Sampling
A survey may be conducted by either of two methods
1. Census Method or Parametric method and
2. Sampling method or Non-parametric method.
1. Census method:
It deals with the investigation of the entire population. Here the data
are collected for each and every unit of the universe. This method
provides more accurate and exact information as no unit is left out.
2. Sampling method:
Here a small group is selected as representative of the whole universe.
It works with the objective to obtain accurate and reliable information
about the universe with minimum of cost, time and energy and to set
out the limits of accuracy of such estimates. It makes exhaustive and
intensive study possible with much less time, money and material. Its
more popular in research work.
Population:
Population or universe means, the entire mass of observations, which is
the parent group from which a sample is to be formed. The term
population or universe conveys a different meaning than a traditional
one. In census survey, the count of individuals (men, women and
children) is known as population.
But in Research Methodology population means characteristics of a
specific group. For example secondary school teachers of, who have
some specific features like teaching experience, teaching attitudes etc.
Sampling means selecting a given number of subjects from a
defined population as representative of that population.
One type of population distinguished by educational
researchers is called the target population.
By target population distinguished by educational researchers is
called the target population.
By target population, also called universe, we mean all the
members of a real or hypothetical set of people , events or objects to
which we wish to generalize the results of our research.
The first step in sampling is to define the target population
Research work is guided by inductive thinking. The researcher proceeds from specificity to generality. The sample observation is the specific situation, which is applied to the population, it is the general situation. The measures of a sample are known as statistics and measures of a population are termed as parameter. Mean, S.D., coefficient of Correlation of sample observation known as Statistics and Mean, S.D., coefficient of correlation of population are known as parameters. Generally parameters are estimated on the basis of sample statistics. Sampling is indispensable technique in behavioral research and not so common in physical sciences. It is fundamental to all statistical methodology of behavioral and social research. It makes research findings economical and accurate. Sampling means selection of individuals from the population in such a way that every individual has equal chance to be taken into the sample. Term sample should be reserved for a set of units or portion of an aggregate of material which has been selected in the belief that it will be representative of the whole aggregate. By Frank Yates “Sample is set of units of an aggregate.”
Assumptions of Sampling
1. Homogeneity amidst complexity:
Social phenomenon is very complex in nature and every unit appears to
be different from another. But at the same time they also possess
similarities in many respects. It is, therefore, assumed that there is the
possibility of such representative types in the whole population that
makes sampling possible.
2. Possibility of Representative Selection:
Sampling has its origin in the mathematical theory of probability and
law of statistical regularity. The law of statistical regularity lays down
that a group of objects chosen at random from a large group tend to
possess the characteristics of that large group (universe) by L. R. Conner.
3. Absolute accuracy not essential but relative or significant accuracy
i.e. needed in case of large scale observations. Because it is practically
impossible to achieve because of errors in measurement, collection of
data , its analysis, interpretation.
Definitions
“A statistical sample is a miniature picture or cross –section of the entire group or aggregate from which the sample is taken.” - P. Y. Young
A sample is a small proportion of a population selected for observation
and analysis .It is a collection consisting of a part or sub-set of the
objects or individuals of population which is selected for the express
purpose of representing the population.
By observing the characteristics of the sample, one can make
certain inferences about characteristics of the population from which it
is drawn.
Sampling, "It is the process of selecting a sample from the
population. For this purpose, the population is divided into a number of
parts called sampling units."
Sampling designs means the joint procedure of selection and
estimation. Sampling is a part of the strategy of research.
Sampling should be such that the error of estimation is minimum.
Good and Hatt, “A sample as the name implies, is a smaller
representation of a larger whole.”
W. G. Cocharn, “In every branch of science we lack the
resources, to study more than a fragment of the phenomenon that
might advance our knowledge.” i.e. fragment is sample and
phenomenon is population. The sample observations are applied to the
phenomenon i.e. generalization.
David S. Fox, “In the social sciences, it is not possible to collect
data from every respondent relevant to our study but only from some
fractional part of the respondents. The process of selecting the
fractional part is called sampling.”
