of study. Non-human entities like farms, houses or business establishments also comprise population. 
For example, if a study is to be conducted on the marks obtained by the students of Statistics then the 'universe' will be all the students of that subject int that class. If the class is consisting of 50 students then the 50 students is the "universe".

"Population or universe" is the sum total of the objects under study, both animated and in animated. Thus, numerical details of the collection of the individuals or of their qualities, or of results of the operations are known as universe. Universe can be classified as follows : 

i) Finite Universe : 
A 'finite universe' comprises of finite number of entities or members. For example, the universe of the heights of students in a particular class.

ii) Infinite Universe : 
If the infinite numbers of members are included in n universe then it is called "infinite universe". For example, the universe of pressures at various points in the atmosphere.

2) Statistical Population : 
To draw out statistical inferences from the set of entities of statistical population is based on random sampling of the population. For example, to derive generalizations about the crows, to only the set of crows should be the interest of study. However, if one chooses a population such as 'all crows', then the limitations will be in observing crows both the existing as well as existing in the future. There will be additional limitations of geography where the resources are limited for studying crows.

The total of quantities that are measurable or set of numbers is 'statistical population'. Thus, when only a single character symbolized every element of such a set then it is defined as 'single population', e.g., income of individuals. Depending on the finite and infinite number of elements, statistical population can be infinite as well as finite. A random set may not be a statistical population. 
For example, a set of crows at a point of time will not represent a statistical population.

3) Sample :
A part of the population which is examined to estimate the characteristics of the population is called a sample.
For example,
  • A portion of rice is examined to assess the quality of rice in the bag where the portion of rice examined is the sample and the bag of rice is the population.
  • From a large consignment, proportion of defective articles is estimated only from a portion of the consignment. The selected portion is called sample.

4) Census :
When in a research every item or entity of the universe is included, that approach is called "census". In this method, detailed information regarding every population unit is collected. It is also called complete enumeration. This method provides more accuracy in the results in comparison to the sampling method. For example, population survey about various issues, such as, literacy, education, income, etc., include all the items of the population.

5) Sampling Frame :
The actual set of units and a list containing every member of the population from which a sample is drawn at random is called sample frame. 
For example, if one wants to know the factors that are responsible for the patients being admitted in the hospital after an acute attack of asthma in a specified area, then it becomes necessary to know the names of all those people of that particular area being admitted into the hospital for that reason.

A good sampling frame should be : 
  • Relevant : Research topic must be directly linked with relevant things.
  • Complete : Coverage of all relevant items. 
  • Precise : It should exclude all the irrelevant items.
  • Up-to-Date : Incorporating recent changes and additions, and deleting redundant items.

6) Sample Unit :
Every sample is made up of several members or components. These members or components are called "sampling units". A sampling unit may or may not be a sampling element. Generally sampling unit is referred as sampling element. But in case of sampling units consisting of several population units, each sampling unit is called a cluster of units. The individual population units which come under the cluster of units, are called sampling elements. Therefore in this case a sampling element is an individual object on which the research is done, and it is a component of sampling unit.
For example, in case of inspecting the quality of oranges, only few oranges are selected to examine and each selected orange is a sampling unit. But in case of finding, the literacy level of a particular colony, several houses are selected in that colony for research. These houses are sampling units. However the individual members of each house are approached for examining the literacy level, thus these individual members of several houses are sampling elements.

7) Sample Design :
Designing a plan for effectively drawing out a sample from the sampling frame is called 'sample design'. In this different tools or techniques are used by researchers for drawing sampling units from the sampling frame.

Characteristics of a Good Sample 

The various features of a good sample design are as follows :

Characteristics of a Good Sample

1) True Representative : 
The true representative of the population and matching its properties is termed as good sample where aggregate of certain properties is the population and sample is the sub-total of the universe.

2) Free from Bias : 
A good sample does not allows prejudices, pre-conceptions, and imaginations which affects its choice and it is unbiased.

3) Accurate : 
A sample is called good when it yields accurate estimates or statistics and free from errors.

4) Comprehensive :
A sample that is true representative of the population is also comprehensive in nature which is controlled by definite purpose of investigation. A sample's characteristic may be comprehensive but it may not be a good representative of the population.

5) Approachable : 
The subjects of good sample are easily accessible where the tools of research are easily conducted and easy collection of data possible.

6) Good Size :
The size of good sample is such that it yields an accurate result and the error due to probability can be estimated. 

7) Feasible : 
A good sample creates the research work more feasible.

8) Goal Orientation : 
Any sample which is selected by the researcher should be able to satisfy the objectives of the research. The sample should be taken in proper number. It should be customized to fit the environment under which the research is going to be conducted. If the sample is changed as per the requirement of the survey design then it can come out with better results and outcomes.

9) Practical : 
It means that the concepts of sample selection should be applied property while conducting the research. The researcher should be well experienced and well instructed. The instructions which are passed to observer should be clear, complete and correct in all terms so as to avoid errors and biasness on their part. The sample should be selected on basis of the sample design. The sampling units should be representative. The sample design should be practical and feasible in nature.