Need of Sampling
1. Economy of time.
2. Economy of money.
3. True detailed knowledge.
4. Utility in experimental study.
5. It has reliability because it is based on probability theory.
Advantages of Sampling
1. It has a greater adaptability.
2. It is an economical technique.
3. It has high speed for generalization.
4. According to W.G. Cocharan, “It has greater precision and
accuracy in the observation”.
5. This technique has great accuracy.
6. It has a greater speed in conducting a research work.
7. It has a greater scope in the field of research.
8. It reduces the cost of observation or data collection.
Disadvatages of Sampling
1. Scope of biasness.(Less accuracy)
2. Problem of representative sample-Difficulty in selecting a truly
representative sample.
3. Need of eligible researchers.
4. Instability of sample subjects or changeability of units i.e. in
heterogeneous population.
5. There are certain situations where sampling is possible.
Essentials of an Ideal Sample
Homogeneity: The units included in sample must be as likeness with
other units.
Adequacy:Independence: Every unit should be free to be included in the
sample.
Representativeness; An ideal sample must be such that it
represents the whole data adequately.
In the number of units included in a sample should be sufficient to
enable derivation of conclusions applicable to the whole data.
Economical in terms of time and money.
High level of reliability.
Characteristics of a Good Sample
1. A good sample is the true representative of the population
corresponding to its properties.
2. The population is known as aggregate of certain properties and
sample is called sub-aggregate of the universe.
3. A good sample is free from bias; the sample does not permit
prejudices, the learning and pre-conception, imaginations of the
investigator to influence its choice.
4. A good sample is an objective one; it refers objectivity in selecting
procedure or absence of subjective elements from the situation.
5. A good sample maintains accuracy .It yields an accurate estimates
or statistics and does not involve errors.
6. A good sample is comprehensive in nature. This feature of a closely
linked with true-representativeness. Comprehensiveness is a quality
of a sample which is controlled by specific purpose of the
investigation. A sample may be comprehensive in traits but may not
be a good representative of the population.
7. A good sample has the practicability for research.
Types of Sampling Design / Methods of Sampling
Probability Sampling
Non-Probability Sampling
Probability Sampling
(1) Simple Random Sampling :
It is one in which each element of the population has an equal and
independent chance of being included in the sample i.e. a sample
selected by randomization method is known as simple random sample
and this technique is simple randomizing.
Randomization is done by using the following techniques:
(a) Tossing a coin
(b) Throwing a dice
(c) Lottery method
(e) Blind folded method
(f) Tippett’s table method
Merits of Randomization:
1. It requires the minimum knowledge of population.
2. It is free from subjectivity and free from personal error.
3. It provides appropriate data for one’s purpose.
4. The observations of the sample can be used for inferential
purpose.
Demerits of Randomization:
1. It cannot ensure the representativeness of a sample.
2. It does not use the knowledge about the population.
3. Its inferential accuracy depends upon the size of the sample.
(2) Systematic Sampling :
Systematic sampling is an improvement over the simple random
sampling. This method requires the complete information about the
population. There should be a list of information of all the individuals of
the population in any systematic way.
Now we decide the size of the sample:
Let the size of sample is = n and population size is = N
Now we select each N/n individual from the list and thus we have the
desired size of sample which is known as systematic sample. Thus for
this technique of sampling population should be arranged in any
systematic way.
Merits:
1. This is a simple method of selecting a sample.
2. It reduces the field cost.
3. Inferential statistics may be used.
4. Sample may be comprehensive and representative of population.
5. Observations of the sample may be used for drawing conclusions
and generalizations.
Demerits:
1. This is not free from error, since there is subjectivity due to
different ways of systematic list by different individuals.
2. Knowledge of population is essential.
3. Information of each individual is essential.
4. This method can’t ensure the representativeness.
5. There is a risk in drawing conclusions from the observations of
the sample.