10) Economical : 
It refers that the research should not incur huge cost, time or efforts. One of the objectives of any research is to complete the research with minimum effort, time, money and resources. The researcher calculates per unit cost for each respondent. The researcher should choose that sampling design which gives minimum per respondent cost and maximum accuracy. The funding agency is highly concerned with per unit cost and the corresponding accuracy.

Procedure of Sampling

A brief description of which measurements will be taken at what times, on which material, in what manner, and by whom, is termed as 'sampling plan'. The designing of the sampling plans should incorporate that data to be evaluated which is a representative sample of the parameters of interest and be answerable to all the questions as stated in the goals. Following are the steps involved in sampling procedure in research or developing a sampling plan :

Sampling procedure

1) Define the Universe : 
The universe can be restricted to some geographical limits, a particular type of product or some other constraints. Defining the universe is the initial problem procedures of sampling. The collection of elements or objects possessing information desired by the researcher regarding what inferences are to be made is termed as the target population or the universe. Precise definition of the target population is must as ambiguous definition will result in ineffective research and deceptive in meaning. Proper defining of the target population requires translating the problem definition precisely as what should and should not be included in the sample. The description of the target population should be on the basis of elements, sampling units, the period and time. Thus, in survey research element is like an object from which or about information is required. In which survey research, the elements are usually the respondents. 
For example, consider a marketing research project in which researcher wants to take response of the consumer for a new brand of men's perfume. It has to decide that who should be included in the target population. It is the important question all the men included in the research or the men who used the perfume during the last month or men who are 17 years of age, or older. Should females be included, because some women buy colognes for their husbands?. Appropriate description of target population requires prior resolution of such types of questions.

2) Sample Frame 
The frame is enlisted by some population that already exists or it is constructed for some study to be conducted by the researcher. After the specification of the population to be studied, the frame of the population is developed. Sampling frame consists of all the sampling units of the population which is built by the researcher for the subject of his study or it consists of already existing list of the population. This frame does not consist of names only but it may include a permanent location, a boundary. an address, or the set of certain rules that describes a sampling unit.

A set of boundaries which is restricting the universe is termed as a frame that may be in the form of lists, indices, maps, directories, population records, electoral rolls, city tax rolls, enrollment of the students in an university, etc. This frame is very mandatory in marketing studies and every element of the population enlisted appears only once which constitutes a sample frame. For a good sampling frame it should be accurate, free from duplication, and conveniently available. For the better performance of sampling procedure, sampling should be essential for marketing research. The representation of the elements of the target populations sampling frame which consists of a list to identify the target population. 
For example, firms in industry that are enlisted in association directory, telephone book, a mailing list purchased from a commercial organization, a city directory, or a map.

3) Specifying the Sampling Units : 
A sampling unit is decided on the basis of the sampling frame which is the primary unit containing the sampling elements of the population like, city blocks, households, a business organization, etc. The design of the project is the partial basis of the selection of the sampling unit. The units that are served as initial sampling are called as primary sampling units. The objective of the inquiry is the basis of the composition of one or more units of the population.
For example, for the assessment of the consumer response to a new line of lipsticks, Lakme wants to sample females who are over 18 years of age where it can be the possibility of sampling such females directly and the sampling unit would be the same as an element. Otherwise, households might be the sampling units which are sampled. All the females above 18 in the selected households would be interviewed where the sampling unit differs from the population element. The time period under consideration is the extent of the geographical boundaries and the time factor.

4) Selection of Sample Design : 
The procedure of selecting units in the sample involves two primary methods of sampling namely, probability and non-probability methods that can be further sub divided into specific methods of selection. The probability sample being one and the procedure involved in selecting the units has some specific chance of its inclusion in the sample. An arbitrary method is adopted in selecting the units which is not depending upon the chance for the non probability sample. The purpose of inquiry and the attitude or convenience of the investigators is the basis of this method.

Two decisions are involved in the selection of the sample design:
  • To use probability or non-probability method of selection.
  • Specific sample design to use in collecting the data.

The following considerations affect the choice of the researcher :
  • Usage of the probability sampling for the evaluation of the sampling error.
  • Probability sample should be used for ensuring randomness in the selection of the units.
  • Non-probability sampling should be used in the absence of proper sample frame.
  • Non-probability sampling should be used when the considerations of time and money are important.
To achieve the objectives of investigation in the best possible way sample design should be selected after the decision of the method of selection whether probability or non-probability method. Unconcerned about the design being chosen finally the researcher has to defend his design at the time of the final presentation of the study results.

5) Determination of Sample Size : 
The desired degree of accuracy in investigation is directly proportional to the size the sample which also depends upon the nature of the population and the method of selection. The ideal samples size in marketing research investigations depends upon the the type of series and the population size. There will be more degree of heterogeneity and larger units s will be drawn in the sample due to larger size of the population. Hence the size of the sample should be larger for being fully representative.

6) Select the Sample : 
Execution of the actual sampling process is called as selection of the sample and the real selection of the elements of sample requiring considerable amount of office and field work especially when the personal interviews are involved. Detailed specification of the sampling design is the require is requirement for the execution of the sampling process with respect to the implementation of the size of population, sampling frame, sampling unit, sampling technique and sample size. Sampling units and sample size. being the household's requirement is the operational definition of a household, procedure for those houses that are vacant and if nobody is at home then the execution of the call backs is mandatory. For all the sampling design, decisions information must be provided in detail.

Advantages of Sampling

Following are the importance of sampling in research : 

1) Saves Time, Money and Effort : 
The subjects involved in sampling are small in number which gives him less time to calculate, tabulate, present, analyse, and interpret and hence the researcher saves time, money and effort.

2) More Effective : 
Due to the size of the sample being small than that of the population, the tiredness is reduced in collecting the information and investigator works more effectively.

3) Faster and Cheaper : 
Due to small sample size the data collection, tabulation, presentation, analysis, and interpretation takes less time and it involves nominal expenses.

4) More Accurate : 
Small errors are in sampling because small data are involved in collection, tabulation, presentation, analysis and interpretation.

5) Gives More Comprehensive Information :
Thorough investigation of the study is the result of small sample which provides more complete information as all the members of the population have equal chance to be included in the sample.

Disadvantages of Sampling 

Following are the disadvantages of sampling : 

1) Biased Selection : 
Biased selection of the respondents may be included in sampling by the research worker.

2) Difficulty in Selection : 
It is very difficult to select a truly representative sample because a large number of factors obstructing the method of selecting good samples.

3) Specialized Knowledge Needed : 
Specialized knowledge is required in the sampling method where the investigator may commit serious mistakes due to its absence.

4) Problem of Cooperation : 
Due to scattered sample the subjects are uncooperative with the researcher.

5) Less Accuracy :
When the higher standard of accuracy is expected then the sampling is not suitable.

6) Limited Nature : 
Due to small or dissimilar universe it is impossible to derive a representative sample where census study is the best possible substitute.

Sample Size

The sample size for a research refers to the total number of elements of the population to be included in the sample for conducting the research study. Both qualitative and quantitative points are involved in specifying the size of the sample for the research study. The accuracy of research depends on the size of the sample. It has been observed that a larger sample gives more accuracy and estimate levels. Apart from it, the availability of money, resources and efforts also specify the size of a sample.

The sample size of any research study is represented by 'n. If the population is highly heterogeneous, then the sample will be divided into various groups with an indication that the sample size would be larger. If the population is heterogeneous then the sample will be small. Thus, at pre-assigned level there is a fixation of the sampling error and the standard error of the statistic, thus having sample size as 'n' is chosen.

Factors Influencing Sample Size/ Sample Size Constraints 

The following points should be taken care of while deciding the sample size :

Sample Size Constraints

1) Size of Universe : 
It has been observed and statistically proven that the sample size should be large so that it can represent the whole target population If the universe size is large and heterogeneous then the researcher should take large sample size and vice versa.

2) Availability of Resources : 
The researcher needs a lot of resources to complete the task of research. These resources bind the researcher. It means that the researcher has to complete the research within those allotted resources. Resources can be money, time, experts or any other variable. If the resources are easily available than the sample size can be large otherwise smaller sample would be appropriate.

3) Level of Accuracy Required : 
For any research, the level of the accuracy affects sample size as bigger sample represents the whole population properly than a smaller sample. However, it is not always mandatory that a large sample will always give more accurate results. The selection of the sampling technique and research tools also impact the accuracy level of the research.

4) Homogeneity or Heterogeneity of the Universe : 
Homogeneous universe is that universe in which all the elements are similar, on the other hand heterogeneous universe is that universe where the elements are different from each other. A A homogeneous universe requires a smaller sample size, while researcher needs to take bigger sample size in case of heterogeneous universe because a small sample may not represent the whole population.

5) Nature of Study : 
The researcher may choose a small sample, if the research is intensive and fundamental (continuous) in nature. But in those cases, where the research is extensive and applied (researches which are not going to be repeated soon), then the researcher is bound to select larger sample size. So, it is quite clear that the nature of study also affects the sample size.

6) Selection of Sampling Technique : 
The sample size is also impacted by the kind of sampling technique selected by the researches for conducting the research. For example, when a researcher adopts the simple random sampling technique then he main choose large sample size, but on the other hand in stratified sampling technique the researcher needs a comparatively smaller sample size for accurate and effective results.

7) Attitude of Respondents : 
The researcher is an experienced individual, so he will always chose a larger sample size in which he thinks that the non-response rate will be higher. In simple words, if there are chances that the respondents will not reply and return the questionnaire, then large sample size is chosen.

8) Degree of the Variability : 
Degree of variability specifies the extent to which the scores in any distribution are spread in the population. Larger numbers always indicates greater variability of scores in a population. Sometimes dispersion is substituted for variability, and this term is widely used in statistics. If the population is heterogeneous, then the researcher uses larger sample size so as to receive a specified precision level. On the other hand, if the population is more homogeneous then the smaller sample size is selected by the researcher.