(3) Stratified Sampling :
It is an improvement over the earlier methods. When we employ this
technique, the researcher divides his population into strata on the basis
of some characteristics and from each of these smaller homogenous
groups (strata) draws at random a predetermined number of units.
Researcher should choose that characteristic as criterion which seems
to be more relevant in his research work.
Stratified sampling may be of three types;
(a) Disproportionate:
Means that the size of the sample in each unit is not proportionate to
the size of the unit but depends upon considerations involving personal
judgement and convenience. This method of sampling is more effective
for comparing strata which have different error possibilities. It is less
efficient for determining population characteristics.
(b) Proportionate:
It refers to the selection from each sampling unit of a sample that is
proportionate to the size of the unit. Advantages of this procedure
includes representativeness with respect to variables used as the basis
of classifying categories and increased chances of being able to make
comparisons between strata. Lack of information on proportion of the
population in each category and faulty classification may be listed as
disadvantages of this method.
(c) Optimum allocation:
Stratified sampling is representative as well as comprehensive than
other stratified samples. It refers to selecting units from each stratum.
Each stratum should be in proportion to the corresponding stratum the population. Thus sample obtained is known as optimum allocation
sample.
Merits:
(i) It is a good representative of the population.
(ii) It is an improvement over the earlier technique of sampling.
(iii) It is an objective method of sampling.
(iv) Observations can be used for inferential purpose.
Demerits:
(i) Serious disadvantage of this method is that it is difficult for the
researcher to decide the relevant criterion for stratification.
(ii) Only one criterion can be used for stratification, but generally it
seems more than one criterion relevant for stratification.
(iii) It is costly and time consuming method.
(iv) Selected samples may be representative with reference to the
used criterion but not for the other.
(v) There is a risk of generalization.
(4) Multiple or Double Repetitive Sampling :
Generally this is not a new method but only a new application of the
samplings. This is most frequently used for establishing the reliability of
a sample. When employing a mailed questionnaire, double sampling is
sometimes used to obtain a more representative sample. This is done
because some randomly selected subjects who are sent questionnaires
may not return them.
Obviously, the missing data will bias the result of the study, if
the people who fail to reply the query differ in some fundamental way
from the others in respect to the phenomenon being studied.
To eliminate this bias, a selected sample may be drawn at
random from the non-respondents and the people interviewed to
obtain the desired information. Thus this technique is also known as
repeated or multiple sampling.
This double sampling technique enables one to check on the reliability of the information obtained from first sample. Thus, double
sampling, where in one sample is analyzed and information obtained is
used to draw the next sample to examine the problem further.
Merits:
(i) Thus sampling procedure leads to the inferences of free
determine precision based on a number of observations.
(ii) This technique of sampling reduces the error.
(iii) This method maintains the procedure of the finding evaluate
the reliability of the sample.
Demerits:
(i) This technique of sampling cannot be used for a large sample . It
is applicable only for small sample.
(ii) This technique is time consuming and costly.
(iii) Its planning and administration is more complicated.
(5) Multistage Sampling :
This sample is more comprehensive and representative of the
population. In this type of sampling primary sample units are inclusive
groups and secondary units are sub-groups within these ultimate units
to be selected which belong to one and only one group.
Stages of a population are usually available within a group or
population, whenever stratification is done by the researcher. The
individuals are selected from different stages for constituting the multi
stage sampling.
Merits:
(i) It is a good representative of the population.
(ii) Multistage sampling is an improvement over the earlier
methods.
(iii) It is an objective procedure of sampling.
(iv) The observations from multi stage sample may be used for
inferential purpose.
Demerits:
(i) It is a difficult and complex method of sampling.
(ii) It involves errors when we consider the primary stages.
(iii) It is again a subjective technique of sampling.
(6) Cluster Sampling :
To select the intact group as a whole is known as a cluster sampling. In
cluster sampling the sample units contain groups of element (cluster)
instead of individual members or items in the population. Rather than
listing all elementary school children in a given city and randomly
selecting 15 % of these students for the sample, a researcher lists all of
the elementary schools in the city, selects at random 15 % of these
clusters of units, and uses all of the children in the selected schools as
the sample.
Merits:
(i) It may be a good representative of the population.
(ii) It is an easy method.
(iii) It is an economical method.
(iv) It is practicable and highly applicable in education.
(v) Observations can be used for inferential purpose.
Demerits:
(i) Cluster sampling is not free from errors.
(ii) It is not comprehensive.
Non-Probability Sampling
Samples which are selected through non-random methods are called non probability samples. Depending upon the technique used it may be;
(1) Incidental or Accidental Sampling :
The term incidental or accidental applied to those samples that are
taken because they are most frequently available i.e. this refers to the
groups which are used as samples of a population because they are
readily available or because the researcher is unable to employ more
acceptable sampling methods.
Merits:
(i) It is very easy method of sampling.
(ii) It is frequently used method in behavioural sciences.
(iii) It reduces the time, money and energy i.e. it is an economical
method.
Demerits:
(i) It is not representative of the population.
(ii) It is not free from errors.
(iii) Parametric statistics cannot be used.
(2) Judgemental Sampling :
This involves the selection of a group from the population on the basis
of available information assuming as if they are representative of the
entire population. Here group may also be selected on the basis of
intuition or on the basis of the criterion deemed to be self-evident.
Generally investigator should take the judgment sample so this
sampling is highly risky.
Merits:
(i) Knowledge of investigator can be best used in this technique of
sampling.
(ii) This method of sampling is economical.
Demerits:
(i) This technique is objective.
(ii) It is not free from errors.
(iii) It includes uncontrolled variation.
(iv) Inferential statistics cannot be used for the observation of this
sampling, so generalization is not possible.
(3) Purposive Sampling :
The purposive sampling is selected by some arbitrary method because
it is known to be representative of the total population, or it is known
that it will produce well matched groups. The idea is to pick out the
sample in relation to criterion which are considered important for the
particular study. This method is appropriate when the study places
special emphasis upon the control of certain specific variables.
Merits:
(i) Use the best available knowledge concerning the sample
subjects.
(ii) Better control of significant variables.
(iii) Sample groups data can be easily matched.
(iv) Homogeneity of subjects used in the sample.
Demerits:
(i) Reliability of the criterion is questionable.
(ii) Knowledge of population is essential.
(iii) Errors in classifying sampling subjects.
(iv) Inability to utilize the inferential parametric statistics.
(v) Inability to make generalization concerning total population.
(4) Quota Sampling :
This combines both judgment sampling and probability sampling: on the
basis of judgment or assumption or the previous knowledge, the
proportion of population falling into each category is decided.
Thereafter a quota of cases to be drawn is fixed and the observer is
allowed to sample as he likes. Quota sampling is very arbitrary and likely
to figure in municipal surveys.
Merits:
(i) It is an improvement over the judgment sampling.
(ii) It is an easy sampling technique.
(iii) It is not frequently used in social surveys.
Demerits:
(i) It is not a representative sample.
(ii) It is not free from errors.
(iii) It has the influence of regional , geographical and social factors.
(5) Snowball Sampling :
The term; snow ball sampling’ has been used to describe a sampling
procedure in which the sample goes on becoming bigger and bigger as
the observation or study proceeds. The term snowball stems from the
analogy of a snowball sample which would allow computation of
estimates of sampling error and use of statistical test of significance.
For example, an opinion survey is to be conducted on smokers
of a particular brand of cigarette. At the first stage, we may pick up a
few people who are known to us or can be identified to be the smokers
of that brand. At the time of interviewing them, we may obtain the
names of other persons known to the first stage subjects. Thus the
subjects go on serving an informant for the identification of more
subjects and the sample goes on increasing.
Merits:
Snowball sampling which is generally considered to be nonprobabilistic can be converted into probabilistic by selecting subjects
randomly within each stage.
Demerits:
Sampling errors may creep in.
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